Turing Fellows

The Alan Turing Institute and its five partner universities have announced a list of inaugural Turing Fellows.

Below is the full list with clickable links that will direct you to each Fellow’s university home page.

Faculty directors



Cambridge

Professor Jon Crowcroft

Cambridge

Bio

Jon Crowcroft has been the Marconi Professor of Communications Systems in the Computer Laboratory since October 2001. He has worked in the area of Internet support for multimedia communications for over 30 years. Three main topics of interest have been scalable multicast routing, practical approaches to traffic management, and the design of deployable end-to-end protocols. Current active research areas are Opportunistic Communications, Social Networks, and techniques and algorithms to scale infrastructure-free mobile systems. He leans towards a “build and learn” paradigm for research.

He graduated in Physics from Trinity College, University of Cambridge in 1979, gained an MSc in Computing in 1981 and PhD in 1993, both from UCL. He is a Fellow the Royal Society, a Fellow of the ACM, a Fellow of the British Computer Society, a Fellow of the IET and the Royal Academy of Engineering and a Fellow of the IEEE.

He likes teaching, and has published a few books based on learning materials.

Research

Computing Systems at scale are the basis for much of the excitement over Data Science, but there are many challenges to continue to address ever larger amounts of data, but also to provide tools and techniques implemented in robust software, that are usable by statisticians and machine learning experts without themselves having to become experts in cloud computing. This vision of distributed computing only really works for “embarrassingly parallel” scenarios. The challenge for the research community is to build systems to support more complex models and algorithms that do not so easily partition into independent chunks; and to give answers in near real-time on a given size data centre efficiently.

Users want to integrate different tools (for example, R on Spark); don’t want to have to program for fault tolerance, yet as their tasks & data grow, will have to manage this; meanwhile, data science workloads don’t resemble traditional computer science batch or single-user interactive models. These systems put novel requirements on data centre networking operating systems, storage systems, databases, and programming languages and runtimes. As a communications systems researcher for 30 years, I am also interested in specific areas that involve networks, whether as technologies (the Internet, Transportation etc), or as observed phenomena (Social Media), or in abstract (graphs).

Dr Leonardo Bottolo

Cambridge

Bio

Dr Leonardo Bottolo is Reader in Statistics for Biomedicine at the University of Cambridge. He received his PhD in Methodological Statistics from the University of Trento, Italy, in 2001. Before joining the University of Cambridge, he was appointed Senior Lecturer in Statistics in the Department of Mathematics, Imperial College. He worked as postdoc in the Mathematical Genetics group, University of Oxford and at the Institute of Mathematical Sciences, Imperial College.

Research

Dr Leonardo Bottolo has considerable experience of methodological and algorithmic aspects of data integration and feature selection in genetics and system biology for high-dimensional data. In continuity with his experience in population genetics gained in Oxford he implemented new multivariate approaches for the detection of systemic associations between different types of -omics data. Currently he is further developing these statistical tools with novel MCMC samplers to make them fully operational on current large genetics data and on future high-throughput Next Generation Sequencing. The implementation of these algorithms benefits from recent advances in parallel computing of massive data sets.

Dr Quentin Berthet

Cambridge

BIO

Quentin Berthet is a Lecturer in the Statslab, in the DPMMS at Cambridge, and a fellow of St John’s College, since 2015. He is a former student of the Ecole Polytechnique, received a Ph.D. from Princeton University in 2014, and was a CMI postdoctoral fellow at Caltech.

RESEARCH

Dr Berthet’s research is focused on the theoretical understanding of statistical procedures. He studies fundamental problems related to modern datasets such as computational constraints, heterogeneity of the data or presence of errors. He is interested in taking into account societal concerns in the analysis of data, by considering ethical aspects (privacy, security of data), as well as ways to guide decision making in a data-driven way. He uses tools from Statistics and Probability Theory, as well as from Optimisation, Information Theory and Computer Science.

Professor Carl Rasmussen

Cambridge

BIO

Professor Carl Rasmussen is a lecturer in the Machine Learning Group of the Computational and Biological Learning Lab in the Division of Information Engineering at the Department of Engineering in Cambridge.

 

RESEARCH

Carl has very broad interests in probabilistic inference in machine learning, covering both unsupervised, supervised and reinforcement learning. He is particularly interested in design and evaluation of non-parametric methods such as Gaussian processes and Dirichlet processes. Exact inference in these models is often intractable, so we need to resort to approximation methods, such as variational techniques or Markov chain Monte Carlo.

 

 

Professor Richard Samworth

Cambridge

Bio

Richard Samworth is Professor of Statistics in the Statistical Laboratory at the University of Cambridge and a Fellow of St John’s College.  He received his PhD, also from the University of Cambridge, in 2004, and currently holds an EPSRC Early Career Fellowship.

Research

His main research interests are in developing methodology and theory for high-dimensional and nonparametric statistical inference.  He is currently particularly interested in techniques for handling statistical challenges in Big Data that rely on perturbations of the data and aggregation.  Examples include random projection ensembles for high-dimensional classification and subsampling for variable selection. Other interests include shape-constrained and other nonparametric function estimation problems, Independent Component Analysis, and the bootstrap and its variants (e.g. bagging).

Dr Rajen Dinesh Shah

Cambridge

Bio

Dr Rajen Dinesh Shah is a Lecturer in Statistics at the Statistical Laboratory, University of Cambridge, where he previously completed my PhD at Trinity College under the supervision of Professor Richard Samworth. He is a member of the Research Section committee of the Royal Statistical Society.

Research

Dr Shah is broadly interested in the analysis of large-scale data and my work falls under the areas of high-dimensional statistics, machine learning and computational statistics. He primarily works on developing algorithms for learning from large-scale data, and understanding their theoretical properties. He is particularly interested in creating methods that are able to perform well under constraints on computation and memory. Recently he has worked on variable selection and uncertainty quantification in high-dimensional settings; hashing methods for compressing large-scale data; and randomised algorithms for detecting interactions.

Dr Sumeetpal Singh

Cambridge

BIO

Sumeetpal S. Singh received the B.E. (with first-class honours) and Ph.D. degrees from the Dept. of Electrical Engineering, University of Melbourne, Australia, in

1997 and 2002, respectively. After having worked in Industry for a number of years, he joined the Cambridge University Engineering Department in 2004 as a Research Associate and is currently a University Senior Lecturer in Engineering Statistics. He is also a Fellow and Director of Studies at Churchill College, an Affiliated Lecturer of the Statistics Laboratory and an Associate Editor of Statistics and Computing. His research focus is on statistical signal processing, in particular, using Monte Carlo methods, covering algorithmic development for applications and theoretical analysis.  He has been recognised for his work on Multi-target Tracking (awarded the IEEE M. Barry Carlton Award in 2013.)

 

RESEARCH

Broadly, his research develops core statistical methodology for Data Science with the view of using cloud computing techniques. Analysing heterogeneous data sets [which is necessary for data-driven decision making] is a major hurdle in Data Science [e.g. Ecology, Epidemiology] as the mathematical models that describe the data are increasingly more intricate and very high dimensional in most cases. Fortunately, statistical inference is still feasible with Monte Carlo methods. His research is inherently cross-disciplinary as he draw on tools from Statistics, Engineering, Computer Science and Probability Theory to design and analyse new computational procedures.

Dr Richard Gibbens

Cambridge

Bio

Richard Gibbens is a mathematician and computer scientist with research interests in the mathematical modelling of networks especially communication, road transport and energy networks. He is a Reader at the Computer Laboratory, University of Cambridge. Prior to 2001 he was a Royal Society University Research Fellow in the Statistical Laboratory, University of Cambridge where he also obtained his PhD.

Research

During the Alan Turing Institute Faculty Fellowship he will be exploring the interface between mathematics, statistics and computer science. This will involve theoretical modelling reinforced with working with data drawn from, for example, road transport networks and energy networks. He is interested in working on the boundaries of current methodology and discovering how recent advances in computer technology such as multi-core and cluster computing can be best utilised. He is also interested in collaborating widely with researchers and policy makers to ensure that data and modelling are both used and valued.

Professor Peter Markowich

Cambridge

BIO

Professor of Applied Mathematics, Department of Applied Mathematics and Theoretical Physics, University of Cambridge and Professor of Applied Analysis, Faculty of Mathematics, University of Vienna.

RESEARCH

Professor Peter Markowich’s research is in Applied Mathematics, in particular in the partial differential equations (PDE) which arise in applications. Mine is an ‘integrated’ approach, involving mathematical analysis, numerical analysis, computational mathematics and mathematical modelling. He has spent a significant part of my career working on PDE in physics but about ten years ago he switched his research emphasis to PDE applications in the life, social and data sciences, emphasising both direct and inverse (data assimilation) problems. This change of research direction shall support and help to shape the current trend in modern mathematics of penetrating and becoming indispensable tools also for non-physical data-rich applied sciences. He has been working on recently: inpainting in mathematical imaging, price formation of economical assets, human crowd motion modelling by fluid and mean-field game approaches, large data assimilation in the Navier-Stokes fluid equations, and network formation and adaptation in the biological and social sciences.

Dr Adrian Weller

Cambridge

BIO

Adrian Weller is a senior researcher in the Machine Learning Group at the University of Cambridge, advised by Prof Zoubin Ghahramani. He received a PhD in computer science (machine learning) at Columbia University, advised by Prof Tony Jebara. He is an active angel investor and advisor, and previously held senior roles in investing and trading at Goldman Sachs, Salomon Brothers and Citadel.

 

RESEARCH

Most of his academic research relates to graphical models but he is also very interested in other areas including: finance, anything on intelligence (natural or artificial), deep learning, reinforcement learning, evolution, Bayesian methods, time series analysis, ethics, music and methods for big data.

Professor Carola-Bibiane Schönlieb

Cambridge

Bio

Carola-Bibiane Schönlieb is a Reader in Applied and Computational Analysis at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge since 2015. There, she is head of the Cambridge Image Analysis group, Director of the Cantab Capital Institute for Mathematics of Information, Co-Director of the EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, and since 2011 a fellow of Jesus College Cambridge. Her current research interests focus on variational methods and partial differential equations for image analysis, image processing and inverse imaging problems. Her research has been acknowledged by scientific prizes, among them the LMS Whitehead Prize 2016, and by invitations to give plenary lectures at several renowned applied mathematics conference, among them the SIAM conference on Imaging Science in 2014, the SIAM conference on Partial Differential Equations in 2015, the IMA Conference on Challenges of Big Data in 2016 and the SIAM annual meeting in 2017.
Carola graduated from the Institute for Mathematics, University of Salzburg (Austria) in 2004. From 2004 to 2005 she held a teaching position in Salzburg. She received her PhD degree from the University of Cambridge in 2009. After one year of postdoctoral activity at the University of Göttingen (Germany), she became a Lecturer in at DAMTP in 2010, promoted to Reader in 2015.

Research

She is interested in the interaction of mathematical sciences and imaging. She studies non-smooth and possibly non-convex variational methods and nonlinear partial differential equations for image analysis and inverse imaging problems, among them image reconstruction and restoration, object segmentation, and dynamic image reconstruction and analysis such as fast flow imaging, object tracking and motion analysis in videos. Moreover, she works on computational methods for large-scale and high-dimensional problems appearing in, e.g. image classification and 3D and 4D imaging.
Within this context she is interested in both the rigorous theoretical and computational analysis of the problems considered as well as their practical implementation and their use for real-world applications.
Currently, her research focuses on customising variational image analysis and image reconstruction models to applications by learning their setup from real-world data training sets. To this end she investigates so-called bilevel optimisation techniques in which the solution is typically constrained to a non-smooth variational problem or a nonlinear PDE.
She has active interdisciplinary collaborations with clinicians, biologists and physicists on biomedical imaging topics, chemical engineers and plant scientists on image sensing, as well as collaborations with artists and art conservators on digital art restoration.

Professor Anuj Dawar

Cambridge

BIO

I am Professor of Logic and Algorithms at the University of Cambridge, where I have been a member of the faculty since 1999. I obtained a Bachelor’s degree from IIT, Delhi and a Master’s degree from the University of Delaware before going on to get my PhD from the University of Pennsylvania in 1993. Before coming to Cambridge I worked for several years as a post-doc and a lecturer at Swansea University.

RESEARCH

I have an interest in computational complexity theory, which seeks to understand the fundamental limitations of some of our most powerful algorithmic techniques. I have worked for many years on approaches to complexity based on logic – by relating computational complexity to problems of definability. Recently I have been focussing on the role of symmetry in computation, both as a resource and a limitation.

Dr Phillip Stanley-Marbell

Cambridge

Bio

Phillip is an Assistant Professor (UL) in the Department of Engineering at the University of Cambridge. Prior to joining Cambridge, he was a researcher at MIT, from 2014 to 2017. He received his Ph.D. from CMU in 2007, was a postdoc at TU Eindhoven until 2008, and then a permanent Research Staff Member at IBM Research—Zurich. In 2012 he joined Apple where he led the development of a new system component now used across all iOS and macOS platforms. Prior to completing his Ph.D., he held positions at Bell-Labs Research, Lucent, Philips, and NEC Research Labs.

Research

His research interests are in computing system hardware, architectures, and algorithms that interact with the physical world and which harness an understanding of the physical world and the flexibility of sensing systems for improved energy-efficiency and performance, and for reduced measurement uncertainty. In collaboration with colleagues at the Alan Turing Institute, Phillip is exploring new research challenges on sensor-rich materials, and on computing hardware and embedded sensing architectures which facilitate tracking measurement uncertainty throughout the sensing and computation process.

Professor Bill Byrne

Cambridge

Bio

Bill Byrne is a member of the Machine Intelligence Lab in the Cambridge University Engineering Department. His background is in information theory and statistical signal processing. Bill’s research is focused on the statistical modelling of speech and language with a particular current interest in statistical machine translation.

Research

Bill hopes to study hybrid symbolic and neural modelling approaches for language processing. His aim will be to develop techniques that combine the fluency and naturalness of `neural` models with the robustness and descriptive power of symbolic approaches.

Dr John Reid

Cambridge

Bio

Dr John Reid is a Senior Statistician working in systems biology at the MRC Biostatistics Unit in the University of Cambridge. He uses machine learning methods to study biological and medical problems. He has a particular interest in the synthesis of Bayesian nonparametric models and deep learning and their application to gene network inference.

Research

Dr John Reid’s research at the Turing involves unbiased discovery of structure in large neuroimaging data sets and subsequent unsupervised clustering. The desired outcome is to discover novel meaningful hierarchical decompositions of psychiatric disorders.

John is a Turing Fellow through his co-supervision of Alexander Campbell, a doctoral student at the Turing.

Dr John Suckling

Cambridge

Bio

Dr Suckling is a physicist with over 25 years’ experience in medical imaging. His research career began at the Royal Marsden Hospital working on new hardware and software for positron emission tomography. After a short period at University College London developing teaching materials for the biomechanics of the hand and arm, John returned to brain imaging at the Institute of Psychiatry, Kings College London when the then new techniques of structural and functional neuroimaging were challenging long-held views on mental health disorders. He continues this work in the Department of Psychiatry, University of Cambridge, applying neuroimaging to investigate diverse psychopathological conditions.

Research

Globally, psychiatric disorders are the leading cause of years lived with disability. Currently diagnostic structures rely on clustering clinically observed symptoms, and their evolution over time. This approach is not naturally aligned with underlying neurobiological systems, making it difficult to bring to bear advances in neuroscience. Data driven approaches to large datasets of symptoms have demonstrated a natural decomposition with a general vulnerability factor to mental health disorders, and sub-labels with prima facie validity. Similar, stable decompositions of imaging features would be significant evidence for a new, biological basis to psychiatric diagnosis.

John is a Turing Fellow through his co-supervision of Alexander Campbell, a doctoral student at the Turing.

Dr Jose Miguel Hernández Lobato

Cambridge

Bio

José Miguel is a University Lecturer in Machine Learning at the Department of Engineering in the University of Cambridge, UK. Before, he was a postdoc in the Harvard Intelligent Probabilistic Systems group at Harvard University, working with Ryan Adams, and a postdoc in the Machine Learning Group at the University of Cambridge (UK), working with Zoubin Ghahramani. Jose Miguel completed his Ph.D. and M.Phil. in Computer Science at the Computer Science Department in Universidad Autónoma de Madrid (Spain), where he also obtained a B.Sc. in Computer Science, with a special prize to the best academic record on graduation.

Research

Part of my research is in the application of machine learning to the efficient solution of expensive optimization problems. For example, in optimal design in engineering, where the goal is to obtain better and more effective products. In many of these design problems the analytic form of the objective function is unknown and its evaluations are very expensive. Bayesian optimization (BO) methods can reduce the number of evaluations required to solve the aforementioned problems. In my research I aim at designing novel methods for Bayesian optimization that will accelerate optimal design problems across a wide range of engineering areas.

Professor Sylvia Richardson

Cambridge

Bio

Professor Sylvia Richardson is Director of the MRC Biostatistics Unit and holds the Chair of Biostatistics in the University of Cambridge since 2012. Prior to this, she was Directeur de Recherches at the French National Institute for Medical Research. In 2000, she moved to the UK to take up the Chair of Biostatistics at Imperial College. She was awarded the Guy Medal in Silver from the Royal Statistical Society in 2009. She is a Fellow of the Institute of Mathematical Statistics, of the International Society for Bayesian Analysis and was elected Fellow of the Academy of Medical Sciences in 2016.

Research

She has worked extensively on statistical methodology and applications to the health sciences. Her main interest is centred on modelling and computational aspects of Bayesian statistics applied to complex-highly structured biological and epidemiological data sets. Her recent research has focussed on the analysis of large data problems such as those arising in genomics and on developing methods and scalable algorithms for clustering, sparse regression and large scale hierarchical analysis of high dimensional biomedical and multi-omics data.

Dr Eiko Yoneki

Cambridge

Bio

Eiko Yoneki is a Senior Researcher in the Systems Research Group of the University of Cambridge Computer Laboratory. She has received her PhD in Computer Science from the University of Cambridge on ‘Data Centric Asynchronous Communication’. During her postdoctoral work, she has worked on complex, time-dependent networks and multi-point communication inspired by social science and biology. Eiko has held an EPSRC Early Career Fellowship until 2016. Prior to academia, she has worked with IBM in USA, Japan, Italy and UK, where she received the highest Technical Award.

Research

Eiko’s research spans distributed systems, networking and databases, including complex network analysis, and parallel computing for large-scale graph processing. Her current focus is on auto-tuning to deal with complex parameter space using machine-learning. She want to apply her group’s recent work, structured Bayesian Optimisation or Reinforcement Learning framework to existing problems, and build a solid auto-tuning platform in a complex parameter space. Optimisation of complex data processing is essential for data science, including the processing capability of computer systems. The multi-dimensional design space for optimising applications is huge. Today, techniques for load balancing, job scheduling and adaptive processors require run-time optimisations that depend on the dynamics of computation resources.

Dr Simon Frost

Cambridge

Bio

Dr Simon Frost is Reader in Pathogen Dynamics at the University of Cambridge, working on the dynamics and evolution of infectious disease, particularly those caused by rapidly evolving RNA viruses. His undergraduate training was in Natural Sciences at Cambridge (1992), and he received his D.Phil. from Oxford in mathematical biology (1996), with postdoctoral training at Princeton (1996), Oxford (1997), Edinburgh (1997-2000), and UC San Diego (2000-2004). After becoming an associate professor at UCSD, Dr Frost returned to the UK as a Royal Society Wolfson Research Merit award holder (2008-2013) to work on zoonotic disease.

Research

Dr Frost is working on real-time, scalable inference of epidemiological datasets related to infectious diseases, integrating many – often imperfect – sources of data, generating inferences of quantities such as transmission rates on-the-fly. He would like to understand how these can be performed at scale, and how approaches such as emulation, where machine learning approaches are used to learn the outputs of a model, could be used to achieve real-time responses. A particular challenge in developing such approaches is how to make the results – and in particular the associated uncertainty – accessible to public health officials, policy makers etc..

Dr Nigel Collier

Cambridge

Bio

Nigel is a Director of Research in Computational Linguistics at the University of Cambridge. Before this he was a Marie Curie Research Fellow at the European Bioinformatics Institute and an Associate Professor at the National Institute of Informatics in Japan. He received his PhD in 1996 from The University of Manchester Institute of Science and Technology (now The University of Manchester) for his research into how a Hopfield network (a type of recurrent artificial neural network) could be used to perform machine translation.

Research

The current focus of Nigel’s research is on machine learning for Natural Language Processing (NLP) with application to health. Health data includes many types of large-scale unstructured sources including informal patient self reports in the social media, news reports, electronic patient records and the scientific literature. I am particularly interested in techniques for grounding text to domain ontologies to enable better machine understanding and knowledge discovery. At the same time I am also interested in forming bridges to other disciplines, particularly around the social and ethical dimensions of new types of health text data.

Nigel is a Turing Fellow through his supervision of Alexander Mansbridge, a doctoral student at the Turing.

Dr Anita Faul

Cambridge

Bio

Anita Faul came to Cambridge, UK, after studying two years in Germany. She did Part II and Part III Mathematics at Churchill College, Cambridge. Since these are only two years, and three years are necessary for a first degree, she does not hold one. However, this was followed by a PhD on the Faul-Powell Algorithm for Radial Basis Function Interpolation under the supervision of Professor Mike Powell. After this she worked on the Relevance Vector Machine with Mike Tipping at Microsoft Research Cambridge. Ten years in industry followed where she worked on various algorithms on mobile phone networks, image processing and data visualization. She is a fellow of Selwyn College, Cambridge, and a Teaching Associate at the Laboratory for Scientific Computing at the Maxwell Centre in Cambridge. In teaching, she enjoys to bring out the underlying, connecting principles of algorithms which is the emphasis of her book on Numerical Analysis. She is working on a book on machine learning.

Research

When building models from data an important consideration is the space in which the model lies. The model space is often application specific. The question is how to best choose the model space or let the data make the choice automatically.

Anita is a Turing Fellow through her supervision of Vidhi Lalchand, a doctoral student at the Turing.

Professor Vasco M. Carvalho

Cambridge

Bio

Vasco M. Carvalho is currently Professor of Macroeconomics and Senior Keynes Fellow at the Faculty of Economics of the University of Cambridge.  He is also a co-ordinator of the Cambridge-INET institute and a Fellow of Jesus College. Having gained his PhD at the University of Chicago, he was a Junior Researcher at CREI in Barcelona and Affiliated Assistant Professor at University Pompeu Fabra before joining the University of Cambridge in 2013. He was awarded the 2014 Wiley Prize in Economics by the British Academy for “achievement in research by an outstanding early career economist”.  He is the Principal Investigator of the European Research Council Grant “MacroNets: Production Networks in Macroeconomics” and of a Leverhulme Prize Fellowship.

Research

Professor Carvalho’s research focuses on the origins of business cycles. His research has shown how the organization of production around supply-chain networks exposes the aggregate economy to disruptions in critical nodes along these chains. Ongoing research seeks to further our understanding of how production networks affect macroeconomic outcomes by (i) providing empirical evidence on both the mechanisms and impact of such supply chain disruptions; (ii) analysing the dynamics of supply-chain formation and input diffusion; (iii) showing how production networks affect the size and direction of innovation and long-run growth.

Yarin Gal

Cambridge

BIO

Yarin is a research fellow at St Catherine’s College, Cambridge, and part-time fellow at the Alan Turing Institute. He obtained his PhD from the Cambridge machine learning group, working with Zoubin Ghahramani and funded by the Google Europe Doctoral Fellowship. Prior to that he studied at Oxford Computer Science for a Master’s degree under the supervision of Phil Blunsom.

RESEARCH

Yarin’s interests lie in the fields of linguistics, applied maths, and computer science. Most of his work is motivated by problems found at the intersections of these fields. In his current research he develops Bayesian techniques for deep learning, with applications to reinforcement learning. In the past he has worked on Bayesian modelling, approximate inference, and natural language processing. This work included Bayesian nonparametrics, Gaussian processes, inference algorithms for big data, and work on machine translation.

Edinburgh

Dr Sotirios Tsaftaris

Edinburgh

Bio

Dr. Sotirios (Sotos) Tsaftaris received the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from Northwestern University, USA, in 2003 and 2006, respectively. He is currently a Chancellor’s Fellow (at a Senior Lecturer level) at the University of Edinburgh. Previously he was on the faculty of Northwestern University, USA and IMT Lucca, Italy. He has published extensively, particularly in interdisciplinary fields, with more than 100 journal and conference papers in his active record. His research interests are machine learning, computer vision, image analysis, image processing, and distributed computing. http://tsaftaris.com

Research

I am interested in machine learning methods that work with multimodal input sources arising in medical imaging (health & wellbeing) applications where multiple information sources are used. Merging information across different sources should allow us to better characterize disease and its evolution. Since key to this is our ability to learn appropriate multimodal representation spaces, representation learning is an important aspect of my research. In my team we work on how we can devise methods that can advantage of such diverse sources whilst learning such spaces. Equally interesting is the ability to transfer these methods to new data sources (transfer learning) and even new tasks (multitask learning). Recent successes include deep learning models that use multimodal sources for image synthesis and biomarker discovery.

Professor Jose Vicente Rodríguez Mora

Edinburgh

Bio

José Vicente Rodríguez Mora received his Ph.D. from MIT. Currently he is a Professor of Economics at the University of Edinburgh. In the past he worked at Universitat Pompeu Fabra and Southampton. He also made long academic visits to Stockholm University, the University of Minnesota and the Minneapolis Fed. When not at work, his favourite thing to do is to ski down a mountain, preferably along with my his children.

Research

Professor Rodríguez Mora is interested in the theory and empirics of macroeconomics with a particular focus on aspects of what is sometimes called ‘social economics’, but by no means restricted to it. He has worked extensively on issues related to inheritance and social mobility. Their measurement, their causation and their effects on allocation of resources and macroeconomics. He has also worked on political economy, unemployment, the relationship between the financial sector and resource (mis)allocation, GDP measurement, international and inter-regional trade and their relationship to the costs of independence; and the economics of information

 

Dr Chris Dent

Edinburgh

Bio

Dr. Chris Dent is Reader (Associate Professor) in Industrial Mathematics in the School of Mathematics at the University of Edinburgh and held research and academic positions at Heriot-Watt, Marburg, Edinburgh and Durham Universities. He holds an MA in Mathematics (Cambridge University), PhD in Theoretical Physics (Loughborough University), and MSc in Operational Research (Edinburgh University). Since 2007 he has worked full time in energy systems analysis, and currently concentrates on security of supply risk analysis, and use of large scale computer models in decision making. He is a Senior Member of the IEEE, a Fellow of the Operational Research Society, and a Chartered Engineer.

Research

Energy systems are growing in complexity and there is a need to manage uncertainty on a range of timescales (from short run uncertainty in renewable generation output, to uncertainty in planning background when taking major policy and capital planning decisions). Dr. Dent is broadly interested in the application of data science across energy system and planning, and has a particular interest in how approaches from data science can productively be taken to practical application in government and industry.

Dr Vaishak Belle

Edinburgh

Bio

Vaishak Belle is a Chancellor’s Fellow at the School of Informatics, University of Edinburgh, UK. Vaishak’s research is in artificial intelligence, machine learning and knowledge representation. Previously, he was at KU Leuven, the University of Toronto, and the Aachen University of Technology. He has co-authored several articles in AI-related venues, and his work has won the Microsoft best paper award at UAI, the Machine learning journal best student paper award at ECML-PKDD, and the Kurt Goedel silver medal.

Research

His research is at the intersection of machine learning and symbolic systems (logics, programs), in service of the science and technology of artificial intelligence.

He is motivated by the need to augment learning and perception with high-level structured, commonsensical knowledge, to enable systems to learn faster and more accurate models of the world. He is most keen on computational frameworks that are able to explain their decisions, modular, re-usable, and robust to variations in problem description. Concretely, he works on themes such as probabilistic inference and learning, probabilistic programming, automated planning, statistical relational learning and epistemic reasoning.

Dr Harry van der Weijde

Edinburgh

Bio

Dr Harry van der Weijde is an applied economist and operations researcher. He is currently Chancellor’s Fellow (tenure-track lecturer) in the School of Engineering of the University of Edinburgh, having previously held research positions at University of Cambridge and the Vrije Universiteit Amsterdam. He holds a PhD in Spatial Economics from the Vrije Universiteit Amsterdam and an MSc in Economics from the University of Edinburgh. In his research, he tries to answer economic questions about engineering systems (primarily energy and transportation) using mathematical tools such as optimisation and complementarity modelling.

Research

At the Turing Institute, Dr van der Weijde will look at data science applications to energy systems. In particular, he hopes to collaborate with colleagues in statistics, mathematics and informatics to develop methods that can quantify the monetary value of new data sources in energy applications. The energy industry is very excited about new data sources, such as smart meter data, but somewhat surprisingly, nobody seems to know what their value is to planners, system operators and utilities, and which parts of the enormous new datasets (which cannot always be used in their entirety) are particularly useful.

Professor Mark Steedman

Edinburgh

Bio

Mark Steedman is Professor of Cognitive Science in the School of Informatics at the University of Edinburgh. Previously, he taught as Professor in the Department of Computer and Information Science at the University of Pennsylvania, which he joined as Associate Professor in 1988. His PhD in Artificial Intelligence is from the University of Edinburgh. He is a Fellow of the Association for the Advancement of Artificial Intelligence, the British Academy, the Royal Society of Edinburgh, the Association for Computational Linguistics, and the Cognitive Science Society, and a Member of the European Academy.

Research

His research interests cover issues in computational linguistics, artificial intelligence, computer science and cognitive science, including syntax and semantics of natural language, wide-coverage parsing and open-domain question-answering, comprehension of natural language discourse by humans and by machine, grammar-based language modeling, natural language generation, and the semantics of intonation in spoken discourse. Much of his current NLP research is addressed to probabilistic parsing and robust semantics for question-answering using the CCG grammar formalism, including the acquisition of language from paired sentences and meanings by child and machine. He sometimes works with colleagues in computer animation using these theories to guide the graphical animation of speaking virtual or simulated autonomous human agents. Some of his research concerns the analysis of music by humans and machines.

Mark is a Turing Fellow through his supervision of Javad Hosseini, a doctoral student at the Turing.

Dr Sotirios Sabanis

Edinburgh

Bio

Sotirios Sabanis is a Reader (Associate Professor) at the School of Mathematics of the University of Edinburgh. He received his undergraduate training in Mathematics at the Aristotle University of Thessaloniki and was awarded a PhD from Strathclyde University (Department of Statistics and Modelling Science), Glasgow. As Programme Director, he has led the development of the suite of postgraduate programmes in Computational Mathematical Finance at the University of Edinburgh and collaborates with industry (mainly financial services) through a number of joint projects. His research has appeared in the Annals of Applied Probability, SIAM Journal of Numerical Analysis, Stochastic Partial Differential Equations and other leading journals.

Research

My research focus recently has been on explicit numerical algorithms for nonlinear random systems of (typically) high dimension and their interplay with data science techniques. Examples of these algorithms are explicit numerical schemes for Stochastic (Partial) Differential Equations, stochastic approximation methods in the context of recursive identification of system parameters and MCMC algorithms. Any application of these methodologies to financial data is of keen interest to me.

Dr Pramod Bhatotia

Edinburgh

Bio

Pramod Bhatotia is a Senior Lecturer in the School of Informatics at the University of Edinburgh, and a Faculty Fellow at the Alan Turing Institute. Before moving to the UK, Pramod was a Research Group Leader at TU Dresden, where he led the Parallel and Distributed Systems group. Pramod graduated with a PhD (2015) from the Systems group at the Max Planck Institute for Software Systems (MPI-SWS). During his PhD studies, he interned/collaborated with Microsoft Research, IBM Research, Yahoo! Research, and Bell Labs. Prior to joining MPI-SWS, Pramod was a member of technical staff in the HPC group at IBM Research.

Research

Pramod Bhatotia is a Senior Lecturer in the School of Informatics at the University of Edinburgh, and a Faculty Fellow at the Alan Turing Institute. Before moving to the UK, Pramod was a Research Group Leader at TU Dresden, where he led the Parallel and Distributed Systems group. Pramod graduated with a PhD (2015) from the Systems group at the Max Planck Institute for Software Systems (MPI-SWS). During his PhD studies, he interned/collaborated with Microsoft Research, IBM Research, Yahoo! Research, and Bell Labs. Prior to joining MPI-SWS, Pramod was a member of technical staff in the HPC group at IBM Research.

Dr Michal Branicki

Edinburgh

Bio

I work on the interface of applied probability, information theory and dynamical systems with applications to Bayesian data assimilation, Bayesian learning, stochastic control, and data-driven dimension reduction in stochastic systems. I am particularly interested in theoretical and computational aspects of probabilistic approaches to prediction and uncertainty quantification in dynamical systems, and methods for Bayesian time-dependent inverse problems in high-dimensions. These themes require a systematic integration of data-driven and model-based techniques for state estimation and classification problems from large sets of noisy data in order to maximise information flow from the available data to model dynamics.

Research

Provably robust techniques for data-driven construction of predictive methods for state estimation and classification from large sets of noisy data sets embedded in high-dimensions. The overarching approach relies on the synergy between diffusion maps, topological data analysis and dimension reduction; here, topological tools naturally complement manifold learning techniques based on concepts from harmonic analysis and information geometry. Two focus areas are: (i) Ranking of discrete data sets with incomplete information in a ‘small & noisy data’ regime (applications to latent topic modelling), (ii) Consistent manifold recovery for topological data analysis, spectral clustering, and equation-free modelling for data assimilation.

 

Dr Kenneth Heafield

Edinburgh

Bio

Kenneth Heafield is a Lecturer in the School of Informatics at the University of Edinburgh where he leads a machine translation group.  He wrote the KenLM library for efficient n-gram language modeling and now works to make neural machine translation faster and higher-quality.  He holds a PhD from Carnegie Mellon’s School of Computer Science and did a postdoc at Stanford.

Research

Kenneth combines natural language processing and systems to make and scale models for machine translation.  Neural networks have improved translation quality and their ability to learn, or not learn, natural language phenomena is an open research area.  However, the high computational cost of training neural network models has slowed experimentation and has forced researchers to decrease training data sizes, sometimes by three orders of magnitude.  Through the Alan Turing Institute, I am collaborating with Intel to accelerate neural networks and challenge the HPC community with real-world natural language processing tasks.

Professor Chris Williams

Edinburgh

Bio

Chris Williams is Professor of Machine Learning in the School of Informatics, University of Edinburgh, and is The Alan Turing Institute’s University Liaison Director for Edinburgh. He obtained his MSc (1990) and PhD (1994) at the University of Toronto, under the supervision of Geoff Hinton. He was a member of the Neural Computing Research Group at Aston University from 1994 to 1998, and has been at the University of Edinburgh since 1998.

Research

Chris is interested in a wide range of theoretical and practical issues in machine learning, statistical pattern recognition, probabilistic graphical models and computer vision. This includes theoretical foundations, the development of new models and algorithms, and applications. His main areas of research are in models for understanding time-series, visual object recognition and image understanding, unsupervised learning, and Gaussian processes. At the Turing he also has interests in improving the data analytics process, looking to address the issues of data understanding and preparation that are widely quoted as taking around 80% of the time in a typical data mining project.

Dr Aretha Teckentrup

Edinburgh

Bio

Dr Teckentrup is a lecturer in data science in the School of Mathematics. Before coming to Edinburgh, she held postdoctoral research positions at Florida State University and the University of Warwick. Dr Teckentrup received her PhD in applied mathematics from the University of Bath in 2013.

Research

Aretha’s research interests are at the interface of numerical analysis, statistics and data science. She is particularly interested in uncertainty quantification in simulation with complex computer models, with recent research focussing on multilevel sampling methods, Bayesian inverse problems, Gaussian process regression, approximation theory and deep Gaussian processes.

Dr. Kostas Zygalakis

Edinburgh

Bio

He received his PhD in computational stochastic differential equations from University of Warwick at 2009 and held postdoctoral positions at the Universities of Cambridge, Oxford and the Swiss Federal Institute of Technology, Lausanne. In 2011 he was awarded a Leslie Fox Prize (IMA UK) in numerical analysis. Before joining Edinburgh as a lecturer in the mathematics of Data Science in 2016, he held a lectureship in Applied Mathematics at the University of Southampton.

 

Research

His research is on the interplay between numerical analysis and computational statistics. In particular, he is interested in the properties of long time approximation to stochastic differential equations and their connection to sampling algorithms. Furthermore, he has recently started working on problems related to Data Assimilation and Bayesian inverse problems, as well as applications of ideas of numerical analysis to machine learning algorithms.

Professor Peter Boyle

Edinburgh

Bio

Peter received his PhD in Particle Physics from the University of Edinburgh and has been a reader in the Theoretical Particle Physics research group at Edinburgh since 2004.

Peter is a member of the RBC-UKQCD collaboration, and collaborates with Columbia University, Southampton, Brookhaven National Laboratory, RIKEN Brookhaven Research Center, University of Virginia, University of Connecticut.

Research

Low energy Quantum Chromodynamics using computer simulation, and particularly matrix elements of hadrons that are required to constrain fundamental parameters of the standard model and search for physics beyond the standard model. Peter believes his publications represent the best constraints on Vus and BK which enter the famous unitarity triangle.

Subtopics: Kaon matrix elements – BK, Kl3 , K->pi pi. Non-perturbative renormalisation of Lattice operators. Chiral lagrangian. Hadron spectrum and decay constants. Electromagnetic effects.

Professor Ruth King

Edinburgh

Bio

Ruth King is the Thomas Bayesí Chair of Statistics at the University of Edinburgh. She was awarded her PhD in 2001 from the University of Bristol. She then held positions at the Universities of Cambridge (PDRA; 2001-3) and St Andrews (lecturer 2003-10; reader 2010-15) before taking up her appointment at the University of Edinburgh in 2015.

Research

My main research interests involve the development of new statistical models and associated model-fitting techniques and their application to real data problems. Particular areas of interest include hidden Markov models/state-space models; missing data; Bayesian inference; incorporating different forms of heterogeneity; capture-recapture; and applications particularly within ecology and epidemiology.

Dr Nick Polydorides

Edinburgh

Bio

Nick Polydorides is a Senior Lecturer in the Institute for Digital Communications, at the School of Engineering at Edinburgh University. He was joined Edinburgh in fall 2013 from the MIT-established Cyprus Institute where he was an Assistant Professor. Prior to that he held postdoctoral positions at the University of Manchester and the MIT. His research interests are in applied inverse problems and in particular algorithms for large-scale electromagnetic imaging and tomography in geophysics, biomedicine, and material non-destructive characterisation.

Research

At the Alan Turing Institute I am keen to explore new algorithms for ëfitting data into modelsí in the context of Bayesian inference. Some key questions underpinning this quest are: how can we predict efficiently and accurately model observations conditioned to our prior knowledge on the models? Reversely, how can model parameters be estimated with robustness from incomplete and inconsistent data sets? Relevant to this context is also the problem of optimal design, which aims to yield an optimal set of data whose information n content complements that of the prior models, and yield estimators with suppressed uncertainty levels. I hope to address some of these questions, and implement some new ideas in code and analysis.

Dr Beatrice Alex

Edinburgh

Bio

Dr. Beatrice Alex is a Senior Research Fellow at the School of Informatics at the University of Edinburgh.  Her research interests center around information extraction and text mining.  She has worked on a many projects involving natural language processing applied to different domains, including biomedicine, astronomy, news, recruitment, history and most recently social media and literature.

Research

Project Description:
Title: Data Analytics for Large Speech Archives: Mining transcripts of audio and video collections
Accessibility, interpretability and linking of data have become the focus of many data providers and users. Most research in this area has been directed towards making textual resources more accessible. Audio and video archives need to be regarded as equally important. Cataloguing and transcription of speech archives is extremely labour-intensive and time-consuming. The goal of my proposal is to mine transcripts of large speech archives automatically to generate metadata for such collections. I will be working closely with the British Library and apply my work to TV and radio recordings held at their archive.

Professor David Aspinall

Edinburgh

BIO

David Aspinall holds a personal chair in Software Safety and Security at University of Edinburgh. He leads the Security and Privacy research group in the School of Informatics and a cross-discipline Cyber Security & Privacy Research Network in the University.  His research interests cover foundational and applied areas of computer security, as well as interactive theorem proving and proof engineering.

 

RESEARCH

Professor Aspinall wants to pursue several ideas in the Alan Turing Institute.  First, he plans to work on applying interactive theorem proving to cryptography, by formalising game and simulation based cryptographic soundness proofs on machine.  Second, he wants to build on earlier work that combines machine learning and formal methods, which he used to construct robust malware classifiers for Android applications: he wants to extend this work to other sources of data, including data collected by the security industry, such as network traffic and log data, to develop better methods of anomaly detection and inference from event streams.  Finally, he is interested in privacy and security issues behind personal data, including data that is collected in eHealth and mHealth solutions, or IoT devices.

 

Dr Natalia Bochkina

Edinburgh

BIO

Natalia Bochkina joined the University of Edinburgh as a Lecturer in Statistics in 2007. In 2003-2007 she was a postdoc at the Biostatistics group at the Imperial College London working on the collaborative project building a biological atlas of insulin resistance. In 2002-2003 she was a statistician in biopharmaceutical company Oxford GlycoSciences (Ltd), completing her PhD in 2002 at the University of Bristol.

 

RESEARCH

I am interested in understanding theoretical properties of decision-making under uncertainty which arises in statistics and in machine learning motivated by practical applications. Currently my main areas of research are study of the rate of convergence and local approximation of posterior distribution (Bernstein-von Mises theorem) in Bayesian nonparametric, semi-parametric and high dimensional models which can be misspecified, non-regular or ill-posed, including inverse problems.

In additional to theory, I am interested in statistical modelling of high throughput genomic data, including the current project on estimating gene interaction networks using graphical models.

An exciting emerging area is taking into the account computational complexity when studying efficiency of the decision-making. One of the tools is calibration of faster approximate Bayesian models using techniques for misspecified models to achieve best possible efficiency.

Professor Anthony Kennedy

Edinburgh

BIO

Tony Kennedy has been Professor of Computational Science in the School of Physics and Astronomy at the University of Edinburgh since 1998, before which he spent 19 years in the United States working at the University of Maryland, the Institute for Theoretical Physics (ITP) at the University of California at Santa Barbara (UCSB), and the Supercomputer Computations Research Institute (SCRI) at Florida State University (FSU).

 

RESEARCH

His research interests span several disciplines within Theoretical Physics, Mathematics, and Computer Science.

  • His principal research area has been developing algorithms for non-perturbative computations in relativistic quantum field theory, particularly in Quantum Chromodynamics (the theory of the strong nuclear force).  This involves evaluating infinite-dimensional (functional) integrals numerically using Markov Chain Monte Carlo (MCMC) methods.  He introduced the Hybrid (or Hamiltonian) Monte Carlo algorithm in 1987, and several subsequent developments based on this such as the Rational HMC algorithm (which uses optimal rational approximations to improve stochastic estimates for functional determinants), and methods for using Shadow Hamiltonians to automate the determination of optimal parameters for symplectic integrators for use in HMC computations.

 

He is especially interested in applying MCMC and other statistical methods to other areas, such as machine learning (ML), as well as developing a better understanding the theoretical basis of ML in general.

  • He has also interested in high-performance computer architecture and in ways of developing efficient, modular, reusable software for such systems. He was part of the collaboration that built the QCDSP computer for lattice QCD computations (which won a Gordon Bell prize in 1998), and in the subsequent QCDOC computer which directly led to IBM’s Blue Gene supercomputers.
  • Other interests include various areas of theoretical and mathematical physics (such as renormalization theory), algorithms for computations using representations of groups and algebras, algorithms for computer algebra and related category theoretic ideas for computer languages.

 

Professor Ben Leimkuhler

Edinburgh

Bio

I study the design, analysis and implementation of algorithms for time-dependent phenomena and modelling for problems in engineering and the sciences. My previous works have helped to establish the foundations of molecular simulation, providing efficient deterministic and stochastic numerical methods for an exploding field of application. A recent line of research as focussed on stochastic algorithms for Bayesian inference from data and has demonstrated the potential for crossover of work from molecular science to data analytics (in this case molecular dynamics temperature controls were used to stabilise sampling-based parameterisation schemes).

Research

Data science disrupts the traditional mathematical model by replacing physical law with empirical law, through the need to incorporate inference based on massive data sets or streams, and by destroying smooth structures (ODE/PDE solutions) that underpin numerical analysis.   Data science is grounded in statistics and optimization, but to be effective in the engineering setting, data analysis methods such as Bayesian inference must be merged with models built up over many centuries on a foundation of physical law (e.g. quantum mechanics and thermodynamics) and be compatible with dynamical principles and geometric (or topological) constraints. During my fellowship I plan to explore the interplay between “naive” data science approaches and models informed by physical law and mathematical structure. Access to the Alan Turing Institute is already providing me with connections to the statistics, operational research and machine learning communities. I look forward to expanding these connections in the coming years.

Dr Ioannis Papastathopoulos

Edinburgh

BIO

I was born in Athens, Greece, and received my undergraduate training in Statistics and Insurance Science at the University of Piraeus. Then I received my MSc in Statistics and PhD in Statistics from Lancaster University and held a Brunel Research Fellowship in Statistics at the School of Mathematics, University of Bristol.

 

RESEARCH

My ATI research related interests and activities focus on the broad area of extreme value modelling and inference for rare and catastrophic events.Extreme events are multi-dimensional in nature, can cause havoc for the people affected and typically result in large financial losses. A wide variety of geophysical data is now acquired with very broad wavelength ranges from satellites and many other sources.This has led to increasingly complex and large datasets which require new methodological tools to analyse.I am particularly
interested in exploring novel approaches for developing such tools and in identifying and communicating risks imposed by the society, economy and nature.My research interests include:

– Conditional dependence structures and Graphical models;
– item High-dimensional inference;
– item Scalable data fitting algorithms;
– item Spatio-temporal extreme value modelling.

Dr Peter Richtarik

Edinburgh

BIO

Peter Richtarik is a Reader in the School of Mathematics at the University of Edinburgh, and is the Head of a Big Data Optimization Lab. He received his PhD from Cornell University in 2007, and currently holds an EPSRC Early Career Fellowship in Mathematical Sciences.

 

RESEARCH

My main research focus is the development of new optimization algorithms and theory. In particular, much of my recent work is in the emerging field of big data optimization, with applications in machine learning in general and empirical risk minimization in particular. For big data optimization problems, traditional methods are no longer suitable, and hence there is need to develop new algorithmic paradigms. An important role in this respect is played by randomized algorithms of various flavors, including randomized coordinate descent, stochastic gradient descent, randomized subspace descent and randomized quasi-Newton methods. Parallel and distributed variants are of particular importance.

Dr Lukasz Szpruch

Edinburgh

BIO

I am a Lecturer at the School of Mathematics, University of Edinburgh. Before moving to Scotland I was a Nomura Junior Research Fellow at the Institute of Mathematics, University of Oxford, and a member of Oxford-Man Institute for Quantitative Finance. I hold a Ph.D. in mathematics from University of Strathclyde in Glasgow.

 

RESEARCH

My research interests are in theoretical and applied probability theory. I have a particular interest in:

– (Multilevel) Monte Carlo Methods
– Stochastic Interacting Particle Systems and Mean Filed Limits; Stochastic McKean-Vlasov Equations
– Sequential Monte Carlo (Particle Filters)
– (Stochastic) Gradient type algorithms and their applications in statistics and optimization

– Highly dimensional stochastic systems such as SDEs, BSDEs and SPDEs

Dr Charles Sutton

Edinburgh

Bio

Charles Sutton is a Reader (equivalent to Associate Professor: http://bit.ly/1W9UhqT) in Machine Learning at the University of Edinburgh. He has over 50 publications in a broad range of applications of probabilistic machine learning. His work in machine learning for software engineering has won an ACM Distinguished Paper Award. His PhD is from the University of Massachusetts Amherst, and he has done postdoctoral work at the University of California Berkeley. He is currently Director of the EPSRC Centre for Doctoral Training in Data Science at the University of Edinburgh.

Research

My research focuses on developing new machine learning methods that are motivated by the demands of new, cutting-edge practical problems. I develop new techniques in probabilistic machine learning, approximate inference in graphical models, and more recently deep learning.
In general, these motivating applications come from a wide range of areas, including natural language processing, analysis of computer systems, software engineering, sustainable energy, and exploratory data analysis. Most recently, I am particularly fascinated by two important application areas: machine learning and NLP methods to improve software development, and machine learning and artificial intelligence methods to support the full practical workflow of data science.

Oxford

Dr Sandra Wachter

Oxford

Bio

Dr. Sandra Wachter is a Researcher in Data Ethics and Algorithms at the Oxford Internet Institute, she is a member of the Ethics and Philosophy of Information research cluster and the Digital Ethics Lab. Sandra is also a Turing Research Fellow at the Alan Turing Institute in London. Her research focuses on the legal and ethical implications of Big Data, AI, and robotics as well as governmental surveillance, predictive policing, and human rights online. Prior to joining the OII, Sandra worked the Royal Academy of Engineering on topics such as connectivity, AI, and autonomous systems.

Sandra holds a Master’s and PhD in law specialising on European, International, and human rights law as well as technology and data protection law. In her PhD, she explored the concept of democracy according to the European Court of Human Rights and tested whether democracy is compatible with mass surveillance methods such as the European Data Retention Directive. Sandra also holds a Master’s of Science from the Oxford Internet Institute. Her thesis looked at tensions between freedom of speech and the right to privacy on social networks.

Twitter handle @SandraWachter5

Research

Her immediate research focuses on ethical design of algorithms, including the development of standards and methods to ensure fairness, accountability, transparency, interpretability, and group privacy in complex algorithmic systems.

Sandra’s research also addresses legal and ethical aspects of robotics (e.g. surgical, domestic and social robots) and autonomous systems (e.g. autonomous and connected cars), including liability, accountability, and privacy issues as well as international policies and regulatory responses to the social and ethical consequences of automation.

Research interests: Data Ethics; Big Data; AI; machine learning; algorithms; robotics; privacy; data protection and technology law, European, -International-, and human rights law, governmental algorithmic surveillance, and emotion detection; predictive policing .

Professor Jared Tanner

Oxford

Bio

Jared Tanner is Professor of the Mathematics of Information at the University of Oxford and The Alan Turing Institute’s University Liaison Director for Oxford. He obtained his PhD (2002) in applied mathematics at the University of California at Los Angeles, and was a postdoctoral fellow at the University of California at Davis (Maths) and Stanford University (Stats.) where he worked with David L. Donoho.  Prior to joining the University of Oxford in 2012 he was Professor of the Mathematics of Information at the University of Edinburgh (2007-2012).  He is founding editor-in-chief of Information and Inference: A Journal of the IMA, whose mission is to publish high quality mathematically oriented articles furthering the understanding of the theory, methods of analysis, and algorithms for information and data.  He is also on the editorial board for Applied and Computational Harmonic Analysis, Multiscale modelling and simulation A SIAM Interdisciplinary Journal, and was an associate editor for the Princeton Companion to Applied Mathematics.  His research has appeared in the Proc Natl Acad Sci USA, Phil Trans Royal Soc A, and other leading journals.

Research

Jared Tanner’s research concerns extracting models of high dimensional date which reveal of the essential information in the data.  Specific contributions include the derivation of sampling theorems in compressed sensing using techniques from stochastic geometry and the design and analysis of efficient algorithms for matrix completion which minimise over higher dimensional subspaces as the reliability of the data warrants.  These techniques allow more efficient information acquisition as well as the ability to cope with missing data.

Recent interests include new models for low dimensional structure in heterogeneous data and topological data analysis.

Professor Stephen Roberts

Oxford

BIO

Stephen Roberts is the RAEng/Man Professor of Machine Learning at the University of Oxford. Stephen is a Fellow of the Royal Academy of Engineering, the Royal Statistical Society, the IET and the Institute of Physics. Stephen is Director of the Oxford-Man Institute of Quantitative Finance and Director of the Oxford Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (AIMS).

 

RESEARCH

Stephen’s interests lie in methods for machine learning & data analysis in complex problems, especially those in which noise and uncertainty abound. His current major interests include the application of machine learning to huge astrophysical data sets (for discovering exo-planets, pulsars and cosmological models), biodiversity monitoring (for detecting changes in ecology and spread of disease), smart networks (for reducing energy consumption and impact), sensor networks (to better acquire and model complex events) and finance (to provide timeseries and point process models and aggregate large numbers of information streams).

Professor Alessandro Abate

Oxford

Bio

Alessandro Abate is an Associate Professor in the Department of Computer Science at the University of Oxford. He received a Laurea in 2002 from the University of Padova, an MS in 2004 and a PhD in 2007 at UC Berkeley (USA). He has been an International Fellow at SRI International in Menlo Park (USA), and a PostDoctoral Researcher at Stanford University (USA). From 2009 to 2013 he has been an Assistant Professor at TU Delft (NL).

Research

His research interests lie on the analysis, formal verification, and optimal control of stochastic hybrid systems, which are complex and heterogeneous dynamical models encompassing discrete and continuous dynamics, and with dynamics enriched by randomness. Beyond research on deductive, model-based techniques, he works on data-driven (inductive) approaches for model construction and estimation, with techniques from computational statistics (machine learning) and systems identification (an area within systems and control theory). The overall plan is to fully and precisely integrate data-driven procedures within model-based verification architectures.

Professor Pawan Kumar

Oxford

Bio

M. Pawan Kumar is an Associate Professor of Information Engineering at the University of Oxford and a fellow of Lady Margaret Hall. He is a principal researcher in the OVAL group, which focuses on the design and analysis of discrete and continuous optimization algorithms for the problems encountered in computer vision and machine learning.

Research

At the Turing, he plans to explore various problems that are central to scaling up machine learning for visual data. Examples include efficient and principled optimization algorithms for deep neural networks, learning from large-scale weakly supervised data such as freely downloadable images or videos on the Internet that have sparse and often noisy tags, compressing neural networks for memory and speed efficiency, and fast combinatorial algorithms and relaxation-based approaches for structured prediction.

Professor Varun Kanade

Oxford

Bio

Varun Kanade is a Research Lecturer in the Department of Computer Science, Oxford University. He has been a Simons Postdoctoral Fellow at the University of California, Berkeley and a FSMP postdoctoral fellow at ENS, Paris. He obtained his Ph.D. from Harvard University in 2012. His research interests are in machine learning and theoretical computer science.

Research

Varun Kanade’s research interests lie at the intersection of machine learning and theoretical computer science. At the ATI, his research will focus on mathematical foundations of machine learning, in particular statistical and computational tradeoffs, as well as improving our theoretical understanding of methods successful in practice. His work will also focus on randomized algorithms particularly for analysing large networks.

Professor Yee Whye Teh

Oxford

BIO

Yee Whye Teh is a Professor of Statistical Machine Learning at the University of Oxford, an Alan Turing Institute Faculty Fellow and a Research Scientist at Google DeepMind.  He obtained his Ph.D. at the University of Toronto, and did postdoctoral work at the University of California at Berkeley and National University of Singapore (as Lee Kuan Yew Postdoctoral Fellow).  He was a Lecturer then a Reader at the Gatsby Computational Neuroscience Unit, UCL prior to his current appointment.

 

RESEARCH

At the Alan Turing Institute, he is interested in investigating novel scalable machine learning and computational statistics methods, in particular in the frameworks of probabilistic models, Bayesian nonparametrics and deep learning. He is interested in applications to machine perception, recommender systems, genetics and genomics.

Professor Coralia Cartis

Oxford

BIO

Coralia Cartis (BSc Mathematics, Babesh-Bolyai University, Romania; PhD Mathematics, University of Cambridge (2005)) has joined the Mathematical Institute at Oxford and Balliol College in 2013 as Associate Professor in Numerical Optimization. Previously, she worked as a research scientist at Rutherford Appleton Laboratory and as a postdoctoral researcher at Oxford University. Between 2007-2013, she was a (permanent) lecturer in the School of Mathematics, University of Edinburgh.

 

RESEARCH

Assoc. Prof. Cartis’ research is on optimization, algorithm development, analysis and implementation for a variety of problem classes (linear, convex, nonconvex, smooth/nonsmooth, stochastic), suitable for large scale. In the past few years, her research focused on complexity of nonconvex optimization problems; compressed sensing; parameter estimation for climate modelling. Currently, she is investigating optimization methods for problems with imperfect or corrupted information, such as in the case of stochastic optimisation.

Professor Raphael Hauser

Oxford

Bio

Raphael Hauser studied Mathematics and Theoretical Physics at the EPFL and ETH in Lausanne and Zurich, Switzerland, followed by a PhD in Operations Research at Cornell University in Ithaca, USA. After a postdoc at Cambridge, Raphael joined the faculty at the University of Oxford, where he is currently an Associate Professor in Numerical Mathematics at the Oxford Mathematical Institute and the Tanaka Fellow in Applied Mathematics at Pembroke College.
He is a member of the Numerical Analysis Group, the Mathematical and Computational Finance Group, and of the Oxford Emirates Data Lab.

Research

Raphaelís research interests lie in optimisation algorithms, applied probability and statistics, distributed computing, and medical imaging. He is experienced in working on industrial collaborations involving real-world data and an inventor of several patents. At the Alan Turing Institute he will work on distributed algorithms for convex optimisation, extending an approach he developed for large scale principal component analysis.

Professor Francois Caron

Oxford

BIO

Since September 2015, Professor Francois Caron has been an Associate Professor in the department of Statistics and a tutorial Fellow at Keble College, University of Oxford. From September 2013 to August 2015, he was a Marie Curie Research Fellow in the department of Statistics at the University of Oxford. From 2008 to 2013, he was a Research Scientist (Chargé de Recherche) at INRIA Bordeaux. He completed his Ph.D. in Information Engineering at the University of Lille I in 2006, and spent two years as a post-doctoral researcher in the departments of Computer Science and Statistics at the University of British Columbia (2006-2008).

 

RESEARCH

Prof. François Caron’s research interests lie in the development of statistical models and computational procedures for the analysis of structured data, with particular interest in Bayesian nonparametrics and Monte Carlo methods. His recent research focuses on building probabilistic models of network data which can capture the salient properties of real-world networks (sparsity, modularity), as well as efficient algorithms for learning the hidden structure of those networks.

Professor Dino Sejdinovic

Oxford

BIO

Dino Sejdinovic is an Associate Professor at the Department of Statistics, University of Oxford and a Fellow of Mansfield College. He previously held postdoctoral positions at the Gatsby Computational Neuroscience Unit, University College London (2011-2014) and at the Statistics Group, University of Bristol (2009-2011). He received a PhD in Electrical and Electronic Engineering (Bristol, 2009).

 

RESEARCH

Dino is broadly interested in statistical foundations underpinning large-scale machine learning algorithms, with a particular emphasis on nonparametric and kernel methods. Recent research focused on discovery of higher-order interactions in datasets (when weak individual causes combine in a nonlinear way to form a strong effect), as well as on adaptive inference suited for complex models with nonlinear dependencies and intractable likelihoods. Further interests include tradeoffs between statistical, computational and communication properties of learning algorithms.

Professor Thomas Lukasiewicz

Oxford

Bio

Thomas Lukasiewicz is a Professor of Computer Science at the University of Oxford’s Department of Computer Science since 2010. Prior to this, he was holding a prestigious Heisenberg Fellowship by the German Research Foundation, affiliated with the University of Oxford, TU Vienna, Austria, and Sapienza University of Rome, Italy. Among his recent awards are the Artificial Intelligence journal’s Prominent Paper Award 2013 and the RuleML 2015 Best Paper Award. He is  associate editor for the Artificial Intelligence journal and the Journal of Artificial Intelligence Research, area editor for the journal ACM TOCL, and editor for the Semantic Web journal.

Research

Thomas Lukasiewicz’s research interests are in artificial intelligence (AI) and information systems, including especially knowledge representation and reasoning, uncertainty in AI, machine learning, the (Social and/or Semantic) Web, and databases. His research at the Alan Turing Institute focuses on the following areas: (A) value added data systems to support users in discovering, extracting, integrating, accessing, and interpreting the data of relevance to their questions; (B) integrations of deep learning and symbolic knowledge representation and reasoning; (C) question answering over the Web, in plain text documents, and in videos; and (D) personalized search and recommender systems.

Professor Arnaud Doucet

Oxford

BIO

He obtained his PhD from the University of Paris XI (Orsay) in 1997. He previously held academic positions at Cambridge University, Melbourne University, The Institute of Statistical Mathematics in Tokyo and the University of British Columbia where he was a Canada Research Chair in Stochastic Computation.

RESEARCH

Prof. Arnaud Doucet’s research concerns numerical methods for the analysis of complex data sets. In particular, he has contributed to the development and study of sequential Monte Carlo and Markov chain Monte Carlo methods.

Professor Chris Holmes

Oxford

BIO

He is a Professor of Biostatistics at the University of Oxford with a joint appointment between the Department of Statistics and the Nuffield Department of Clinical Medicine through the Wellcome Trust Centre for Human Genetics. He holds a Programme Leader’s award in Statistical Genomics from the Medical Research Council UK.

 

RESEARCH

My research interests are in statistical methods and statistical machine learning approaches for problems arising in modern biomedical science and population health. In particular in the theory, methods and applications of probabilistic data modelling and the use of subjective probability theory as a unified framework for coherent inference to tackle highly structured yet heterogeneous data sets that are now being routinely collected in these application domains. This has lead him to investigate Bayesian methods and computation, particularly for nonlinear systems in the presence of model misspecification.

Professor Terry Lyons

Oxford

BIO

Professor Terry Lyons is the Wallis Professor of Mathematics; he was a founding member (2007) of, and then Director (2011-2015) of, the Oxford Man Institute of Quantitative Finance; he was the Director of the Wales Institute of Mathematical and Computational Sciences (WIMCS; 2008-2011). He came to Oxford in 2000 having previously been Professor of Mathematics at Imperial College London (1993-2000), and before that he held the Colin Maclaurin Chair at Edinburgh (1985-93).

 

RESEARCH

Prof Lyons’ long-term research interests are all focused on Rough Paths, Stochastic Analysis, and applications – particularly to Finance and more generally to the summarsing of large complex data. More specifically, his interests are in developing mathematical tools that can be used to effectively model and describe high dimensional systems that exhibit randomness. Prof Lyons is involved in a wide range of problems from pure mathematical ones to questions of efficient numerical calculation.

Professor Ulrike Tillmann

Oxford

BIO

Prof. Ulrike Tillmann FRS has been at the University of Oxford since 1992. She is an algebraic topologist, known in particular for her work on Riemann surfaces and the homology of their moduli spaces. She has long standing research interest in homology stability questions. In 2011 she introduced an annual course (with Abramsky) in Computational Algebraic Topology at masters level. In the last year, Tillmann has co-organized four workshops on topological data analysis, as well as an CMI-LMS research school.

She held an EPSRC Advanced Fellowship 1997-2003. She was invited to present at the ICM in 2002 and was a member of the topology subject panel for both the 2010 and 2014 ICMs. In 2008 she was made a Fellow of the Royal Society and received the Bessel Forschungspreis from the Humboldt Gesellschaft. She is an inaugural Fellow of the AMS.

 

RESEARCH

Algebraic topology and its applications. Algebraic topology is a very effective tool to study the global properties of geometric objects. For example, take the surface of a ball and divide it into triangles; now count the number of faces, add the number of vertices and subtract the number of edges; no matter how you choose your triangles, the result will always be 2. Do the same with the surface of a donut and the result is always 0. These numbers were already known by Euler and are foreshadows of homology developed in the 20th century. By now the basic ideas of algebraic topology have permeated nearly every branch of research in mathematics.

Her own research has been motivated by questions in quantum physics and string theory. In particular, she has contributed to our understanding of the ‘space of surfaces’.

Professor Frank Wood

Oxford

BIO

Prof. Wood is an associate professor at the Department of Engineering Science, University of Oxford. Before that Dr. Wood was an assistant professor of Statistics at Columbia University and a research scientist at the Columbia Center for Computational Learning Systems. He formerly was a postdoctoral fellow of the Gatsby Computational Neuroscience Unit of the University College London under Dr. Yee Whye Teh. He received his PhD from Brown University in computer science under the supervision of Dr. Michael Black and Dr. Tom Griffiths.

Prof. Wood is a product of the Illinois Mathematics and Science Academy (1992). He began college at the University of Illinois at Chicago (UIC) but transferred and received a B.S. in Computer Science from Cornell University (1996). Prior to his academic career he was a successful entrepreneur having run and sold the content-based image retrieval company ToFish! to Time Warner and serving as CEO of Interfolio. He started his career working at both the Cornell Theory Center and subsequently the Lawrence Berkeley National Laboratory.

Professor Ian Horrocks

Oxford

BIO

Ian Horrocks is a Professor of Computer Science at the University of Oxford and a Visiting Professor in the Department of Informatics at the University of Oslo. He is a Fellow of the Royal Society, a member of Academia Europaea, an ECCAI Fellow and a Fellow of the British Computer Society.

 

RESEARCH

His research interests include logic-based knowledge representation and reasoning and semantic technologies, with a particular focus on ontology languages and applications. He was an author of the OIL, DAML+OIL, and OWL ontology language standards, chaired the W3C working group that standardised OWL 2, and developed many of the algorithms, optimisation techniques and reasoning systems that underpin OWL applications. His recent work includes query answering over ontologies and very large data sets, and applications in domains such as engineering, oil and gas, finance and medicine.

Professor Dan Olteanu

Oxford

BIO

Dan Olteanu is Professor of Computer Science at Oxford and Computer Scientist at RelationalAI. He also consulted for LogicBlox and taught at the universities of California Berkeley, Munich, Saarland, and Heidelberg. He received his PhD in Computer Science from University of Munich in 2005. His research interests are in databases and adjacent areas. Dan contributed to relational and XML query processing, incomplete information and probabilistic databases, and more recently to factorised databases, in-database analytics, and the LogicBlox commercial system. He co-authored the book “Probabilistic Databases” (2011). He served as associate editor for PVLDB’13 and IEEE TKDE, track chair for ICDE’15, group leader for SIGMOD’15, and vice chair for SIGMOD’17.

 

RESEARCH

His research interests are in database systems and theory. Dan contributed to XML query processing, incomplete information and probabilistic databases, and more recently to factorized databases and the industrial-strength LogicBlox database and analytics system. He is a co-author of the book “Probabilistic Databases” (2011). Olteanu has served as associate editor for PVLDB’13 and IEEE TKDE, as track chair for ICDE’15, group leader for SIGMOD’15, and will serve as vice chair for SIGMOD’17. His current research is supported by awards from Amazon, Google, and LogicBlox, and an ERC consolidator grant.

 

 

Professor Shimon Whiteson

Oxford

BIO

Professor Shimon Whiteson studied English and Computer Science at Rice University before completing my doctorate in Computer Science at the University of Texas at Austin in 2007.  He then spent eight years as an Assistant and then an Associate Professor at the University of Amsterdam before joining Oxford as an Associate Professor in 2015.  He was awarded an ERC Starting Grant from the European Research Council in 2014.

 

RESEARCH

Assoc. Prof. Shimon Whiteson’s research focuses on artificial intelligence. His goal is to design, analyse, and evaluate the algorithms that enable computational systems to acquire and execute intelligent behaviour. He’s particularly interested in machine learning, with which computers can learn from experience, and decision-theoretic planning, with which they can reason about their goals and deduce behavioural strategies that maximise their utility.

In addition to theoretical work on these topics, he has in recent years also focused on applying them to practical problems in robotics and search engine optimisation.

Dr Sarah Filippi

Oxford

BIO

Dr. Sarah Filippi joined the Department of Statistics of Oxford University in June 2014. She previously held a Medical Research Council Fellowship (2011-2104) in the Theoretical Systems Biology group at Imperial College London where she worked on a range of topics in computational statistics focused on understanding biological processes and their relation to disease. Prior to this she studied mathematics and stochastic processes at the University Denis Diderot in Paris (France) and completed her PhD in 2010 in reinforcement learning and parametric bandit models at LTCI, a joint lab of TELECOM ParisTech and CNRS.

 

RESEARCH

Dr. Filippi’s main research interests are related to the use of mathematical modelling, statistical machine learning and computational statistics to gain insight into biological processes and their role in diseases. She has a particular interest in addressing computational and statistical challenges in the analysis of genomic, transcriptomic and proteomic data at a single-cell level.

Professor Luciano Floridi

Oxford

BIO

Luciano Floridi is Professor of Philosophy and Ethics of Information at the University of Oxford, where he directs the Digital Ethics Lab (DELab) of the Oxford Internet Institute. He is also Faculty Fellow of the Alan Turing Institute and Chair of its Data Ethics research Group, and Chairman of the Ethics Advisory Board of the European Medical Information Framework. He seats on the EU’s Ethics Advisory Group on Ethical Dimensions of Data Protection, on the Royal Society and British Academy Working Group on Data Governance, and on Google Advisory Board on “the right to be forgotten”. His areas of expertise include the philosophy of information, digital ethics, and the philosophy of technology. Among his recent books, all published by Oxford University Press: The Fourth Revolution – How the infosphere is reshaping human reality (2014), The Ethics of Information (2013), The Philosophy of Information (2011).

 

RESEARCH

His areas of research are: philosophy of information, information ethics (including data ethics and computer ethics), philosophy of AI, philosophy of technology, epistemology, and logic.
http://www.oii.ox.ac.uk/people/floridi/

Dr Mariarosaria Taddeo

Oxford

BIO

Dr. Mariarosaria Taddeo Researcher – Oxford Internet Institute, University of Oxford and Faculty Fellow at the Alan Turing Institute. Her recent work focuses mainly on the ethical analysis of cyber security practices and information conflicts.

Dr. Taddeo is the 2010 recipient of the Simon Award for Outstanding Research in Computing and Philosophy and of the 2013 World Technology Award for Ethics. She is Co-Investigator, PETRAS Hub for Internet of Things, a EPSRC project and serves editor-in-chief of Minds & Machines, in the executive editorial board of Philosophy & Technology, and is the President of the International Association of Computing and Philosophy.

 

RESEARCH

Her area of expertise is Information and Computer Ethics, although she has worked on issues concerning Philosophy of Information, Epistemology, and Philosophy of AI. She published several papers focusing on online trust, cyber security and cyber warfare and guest-edited a number of special issues of peer-reviewed international journals: Ethics and Information Technology, Knowledge, Technology and Policy, Philosophy & Technology. She also edited (with L. Floridi) a volume on ‘The Ethics of Information Warfare’ (Springer, 2014) and is currently writing a book on ‘The Ethics of Cyber Conflicts’ under contract for Routledge.

Professor Charlotte Deane

Oxford

BIO

Charlotte has a wide range of research interests concerning protein structure prediction and protein interaction networks, combining both theoretical work and empirical analyses. Originally a Chemist she has moved across many traditional research disciplines and her research group is now based in Statistics. Charlotte completed her PhD in Cambridge and after a Wellcome trust fellowship in California she returned to Oxford as a University Lecturer in 2002.

 

RESEARCH

Prof. Charlotte Deane’s group works in the protein bioinformatics area. Currently the research focuses on understanding protein structure and improving our ability to model and design proteins.

To this end, the group is developing a rigorous definition of evolutionary relationships between proteins of differing structures using, in the initial stages, genomic data and building novel protein structure prediction software.

In a separate thread the group has been examining protein-protein interaction networks in terms of both their quality and use as a predictive tool.

UCL

Dr Alexandros Beskos

UCL

Bio

Dr Beskos obtained his BSc in Statistics at the Department of Statistics, Athens University in Economics and Business, in 2000. He received a PhD in Statistics from the Department of Mathematics and Statistics, Lancaster University, in 2005. He worked as a Post-Doctoral Researcher at the Mathematics Institute and the Department of Statistics from 2005 to 2008. Dr Beskos was appointed as a Lecturer in Statistics at the Department of Statistical Science, UCL in 2008; he was promoted to Senior Lecturer in 2013 and Reader in 2015.

Research

His research interests span the areas of Computational Statistics, Bayesian Methods, Monte-Carlo Algorithms, Applied Mathematics, Inverse Problems, and Biostatistics. He has worked with a number of collaborators in projects involving applications in Finance, Econometrics, Physical Sciences, Genetics, Statistical Ecology and Energy.

Alexandros is a Turing Fellow through his supervision of Shouto Yonekura, a doctoral student at the Turing.

Dr Sarah Meiklejohn

UCL

BIO

Sarah is a Reader in Cryptography and Security at University College London. She has worked on topics such as anonymity and criminal abuses in cryptocurrencies, privacy-enhancing technologies, and bringing transparency to shared systems.

RESEARCH

Sarah has worked on empirical measurements of cryptocurrencies in order to quantify various security-related aspects; most notably, she has looked at anonymity and the extent to which real deployments achieve their stated anonymity guarantees.

Sarah is a Turing fellow through her co-supervision of Andrew Burnie, a doctoral student at the Turing.

Professor David Pym

UCL

Bio

David Pym is Professor of Information, Logic, and Security at UCL and is The Alan Turing Institute’s University Liaison
Director for UCL. Heholds a PhD in logic and theoretical computer science from Edinburgh,and an MA and an ScD in mathematics from Cambridge. He is a Fellow of the IMA and the BCS. David spent many years with Hewlett-Packard’s Research Laboratories, where he developed interests in systems, security, and economics.

Research

David will work on a range of topics in security and privacy in distributed information-processing systems. He is interested in questions about access control in distributed systens, security and privacy policy, and the economics of security management. He is also interested in understanding basic questions in distributed systems architecture and behaviour, such as consistency and the relationship between systems management policies and systems architecture. David addresses these issues using ideas and techniques from logic, theoretical computer science, probability theory, and economics. He aims to build both conceptual and implemented tools to support decision-making in systems and policy design.

Dr Emine Yilmaz

UCL

BIO

She is a senior lecturer (associate professor) at University College London, Department of Computer Science and a faculty fellow of the Alan Turing Institute. She also works as a research consultant for Microsoft Research, Cambridge and serve as one of the organizers of the Centre for Computational Statistics and Machine Learning (CSML) at UCL. She is the recipient of the Karen Sparck Jones Award in 2015 and is one of the recipients of the Google Faculty Research Award in 2014/2015.

 

RESEARCH

Her research interests lie in the areas of information retrieval, web science, and applications of machine learning, probability and statistics. For more information about recent publications, please visit my publications page.

Dr Ricardo Silva

UCL

BIO

Ricardo Silva got his PhD at Carnegie Mellon in 2005, in the newly formed Machine Learning Department. He moved to UCL as a Senior Research Fellow in the Gatsby Computational Neuroscience Unit. After a year at the Statistical Laboratory at Cambridge as a postdoc, Ricardo returned to UCL to join the Department of Statistical Science as a Lecturer in 2008.

 

RESEARCH

His research focuses on:

1. Algorithms for probabilistic inference: approximations for likelihoods and posterior distributions based mostly of variational approximations and Markov chain Monte Carlo.

2. Latent variable models:  measurement error models and generalisations of probabilistic principal component analysis, as well as the modelling of network data.

3. Machine learning for causal inference: identification and discovery of models with unmeasured confounding and measurement error.

He has also recently focused on applied work on human movement modelling, including partners such as TfL (for traffic data), UCL Institute of Behavioural Neuroscience (human navigation strategies) and Stratagem Ltd (sports modelling).

Dr David Barber

UCL

BIO

He studied mathematics as an undergraduate (Cambridge) and the Statistical Mechanics of Neural Nets for my PhD (Edinburgh). He has worked in various institutions since then, joining the Department of Computer Science at UCL around 2006.

 

RESEARCH

He is interested in developing methods to make progress in tackling fundamental challenges in Artificial Intelligence.  Particularly interesting areas for me are reasoning under uncertainty and how to represent knowledge. How can we best use large scale data and large scale computation to advance AI. What kinds of problems can we not currently solve and how could we try to solve them in the future?

Dr Ioanna Manolopoulou

UCL

Bio

Ioanna has been a Lecturer in Statistics at University College London since 2012. She completed her PhD at the University of Cambridge in 2008 under the guidance of Simon TavarÈ and Steve Brooks. She then joined SAMSI (Statistical and Applied Mathematical Sciences Institute) as Postdoctoral Fellow and Duke University as Visiting Assistant Professor, working with Mike West and Sayan Mukherjee.

Research

Ioanna’s work focuses on developing Bayesian modelling and inferential tools for complex data, involving space and time processes with inhomogeneous features. Her work has been applied to various applications including customer science, ecology, health economics and accounting. Methodologically, one of her directions within the ATI will be to develop new methods for dealing with data with complex dependencies or which have not been collected completely randomly. In terms of applications, her work in retail analytics aims to provide novel customer and product segmentations as well as devise more tailored prediction algorithms. She is also interested in health economics, in particular identifying population-wide treatment effects and studying the effect of uncertainty in public health decision-making.

Dr Franz Kiraly

UCL

BIO

As a practical statistician and machine learner, Franz is interested in creating a data analytics workflow which is empirically solid, quantitative, and useful in the real world, with a focus on predictive modelling.

He is working on what he considers to be two of the most pressing challenges in a practical and data-centric context: namely, how to deal with structured data, such as learning with data samples of series, sequences, matrices, or graphs; and how to quantitatively assess and compare methods against each other, for example whether complicated algorithm X is really better than a random guess.

These are especially relevant in applications where usually the data and the associated scientific questions, and not a single method class is in the focus of interest; current project and collaboration domains include the medical sciences, sports and prevention, geoscience, physics and finance.

 

RESEARCH

Recently, Franz has been doing research on these applications:

Prediction and Prevention of Falls in a Neurological In-Patient Population. Falling, and associated injuries such as hip fracture, are a major strain on health and health resources, especially in the elderly or hospitalized. We are able to predict, with high accuracy in a neurological population, whether a patient is likely to fall during their stay, using only a number connecting test (the Trail making test).

(read more)

Quantification and Prediction in Running Sports. Characterizing the training state of running athletes, and making predictions for race planning and training. We can predict Marathon times with an error in the order of a few minutes, and we are able to accurately summarize an athlete by three characteristic numbers.

(read more)

His current work on data analysis methodology includes:

Non-linear prediction and dimension reduction with series-valued samples. We propose a new learning framework for the situation where the data samples are time series or otherwise sequentially ordered, based on kernels whose features are ordered variants of sample moments.
(read more)

Single-Entry Matrix Completion and Local Matrix Completion. Our new methods can (i) reliably impute or predict single missing entries in a numerical data table, with error bars, and (ii) do so without necessarily reading in all entries in a big data table. They are the first of their kind under the common low-rank assumption.

(read more)

Kernel Learning with Invariances. Encoding known invariances of the data, say sign/mirror symmetry, scaling or phase invariance, efficiently with a kernel (work in progress).

(read more)

Dr Ioannis Kosmidis

UCL

Bio

Ioannis Kosmidis is a Senior Lecturer at the Department of Statistical Science in University College London. Having obtained a BSc in Statistics at the Athens University of Economics and Business in 2004, he was then awarded his PhD in Statistics in 2007 at University of Warwick with a thesis titled “Bias reduction in exponential family nonlinear models“. He then held an appointment as a CRiSM Research Fellow at University of Warwick until 2010. In September 2010, Ioannis joined University College London as a Lecturer in the Department of Statistical Science, and got promoted to Senior Lecturer in 2015.

Research

Ioannis’ theoretical and methodological research focuses on optimal estimation and inference from complex statistical models, penalized likelihood methods and clustering. A particular focus of his work is the development of efficient, in terms of computational complexity and implementation, algorithms for applying the methods he develops to prominent data-analytic scenarios. He is doing extensive work in producing corresponding, well-documented, open-source software that delivers the methodological advances to the data science community and beyond (for example, the brglm, profileModel, betareg and trackeR R packages). Ioannis also actively engages in a range of cross-disciplinary applications (e.g. applications in earthquake engineering, finance, and sport and health analytics), particularly in settings where statistical modelling and the associated algorithms can impact policy making. He is the founder and leader of the Statistics in Sports and Health research group at the University College London, and associate editor for Statistics and Computing and the Journal of Statistical Software.

Detailed, up-to-date information on his research, teaching, enabling and engaging activities can be found on his website and his CV.

Professor Petros Dellaportas

UCL

Bio

Petros Dellaportas is a Professor of Statistical Science at the University College London. Previously he held academic positions in Athens University of Economics and Business, Greece.  He has published in Annals of Statistics, Biometrika, JRSSB, Statistical Science, Journal of Business and Economics Statistics, etc.  He is currently associate editor of Biometrika and Electronic journal of Statistics.

Research

His research interests include Bayesian theory and applications, computational statistics, financial modelling. In particular he currently works in problems of multivariate stochastic volatility, Gaussian processes, Bayesian mixture hierarchical models, modelling of high-frequency financial data, modelling of sports data.

Dr Hao Ni

UCL

Bio

Dr Hao Ni is a senior lecturer in financial mathematics at UCL since September 2016. Prior to this she was a visiting postdoctoral researcher at ICERM and Department of Applied Mathematics at Brown University from 2012/09 to 2013/05 and continued his postdoctoral research at the Oxford-Man Institute of Quantitative Finance until 2016. She finished her D.Phil. in mathematics in 2012 under the supervision of Professor Terry Lyons at University of Oxford.

Research

Her research interests include stochastic analysis, financial mathematics and machine learning. More specifically she is interested in non-parametric modelling effects of data streams through rough paths theory and statistical models. Rough paths theory is a non-linear extension of classical theory of control differential equations to model highly oscillatory systems, and the core concept in rough paths theory is the signature of a path, which can be used as useful features for learning to summarize sequential data in terms of its effect. Moreover, she is also interested in its applications, e.g. online Chinese handwritten character and financial data streams.

Professor George Danezis

UCL

Bio

George Danezis is a Professor of Security and Privacy Engineering at the Department of Computer Science of University College London, and Head of the Information Security Research Group. He has been working on anonymous communications, privacy enhancing technologies (PET), and traffic analysis since 2000. He has previously been a researcher for Microsoft Research, Cambridge; a visiting fellow at K.U.Leuven (Belgium); and a research associate at the University of Cambridge (UK), where he also completed his doctoral dissertation under the supervision of Prof. R.J. Anderson.

His theoretical contributions to the Privacy Technologies field include the established information theoretic and other probabilistic metrics for anonymity and pioneering the study of statistical attacks against anonymity systems. On the practical side he is one of the lead designers of the anonymous mail system Mixminion, as well as Minx, Sphinx, Drac and Hornet; he has worked on the traffic analysis of deployed protocols such as Tor.

His current research interests focus around secure communications, high-integirty systems to support privacy, smart grid privacy, peer-to-peer and social network security, as well as the application of machine learning techniques to security problems. He has published over 70 peer-reviewed scientific papers on these topics in international conferences and journals.

He was the co-program chair of ACM Computer and Communications Security Conference in 2011 and 2012, IFCA Financial Cryptography and Data Security in 2011, the Privacy Enhancing Technologies Workshop in 2005 and 2006. He sits on the PET Symposium board and ACM CCS Steering committee and he regularly serves in program committees of leading conferences in the field of privacy and security. He is a fellow of the British Computing Society since 2014.

Web page: http://www0.cs.ucl.ac.uk/staff/G.Danezis/
Full CV: http://www0.cs.ucl.ac.uk/staff/G.Danezis/danezis-cv.pdf

Research

George’s research at the Turing Institute revolves around two key themes, or privacy and distributed ledgers. First, he research how the degree of privacy protection may be quantified and experimentally calculated for different proposed Privacy Enhancing Technologies. His approach is influenced by Differential Privacy definitions, however it is adapted to the settings and driven by experimental, rather than purely analytical evaluations. Second, he researches distributed ledgers, which are transparent and accountable distributed computational platforms. Those form the core of `blockchain’ technologies, and are challenging to scale while retaining beneficial security and governance properties

Warwick

Dr Anastasia Papavasiliou

Warwick

Bio

Dr Anastasia Papavasiliou got her PhD from Princeton University in 2002, working on stability questions for stochastic filtering and particle filters. Before coming to Warwick, she taught at Columbia University for a couple of years and spent another year in Princeton, working on efficient simulation methods for multiscale stochastic systems. Stochastic filtering, multiscale systems and, stochastic simulations are still some of her research interests, while recently she has working on applying ideas coming from the theory of rough paths to statistics.

Research

Dr Papavasiliou is interested in investigating signature-based representations of data streams as alternative ways of describing information, possibly more efficiently.

Professor David Wild

Warwick

Bio

David Wild is a computational biologist with extensive academic and industrial experience of working at the interface of the mathematical and physical sciences and molecular biology. He received a B.A. in Physics from York University, a D.Phil. in Molecular Biophysics from Oxford University, and also holds masters degrees in Mathematics and Biostatistics. He has held staff positions at the European Molecular Biology Laboratory, the Salk Institute, and has worked in industry with Allelix Biopharmaceuticals, Oxford Molecular and GlaxoWellcome. He was one of the founding faculty of the Keck Graduate Institute of Applied Life Sciences in Claremont, California, and is currently a Professor in the Department of Statistics at the University of Warwick and is affiliated to the Warwick Systems Biology Centre. His research interests encompass bioinformatics, systems and structural biology.

Research

Our research aims to build upon recent advances in algorithms for uncovering group structure in high-dimensional genomic data. A key component will be to consider techniques in high performance computing to ensure the methods developed can be feasibly applied to real data. We are exploring potential projects in the area of parallel and distributed computing applications to sequential Monte Carlo. The ability to interact with Intel’s dedicated architecture team, and utilise the Xeon Phi architecture is a unique opportunity, as is the Institute’s access to NVidia GPU systems via the Microsoft Azure servers.

David is a Turing Fellow through his supervision of Nathan Cunningham, an enrichment student at the Turing.

Dr Arshad Jhumka

Warwick

Bio

Arshad Jhumka leads the Fault-Tolerant and Reliable Systems group in the Department of Computer Science at Warwick. He received his PhD in Computer Science in 2003 from TU-Darmstadt, Germany. In 2004, he worked as a postdoctoral research associate at Chalmers University of Technology, Sweden. He joined Warwick in 2005 as a lecturer and is now an Associate Professor.

Research

His main interest is the design and validation of reliable systems, ranging from small embedded systems to large-scale distributed systems such as cluster systems. Arshad has used both formal and experimental approaches to the design of such systems but, increasingly, he has been using a data-centric approach for both the design and the validation of these systems. Dr Jhumka is interested in the application of machine learning techniques to both reliability and security issues such as intrusion detection, malware detection, design and validation of reliable SW and failure diagnosis in supercomputers, among many others.

Arshad is a Turing Fellow through his supervision of Edward Chuah, a doctoral student at the Turing.

Professor Robert MacKay

Warwick

BIO

Robert MacKay FRS CPhys FInstP CMath FIMA is a Professor in the Mathematics Institute of the University of Warwick and Director of Mathematical Interdisciplinary Research at Warwick. He was Director of Warwick’s Centre for Complexity Science from 2007-15 and President of the (UK) Institute of Mathematics and its Applications for 2012-13. He has made many contributions to the theory and applications of Nonlinear Dynamics. He has extensive research leadership and management experience.

RESEARCH

Robert proposes to develop (i) Gaussian processes to detect underdamped oscillations, with particular reference to AC electricity networks, (ii) models of financial systems to address their stability and regulation, (iii) design of assessor-assessee graphs for panel assessments to optimise the use of expertise and calibration.

Dr Suhaib Fahmy

Warwick

Bio

Suhaib Fahmy leads the Connected Systems Research Group within the School of Engineering at Warwick, where he has been Associate Professor since 2015. He graduated from Imperial College London in 2003 with an MEng in Information Systems Engineering, and in 2008 with a PhD in Electrical and Electronic Engineering. From 2007 to 2009, he was a Postdoctoral Research Fellow at CTVR, Trinity College Dublin and Visiting Research Engineer at Xilinx Research Labs, Ireland. He was an Assistant Professor in the School of Computer Engineering at Nanyang Technological University from 2009 to 2015, where he was also Deputy Director of the Centre for High Performance Embedded Systems (CHiPES).

Research

His research explores the use of reconfigurable computing systems in a connected context, from embedded to the datacenter, across a range of application domains. Working with FPGAs for well over a decade, he has explored how they can be virtualised to support more flexible exploitation of their computational efficiency and how computational capability can be added at the network interface to enhance system capabilities. At the Turing Institute, he will be investigating how this interplay between computation and communication can be exploited to address large scale problems, and how real-time analytics capability can be achieved at scale.

Professor Wilfrid Kendall

Warwick

Bio

After a Mathematics DPhil from Oxford, Wilfrid worked first at University of Hull Statistics, then University of Strathclyde Mathematics, then University of Warwick Statistics, where he served a period as head of department and is now Professor of Statistics. He also served as Scientific Secretary and later as President of the Bernoulli Society for Mathematical Statistics and Probability, and was a founding co-director of the APTS scheme for training first-year UK Statistics students. He co-authored the primary monograph on stochastic geometry (with Stoyan and Mecke and now Chiu), currently in its 3rd edition.

Research

Wilfrid is interested in research themes concerning interactions between probability, geometry and computation. These include: contributions to the theory of Markov chain Monte Carlo (most recently in applying Dirichlet form theory to study optimal scaling); developing new theoretical models for scale-invariant random spatial networks; and nascent projects concerning (a) a substantial and heterogeneous GIS database related to hypotheses concerning Anglo-Saxon building design, and (b) building and assessing a broad database on degradation of CT detector screens.

View Professor Wilfrid Kendall’s personal website: www.warwick.ac.uk/WSK

Professor Nasir Rajpoot

Warwick

BIO

Nasir Rajpoot is Professor in Computer Science at Warwick, where he started his academic career as an Assistant Professor in 2001, and the Royal Society Wolfson Research Merit Award holder since Sep 2017. He holds an Honorary Scientist position and serves as the Academic Lead for Digital Pathology Centre of Excellence at the University Hospitals Coventry & Warwickshire NHS Trust. Rajpoot is the founding Head of Tissue Image Analytics (TIA) lab at Warwick since 2012. The focus of current research in his lab is on developing novel computational pathology algorithms for improved cancer diagnostics and better stratification of cancer patients.

 

RESEARCH

Modern day slide scanners are capable of generating large microscopic resolution images of conventional tissue slides, spurring a revolution in the practice of cellular pathology as a discipline. This development comes at a time when computing capacity and machine learning technologies are peaking, offering a remarkable opportunity to reveal complex cellular patterns in a data-driven manner. Rajpoot’s research capitalises on this opportunity to seek answers to questions like: Can we develop novel efficient image based measures of the ‘state of play’ of complex diseases such as cancer? And can we use such measures to further our understanding of cancer and predict the progression and outcome of cancer?

Professor Thomas Hills

Warwick

BIO

Thomas Hills is a Professor of Psychology at the University of Warwick. His initial training was in the neurogenetics and mathematical characterization of animal foraging at the University of Utah Biology Department.  He subsequently worked with the University of Texas at Austin College of Education and Indiana University Department of Psychological and Brain Sciences, most recently working with the University of Basel before moving to his present postion.

RESEARCH

My research focuses on the quantitative description of cognitive information, such as the semantic structure of memory, the evolution of language, psychological attitudes towards concepts such as risk and immigration, and patterns of behaviour and language symptomatic of beliefs and underlying pathology.

Dr Weisi Guo

Warwick

Bio

Dr. Weisi Guo graduated from the University of Cambridge with degrees in General Engineering and Computer Science. He has worked for 2 years in the telecommunications industry and has been a faculty staff at the University of Warwick for 5 years. In his private life, his experiences include working in UNHCR refugee camps, solo climbing high peaks, and have completed both the London and Sahara full marathons.

Research

His research expertise is in Information and Networks. In terms of applications, he is particularly interested in interdependent critical infrastructures and urban analytics for defence and security. His pioneering research in molecular signalling and complex networks has won him a number of awards & nominations (IEEE, IET, and Bell Labs). At the Turing Institute, he is a Group Leader in the Program on Data-Centric Engineering.

Professor Graham Cormode

Warwick

Bio

Graham Cormode is a Professor in Computer Science at the University of Warwick and The Alan Turing Institute’s University Liaison Director for Warwick. He works on research topics in data management, privacy and big data analysis. Previously, he was a principal member of technical staff at AT&T Labs-Research in the USA.  He serves as an associate editor for ACM Transactions on Database Systems (TODS), and the Journal of Discrete Algorithms.

Research

Graham will work on a variety of topics at the Turing including:

  • Algorithms for scalable analytics & streaming, sketching, dimensionality reduction, compressed sensing, with applications to internet scale data, vehicle data.
  • Data Anonymization and privacy & statistical and cryptographic approaches to privacy, primarily differential privacy, with applications to telecommunications and social data.
  • Distributed algorithms & algorithms for large scale monitoring and logging of activities, drawing on ideas from approximation and distributed ledger technologies.

Professor Saul Jacka

Warwick

BIO

After a PhD from Cambridge Statistical Laboratory, Saul trained as an actuary and retains strong links with the profession. He has been a Professor of Statistics at Warwick since 2003.
He won two Merrill Lynch Best Paper prizes in Mathematical Finance, is a member of Scientific Steering Committee of the Isaac Newton Institute and editor of the probability journal, Stochastics

 

RESEARCH

Saul specialises in probability, stochastic control and finance.

He is currently working on stochastic control, optimal stopping and applications to finance, anomaly detection and numerics for non-linear pdes and multi-level Monte Carlo (MLMC) simulation.
He is increasingly focusing on high-dimensional and analytically intractable control problems via the Policy Improvement Algorithm, approximations thereof and related numerical tools.

Dr Anthony Lee

Warwick

BIO

Anthony Lee has been an Assistant Professor of Statistics at the University of Warwick since 2013. He received BSc. and MSc. degrees in Computer Science from the University of British Columbia, and a DPhil. in Statistics from the University of Oxford in 2011. He was a Centre for Research in Statistical Methodology Research Fellow from 2011 to 2013.

 

RESEARCH

His research is mainly in the area of stochastic algorithms for approximating intractable quantities that arise in data analysis. Examples of such algorithms are Markov chain and Sequential Monte Carlo. He works on both theory and methodology, with a focus on algorithms that scale well in parallel and distributed computing environments. Research in this area is interdisciplinary, bringing together advances in applied probability, algorithms, architecture and statistics. He is also interested in computational and statistical trade-offs when conducting inference.

Professor Chenlei Leng

Warwick

Bio

Chenlei obtained his BSc in mathematics from the University of Science and Technology of China and his PhD in statistics from the University of Wisconsin-Madison. He held regular and visiting faculty positions in China, Germany and Singapore before joining the University of Warwick as a professor of statistics in 2013.

Research

Chenlei is a statistician working mainly on developing novel statistical methods for analysing complex data. His recent research interests have been focused on high-dimensional data analysis, correlated data analysis, network data analysis and statistical learning. His works have found applications in medicine, biology, engineering, social sciences and so on. As a faculty fellow, he looks forward to the opportunity to work on projects where statistical analysis can be useful.

Dr Filip Rindler

Warwick

BIO

Filip Rindler is currently Associate Professor in Mathematics at the University of Warwick, where he has been since 2013. After completing his doctorate in nonlinear PDE theory and the calculus of variations at the OxPDE centre within the University of Oxford in 2011, he moved to the University of Cambridge to take up the Gonville & Caius College Drosier Research Fellowship, holding the position until 2015 (on leave 2013-2015). For 2014-2017 he is funded by a full-time EPSRC Research Fellowship. Originally a computer scientiest, he will aim to contribute to the ATI’s activities at the interface between theory and implementation.

 

RESEARCH

He is interested in variational / optimization problems, interpreted broadly. He is particularly interested in theoretical aspects, but also in applications to material science, physics, and economics. One particular aspect of his research to date has centered around generalized solutions to minimization problems that do not have a minimizer, but whose approximate solutions develop finer and finer oscillations or concentrations as one approaches the minimum. Describing and analyzing such situations often relies on advanced tools, such as Young measures, and he has contributed several results and techniques in this area.
Recently, he has also been interested in anomaly detection problems.

Dr Krys Latuszynski

Warwick

Bio

Krys Latuszynski is a Royal Society University Research Fellow and Jeff Harrison Early Career Professor of Statistics at the University of Warwick. Prior to this, he was a Research Fellow at the Universities of Warwick and Toronto. He graduated from the University of Warsaw (BSc, MSc, PhD in mathematics) and Warsaw School of Economics (BSc, MSc in econometrics).

Research

His research is in the area of computational statistics with particular focus on design and analysis of Markov chain Monte Carlo and related algorithms for high dimensional problems and unbiased inference for intractable likelihood problems, in particular diffusion process.

Dr Matthew Leeke

Warwick

BIO

Dr Leeke is an Associate Professor and Director of Admissions and Recruitment within the Warwick University Department of Computer Science.

 

RESEARCH

His research addresses a variety of issues relating to the design, implementation and evaluation of dependable systems. He is particularly interested in approaches for the design and location of efficient error detection mechanisms, as well a privacy issues in emerging areas such as wireless sensor networks.

Dr Sebastian Vollmer

Warwick

BIO

He obtained his MSc in Mathematics from the University of Warwick in 2010.  He remained at Warwick to complete his PhD with Profs Andrew Stuart and Martin Hairer, finishing in 2013.  Since October, he is a postdoc working with Profs Arnaud Doucet and Yee-Whye Teh.

 

RESEARCH

His research interests lie at the interface of probability theory and applied mathematics. In particular, he is focusing on the convergence of stochastic processes to their equilibrium. His main area of research is sampling algorithms where the aim is to draw approximate samples from a distribution in order to approximate an expected value. With these methods it is possible to make inference in the areas such as Bayesian statistics, Bayesian inverse problems, molecular dynamics and statistical mechanics. Furthermore, he is interested in dynamical systems and decision theory and he is curious in the handling and use of large data sets.

Collaborating Fellows

Dr Tomas Petricek

Collaborating Fellow

BIO

Tomas is an collaborating Turing Fellow at the Alan Turing institute, working on data-driven storytelling. He is building tools that integrate with modern data sources (open government data, data published by citizen initiatives) and let users easily create analyses and visualizations that are linked to the original data source, making the analyses more transparent, reproducible, but also easy to adapt. His early work on the project can be found at http://thegamma.net.

RESEARCH

Tomas’ many other interests include open-source and functional programming (he is an active contributor to the F# ecosystem), programming language theory (his PhD thesis on “coeffects” develops a theory of context-aware programming language language), but also understanding programming through the perspective of philosophy of science.

Professor Michael Farber

Collaborating Fellow

Bio

Michael Farber is a mathematician at Queen Mary University of London and is interested in mathematical problems arising in engineering (robotics) and statistics (data analysis, statistical shape theory etc). In his work he studied topology of various configuration spaces and their applications to problems of robotics (linkages) and to the theory of convex billiards. He is also interested in problems of stochastic topology; the aim is to understand geometric and topological structure of large random spaces which are created by many independent random choices.

Research

He intends to study problems of data analysis and various relevant mathematical techniques.

Michael is a Turing fellow through his supervision of Lewin Straus, an enrichment student at the Turing.

Dr Christopher Yau

Collaborating Fellow

Bio

Christopher is a Reader in Computational Biology at the University of Birmingham. He received his undergraduate degree in Engineering at the University of Cambridge in 2004 and a D.Phil in Statistics from the University of Oxford in 2009. He was previously a Lecturer in Statistics at Imperial College London and Associate Professor in Genomic Medicine at the University of Oxford.

Research

His research is focused on issues related to the interpretation of high-dimensional data arising from modern molecular technologies and health systems and how such data can be used to give insights into the molecular basis of human disease particularly cancer. His efforts in this area span a spectrum of areas from core statistical and machine learning methodological research to wet lab-based experimental investigations to translational clinical research. He currently co-leads the Machine Learning Clinical Interpretation Partnership as part of the Genomics England 100,000 Genomes Project.