Research Fellows

Employed by our Joint Venture Universities, the inaugural Alan Turing Institute Research Fellows will pursue research based at the Institute hub for the next 3-5 years.

Profiles for each Research Fellow are below.

Brooks Paige

Cambridge

BIO

Brooks Paige moved to the UK in 2013 to undertake a D.Phil. in Engineering Science at the University of Oxford. He previously lived in New York City, where he completed an M.A. in Statistics at Columbia University and worked professionally for several years as a software developer and web designer. In the distant past, he was an undergraduate at Amherst College, and remains a strong proponent of a liberal arts science education. http://www.robots.ox.ac.uk/~brooks/

 

RESEARCH

The emerging field of probabilistic programming aims to reduce the technical and cognitive overhead for writing and designing novel probabilistic models, by introducing a specialized programming language as an abstraction barrier between modeling and inference. Brooks’ research focus is on developing general-purpose Bayesian inference algorithms which can be applied automatically to generative models written as probabilistic programs, with a particular emphasis on sequential Monte Carlo methods and scalable variational inference.

Dong Nguyen

Edinburgh

BIO

Dong Nguyen has a master’s degree from Carnegie Mellon University and holds a PhD from the University of Twente. She has interned at Google, Microsoft Research and Facebook. She works on text mining methods that can help answer questions from the social sciences and the humanities. Her research was featured in the New York Times and Time Magazine. http://www.dongnguyen.nl/

 

RESEARCH

The rise of social media brings exciting opportunities to study social phenomena through large-scale text analysis. So far, the dominant approach for text analysis of social media data is based on the concept of language as a means to convey information. However, people also use language to construct their identities, and to build and maintain social relationships. In her research, she focusses on using computational models to study the social dynamics of language use in social media.

Hemant Tyagi

Edinburgh

BIO

Hemant Tyagi completed his PhD at the Institute of Theoretical Computer Science, ETH Zurich, in 2016. Earlier, he obtained a Masters degree in Communication Systems at EPFL in 2011, and a Bachelors degree in Electrical & Electronics Engineering at NIT Surathkal, in 2006.

 

RESEARCH

Hemant’s research interests broadly revolve around the areas of: low dimensional models for high dimensional data, compressive sensing, approximation theory and high dimensional statistics. He is also interested in (non-) convex optimization as well as online optimization (multi armed bandits etc.).

Armin Eftekhari

Edinburgh

BIO

Armin Eftekhari received his PhD from Colorado School of Mines in 2015, under the supervision of Michael Wakin. Before joining the Alan Turing Institute, he was an ICES postdoctoral fellow at the University of Texas at Austin.

 

RESEARCH

The late statistician George Box once wrote that “all models are wrong, but some are useful.” Indeed, to make sense of data, we must necessarily impose some structure upon it. For example, we often assume that digital data is collected from bandlimited sources. Or, in compressive sensing, a small number of measurements suffice to reconstruct a sparse signal. Making inferences from (often noisy, incomplete, and high-dimensional) data by imposing appropriate structures has been the main theme of Armin’s research, and he hopes to continue this line of work at the Alan Turing Institute, with more emphasis on practical aspects.

https://sites.google.com/site/armineftekhari

Mihai Cucuringu

Oxford

BIO

Mihai received his PhD in Applied and Computational Mathematics (PACM) at Princeton University in 2012, supervised by Amit Singer. His thesis was on the low-rank matrix completion problem and several distance geometry problems with applications to sensor network localization and three-dimensional structuring of molecules.  During 2013-2016 he was a CAM Assistant Adjunct Professor in Computational Applied Mathematics at UCLA. During Fall 2014, he was a Research Fellow at the Simons Institute for Theory of Computing at UC Berkeley, in the program Algorithmic Spectral Graph Theory.

 

RESEARCH

Mihai’s research interests concern the development and mathematical analysis of algorithms for large networks, certain inverse problems on graphs, and big data analysis, with applications to various problems in engineering, machine learning, finance, and biology. Particular areas of interest are spectral and SDP-relaxation algorithms and applications, the group synchronization problem, ranking from noisy pairwise comparisons, lead-lag relationships in multivariate time series, clustering, core-periphery structure in networks, multiplex networks, dimensionality reduction and diffusion maps (with an eye towards heterogeneous data and nonlinear time series), spectral algorithms for analysis of signed graphs and correlation networks. The above problems share an important feature: they can all be solved by exploiting the spectrum of their corresponding graph Laplacian. Mihai’s web page: http://www.stats.ox.ac.uk/~cucuring/

Efthymia Tsamoura

Oxford

BIO

Efthymia Tsamoura received her BSc in 2007 and her PhD in 2013 with honours both from the Computer Science Department of Aristotle University of Thessaloniki, Greece. Since June 2013 she is a postdoctoral researcher in the Computer Science Department of University of Oxford, working on problems related to querying semantically interrelated resources. Her research interests lie in the fields of data integration, query answering under constraints, distributed query optimization, multi-objective query optimization and grid computing. Efthymia received several distinctions and awards both during her undergraduate and postgraduate studies.

 

RESEARCH

Today a vast amount of bioinformatics data resides on the web. Yet the usefulness of these resources is limited by the difficulty in harnessing the data to answer user information needs. In collaboration with researchers from Turing, the University of Oxford, and the European Bioinformatics Institute (EBI), she will create tools and techniques enabling biologists to more easily integrate web-based data into their activities. This will involve building systems that allow scientists to use the plethora of web-based data without expertise in the resources that want to access. While making the use of data simpler, the tools that she will create will also make them run more efficiently, by giving scientists access to the power of large-scale infrastructure.

Vidit Nanda

Oxford

BIO

Dr Vidit Nanda is an applied and computational algebraic topologist. Before starting at the Alan Turing Institute, he worked a post-doctoral research fellow at the University of Pennsylvania and as a PhD candidate in mathematics at Rutgers University. http://www.sas.upenn.edu/~vnanda/

 

RESEARCH

Dr Nanda develops algebraic-topological theories, algorithms and software for the analysis of non-linear data and complex systems arising in various scientific contexts. In particular, he employs discrete Morse-theoretic techniques to substantially compress cell complexes built around the input data without modifying core topological properties. His recent work has involved excursions into computational geometry, cellular sheaf theory and higher-categorical localization.

Matt Kusner

Warwick

BIO

Matt J. Kusner was a visiting researcher at Cornell University under the supervision of Kilian Q. Weinberger and received his Ph.D. in Machine Learning from Washington University in St. Louis. His work is in the areas of privacy, budgeted learning, model compression, and Bayesian optimization. He is from Iowa City, Iowa, USA and is married to the wonderful Sonia Rego.

 

RESEARCH

Matt’s research aims to address the disconnect between state-of-the-art machine learning models, and models that are often used to solve real-world problems. Frequently, in real-world settings the modeler is confronted with a trade-off between maximizing an objective (e.g., returning more accurate results) and minimizing a budget (e.g., producing predictions in under a millisecond). His research considers three specific types of budgets: time, space, and privacy. These feature in recommendation, face recognition, bankruptcy prediction, stock market modeling, and real-time machine translation. Directly addressing these real-world trade-offs at an optimization level results in algorithms that are simultaneously practical and accurate.

Nathanaël Fijalkow

Warwick

BIO

Nathanaël Fijalkow obtained his PhD in 2015 in Computer Science, entitled “Counting and Randomising in Automata Theory”, jointly awarded by Paris 7 and Warsaw University under the supervision of Thomas Colcombet and Mikołaj Bojańczyk. He joined the University of Oxford in 2016 as a Research Assistant in the Verification group. His research interests are in Logics, Automata Theory, Games, Stochastic Systems, Formal Methods, Verification and Complexity.

 

RESEARCH

Nathanaëls Research Fellowship at the Alan Turing Institute will allow him occasion to broaden his perspectives; he plan to work on Streaming Algorithms, Robustness for Stochastic Systems, Probabilistic Databases and Machine Learning. His ambition is to provide theoretical foundations for these questions, and to demonstrate their values through practical applications.

Catalina Vallejos

UCL

BIO

Catalina Vallejos is a Bayesian statistician. She graduated from the University of Warwick (PhD in Statistics, 2014) and from the Pontificia Universidad Catolica de Chile (BSc and MSc in Statistics, 2010). Before joining the Turing, she worked as a postdoctoral researcher on statistical genomics at the MRC Biostatistics Unit and the EMBL European Bioinformatics Institute.

Catalina’s website: https://sites.google.com/view/catalinavallejos/home

 

RESEARCH

Catalina’s research at the Turing will focus on applied Bayesian statistical methodology, with emphasis on the development of open-source analysis tools for high-dimensional datasets with complex structure. Within a Bayesian hierarchical modelling framework, she will develop integrative methods to combine information from different data sources and data types, to disentangle multiple interacting sources of variability. Her work will serve as a bridge between advanced statistical methods and applied areas of research, including strong collaborations with biologists and other scientists.

Barbara McGillivray

Cambridge

BIO

Barbara McGillivray is a computational linguist. She holds a degree in Mathematics and one in Classics for the University of Florence (Italy), and a Ph.D. in Computational Linguistics from the University of Pisa (2010). Before joining the Turing Institute, she worked as a language technologist in the Dictionary division of Oxford University Press and as a data scientist in the Open Research Group of Springer Nature.

RESEARCH

Barbara’s research at the Turing Institute will focus on computational models for semantic change. She will develop machine-learning models for the change in meaning of words in Latin and English. Her contribution will be interdisciplinary in nature and it will range from Data Science and Natural Language Processing to Historical Linguistics and other humanistic fields, thus pushing the boundaries of what academic disciplines separately have achieved so far on this topic. This research is relevant to current data as well, with potential applications ranging from lexicography to sentiment analysis, information retrieval, question answering systems, dialogue systems, and machine learning.

Adria Gascon

Warwick

BIO

Adria Gascon is a computer scientist with research interests in formal languages and compression, automated reasoning, cryptography, and machine learning.  He earned his Phd. from the Technical University of Catalonia, and has held positions at SRI International and the University of Edinburgh.

RESEARCH

Data is a valuable asset, as it is crucial to many decision making processes.  In this paradigm, privacy is an important aspect of each component of a data analysis pipeline.  Similarly to general security, privacy has proven to be a slippery concept, that requires a robust, mathematically rigorous approach.
Adria‘s research focus is on finding efficient solutions to the problem of computing on private data, that provide formal guarantees regarding information disclosure.  This research touches on several areas of expertise and application domains, such as machine learning and statistics, cryptography, formal methods, databases, and systems security.

Stephen Law

UCL

BIO

Stephen Law studied economics in Canada and urban design in the UK. His research interest focuses on the intersection of two, both in consultancy at Space Syntax Limited and his doctoral research in UCL examining the economic value of spatial networks through the London housing market.

RESEARCH

Stephens research proposal aims to answer the following research question: “What is the economic value of urban design?” Recent advances in citizen science and geo-data computation, present the possibility of analysing the collective perception of urban design with large volumes of crowd-source data and machine learning techniques. This research therefore proposes a novel approach for collecting and measuring the perception of urban design and in retrieving its economic value. To facilitate this objective, the vision and the ambition of the research is to build an open source toolkit known as the “Urban-Value Toolkit” where future urban design projects can be evaluated.

Kirstie Whitaker

Cambridge

BIO

Kirstie discovered the wonder of brain imaging at the University of British Columbia during a masters degree in Medical Physics. She completed a PhD in Neuroscience at the University of California, Berkeley in 2012 and joins the Turing Institute from a postdoctoral fellowship at the University of Cambridge in the Department of Psychiatry. She is an Fulbright scholarship alumna and 2016/17 Mozilla Fellow for Science. Kirstie was named, with her collaborator Petra Vertes, as a 2016 Global Thinker by Foreign Policy magazine.

RESEARCH

Adolescence is a period of human brain growth and high incidence of mental health disorders. Kirstie‘s research uses magnetic resonance images to understand changes in the brain’s structure and function that underlie the emergence of schizophrenia and depression. Her work considers the brain as a network and investigates how different brain regions work together. She is particularly passionate about ensuring that work is reproducible and can be replicated in independent data sets. Her focus at the Turing Institute is to improve the generalisability of research findings so they may be translated to the clinic and used by public health policy makers.

Brent Mittelstadt

UCL

BIO

Dr. Brent Mittelstadt is a Research Fellow at the Alan Turing Institute and University College London. His research addresses the ethics of algorithms, machine learning, artificial intelligence and data analytics (‘Big Data’). Over the past five years his focus has broadly been on the ethics and governance of emerging information technologies, including a special interest in medical applications.

 

Research

Dr. Mittelstadt’s research focuses on ethical auditing of algorithms, including the development of standards and methods to ensure fairness, accountability, transparency, interpretability and group privacy in complex algorithmic systems. His work addresses norms and methods for prevention and systematic identification of discriminatory and ethically problematic outcomes in decisions made by algorithmic and artificially intelligent systems. A recent paper on the legally dubious ‘right to explanation’ and the lack of meaningful and accountability and transparency mechanisms for automated decision-making in the General Data Protection Regulation, co-authored with Dr. Sandra Wachter and Prof. Luciano Floridi, highlights the pressing need for work in these areas.

Daphne Ezer

Warwick

Bio

Daphne earned a double BSc in Computer Science and Biology at Duke University as an Angier B. Duke Scholar, and then went on to earn a PhD modelling stochastic gene expression in the University of Cambridge as a Marshall Scholar. She has also conducted biological network inference and bioinformatics research in the Sainsbury Laboratory in the botanic gardens of the University of Cambridge.

Research

Daphne wants to use data science to develop sustainable agriculture in the light of climate change. In particular, she is interested in network inference algorithms to detangle how plants integrate environmental signals to determine how much to grow. She is also interested in computer-aided experimental design—i.e. systematically identifying the most informative biology experiments to conduct next, given the cost and time constraints. Finally, she is helping organise a large crowdsourcing project that aims to identify how fluctuations in light and temperature affect spring onion growth.

Alex Shestopaloff

Edinburgh

Bio

Alex received his PhD in Statistics from the University of Toronto in 2016, with a thesis on Markov Chain Monte Carlo (MCMC) methods for Bayesian inference in complex stochastic models. Alex’s research was supervised by Prof. Radford M. Neal.

Research

Alex plans to continue my research in MCMC and related methods to further improve their efficiency and broaden their applicability. In particular, Alex is interested in looking at non-reversible MCMC methods as well as how to scale MCMC to large datasets via parallel computation.

Dr Annie Louis

Edinburgh

BIO

Annie Louis is a Research Associate at the Institute for Adaptive and Neural Computation at the University of Edinburgh. Previously, she was a Newton International Fellow at the University of Edinburgh funded by the Royal Society and the British Academy, and also spent one year teaching at the University of Essex. She obtained her Ph.D. from the University of Pennsylvania in 2013, and is the recipient of a Best Student Paper Award (SIGdial 2010) and a Best Paper Award (EMNLP-CoNLL 2012). She currently (2017-2018) serves on the scientific advisory committee of SIGdial (Special Interest Group on Discourse and Dialog).

RESEARCH

Annie’s research interests are in natural language processing, and machine learning. Her work focuses both on understanding how people use language, as well as the application of technology to solve problems in information retrieval, social media analysis, and software engineering. In particular, she is interested in modeling how documents and conversations are structured, what impressions are made by texts on readers and listeners, and conversely, what we can learn about people from what they have said or written. At the Turing, she works on language understanding and generation, specifically trying to combine language semantics with the context and environment in which the language is produced.