Doctoral Students

Adilet Otemissov

Oxford

Bio

Adilet is a first year doctoral student from Oxford University. He graduated from Nazarbayev University with a BSc in Mathematics and then pursued a master’s degree at The University of Manchester. His research interests include combinatorial and non-convex optimisation.

Research

Adilet’s PhD research will be dedicated to finding more efficient algorithms for the most difficult of optimisation problem classes, namely, that of global optimisation, where the best optima is desired amongst possibly-many local solutions in a highly nonlinear landscape, and often in large dimensions. The grand challenge is scalability of algorithms for global optimisation, as the state of the art software can generally solve global optimisation problems in the order of ten parameters. This project plans to explore modern techniques from compressed sensing, signal processing and machine learning – where techniques have been devised to find structure/important information in large data sets – to effectively reduce the dimension of global optimisation problems so that they can be solved efficiently. This is a little explored direction of research with great potential for impact in terms of algorithm development and transformative applications.

Alex Bird

Edinburgh

Bio

Alex is a first year PhD student registered at Edinburgh, supervised by Chris Williams. His research interests lie primarily in graphical models and approximate inference.

Research

Alex will be working on time series models for disambiguating artifactual signal(s). This will be a continuation of a project by Chris Williams and Konstantinos Georgatzis. The motivation stems from a collaboration with the Queen Elizabeth University Hospital in Glasgow, with the current ambition of predicting drug response in physiological monitoring data.

Andreas Grammenos

Cambridge

Bio

Andreas was born in Athens, the capital of beautiful Greece in the late 80’s and formally obtained his Degree in Electronic & Computer Engineering from Technical University of Crete in 2015. He joined University of Cambridge as a first year PhD student in October 2016.

Research

Andreas’ thesis revolves around the challenges involved when dealing with data at a massive scale which are primarily sourced from mobile devices. Finally, his general research interests include the broad field of Data Analytics which is an interesting intersection of computer science, systems programming and mathematics.

Corinne Cath

Oxford

Bio

Corinne J.N. Cath worked at the intersection of Internet governance, tech-policy, and ethics for various years. She joined the Oxford Internet Institute in 2015 as an MSc student. Her MSc research at the OII focused on the (im)possibility of instantiating human rights in Internet standards. Before Oxford she was a policy advisor for a Democratic Congressman in Washington D.C. After graduating from the OII she worked for human rights NGO ARTICLE 19. Corinne has a BA in anthropology, MA in International relations, and MSc in Social Science of the Internet.

Research

Corinne is currently a first-year DPhil (PhD) student at the University of Oxford. Her main research areas are the governance of the Internet’s technical infrastructure, and the related issues of Internet fragmentation, “responsibility-by-design”, and data ethics. Corinne’s other research interests include Artificial Intelligence, private-public cooperation in cybersecurity policy development, and the politics of Internet Governance organisations. She is working on these issues under the guidance of Professor Luciano Floridi.

Daniel Wilson-Nunn

Warwick

Bio

Daniel has recently graduated from the University of Warwick with an integrated master’s degree in mathematics and statistics. He is now starting a PhD in statistics registered at the University of Warwick and based at the Turing. His areas of interest are rough path theory and machine learning, with particular application to sound.

Research

Rough path theory is a method of analysis of SDEs driven by highly oscillatory processes, such as Brownian motion. Recent developments in the theory of rough paths have opened the doors for ideas and concepts developed for studying rough paths to be used in a wide variety of applied situations, including the recognition of Chinese handwriting and financial modelling. Combining the rich theory of rough paths to the rapidly growing field of machine learning and big data, the aim of Daniel’s project is to develop tools for sound analysis, recognition and compression.

David Butler

Edinburgh

Bio

David is entering The Alan Turing Institute as a first year doctoral student. He is working with his supervisor, David Aspinall, registered with the University of Edinburgh. David’s research interests are in security and privacy. In particular he is working on the formalisation of the proof of security of cryptosystems and their protocols.

Researcher

As cryptographic protocols become more complex it is becoming more difficult to prove that they are ‘secure’. That is to say, it is becoming very hard to ensure no secret information is leaked to an adversary that attacks the protocol. This increasing complexity has meant that proving security of these protocols by hand is becoming unfeasible; human mistakes and errors creep in. Consequently attempts are being made to formalise these security proofs on machine. David is currency working on formalising a general two party protocol scheme using the proof assistant, Isabelle, with particular interest in working with the Oblivious Transfer protocol.

Edward Chuah

Warwick

Bio

Edward commenced his doctoral studies at the University of Warwick in October 2016. His general research interests are Reliability, fault/failure diagnosis, detection and prediction, anomaly detection and clustering, correlation and inference, large clusters and distributed systems, networking.

Research

Recent research has demonstrated the value of combining message logs and resource use data for cluster system failure diagnosis. However, there is little work that combines analysis of patterns in message logs and resource use data to diagnose performance failures in cluster systems.  Cluster systems have increasingly generated a massive amount of monitoring data which can be used to analyse these failures.  Edward’s proposed research will study the nature and characteristics of system performance failures, use statistical and machine learning techniques to identify the causes of performance failures, develop new data-processing methodologies, implement tools for prototyping on production cluster systems.

 

Helen Oliver

Cambridge

Bio

Helen Oliver is beginning her doctoral studies at The Alan Turing Institute after three years as a Research Associate at the University of Cambridge Computer Laboratory. She has been a researcher since 2008, and her research interests have converged at the intersection of wearable technology, narrative, privacy and innovation in the Internet of Things.

Research

Helen’s project focuses on the research through design of wearable technology. As more users adopt wearable technology, more data of an ever more personal nature will be generated, raising urgent questions about how that data is handled and who benefits. At the same time, the HCI aspect of these devices is endangering mainstream acceptance: there is a lack of variety in the forms and functions of wearable devices, and a need to integrate wearables into IoT ecosystems. Helen’s research seeks concrete solutions for wearable technology, and the data it generates, to benefit the individual user and society as a whole.

 

Javad Hosseini

Edinburgh

Bio

Javad started his PhD at University of Edinburgh in Autumn 2016. His research interests are in developing novel Machine Learning techniques to solve Natural Language Processing problems, especially in the area of semantics.

Research

In particular, Javad’s interested in detecting the various statements that are made about the same identifiable entities of various types. He will use the datelines associated with the original stories to detect causal relations between event-types, as well as relations of paraphrase and entailment. The resulting semantics will be built into a semantic parser, which will be evaluated on standard datasets for question answering and text-entailment.

Hilmi Majid

Warwick

Bio

Hilmi’s a first-year PhD student and started in October 2016 at the University of Warwick under the supervision of Professor Chenlei Leng. Previously he studied at the University of Auckland, NZ and University of Malaya, Malaysia. His background is in Mathematics and Statistics and his current research interest includes sports data analysis, networks and machine learning from statistical point of view.

Research

In a sports league, each team may face each other a finite number of times. Based on the results, we will be able to come out with a model that predicts the winning probability of a team against another specific team, for example by using the Bradley-Terry model. It is of interest to generalise this model to include interaction terms that may affect the outcome of the match, for example whether the team is playing as home or visitor. In his research, Hilmi will be focusing on this type of problem and extending it for more complex cases.

Nicole Peinelt

Warwick

Bio

Nicole Peinelt is currently a first year doctoral student at the Department of Computer Science at the University of Warwick and The Alan Turing Institute with a special interest in Natural Language Processing. Prior to that, she studied Chinese language and Computational Linguistics in Germany, China and Taiwan.

Research

Nicole’s research interests include information retrieval and computer-assisted language learning. Her doctoral work explores machine learning algorithms for community-based question answering and text summarisation. She is particularly interested in applying these methods for real-world educational purposes.

Odysseas Sclavounis

Oxford

Bio

Odysseas is a graduate of King’s College London and of the University of Oxford. He recently completed his masters degree at the Oxford Internet Institute. His thesis was on reputation stability on dark net marketplaces. Odysseas has now progressed onto the DPhil program at the Oxford Internet Institute under the supervision of Vili Lehdonvirta.

Research

At The Alan Turing Institute, Odysseas will be studying how public blockchains such as Bitcoin are governed. The ongoing blocksize debate and the DAO fallout, show how contentious the governance of these socio-technical systems has been.

Shouto Yonekura

UCL

Bio

Shouto studies at the Department of Statistical Science, at University College London. His current research interests are statistical methods for stochastic differential equations with discrete observations.

Research

Shouto’s current research interests include studying model selection methods for diffusion-type models with discrete time observations. Although empirical studies using diffusion-type models have been exponentially increasing, there are quite a few studies on model selection methods for these models. This is  basically because the likelihood functions of diffusion-type models with discrete time observations are intractable. To overcome this difficulty, Shouto’s research seeks to develop Sequential Monte Carlo methods (a special case of Feynman-Kac Formulae) to make it possible to construct model selection methods for diffusion-type models with discrete time observations. He is also interested in Sequential Monte Carlo methods for parameter estimation.

Vidhi Lalchand

Cambridge

Bio

Vidhi is a first year PhD student at the University of Cambridge, Department of Physics, Laboratory for Scientific Computing.

Prior to this she completed a MPhil in Scientific Computing from Cambridge (2016) and a M.Sc. in Applicable Mathematics from the London School of Economics and Political Science (2010) securing Distinction in both. Before joining Cambridge in 2015 she worked as a quantitative analyst at Credit Suisse and as a high frequency trader at the Chicago based hedge fund, Citadel Securities (Europe) in London between 2011 and 2015. Her supervisors are Dr. Anita Faul and Dr. Christopher Lester from Cambridge.

Research

Her research can be broadly categorised as supervised machine learning and its applications. On the theoretical side she is developing a non-parametric framework for supervised learning which uses Bayesian inference. Bayesian inference is a sub-field of machine learning which entails learning from data in a way that is inherently probabilistic. The result of the “learning” exercise is to make predictions on unseen data in a continuous context (regression) or categorical labels (classification). Prediction in Bayesian inference returns a probability distribution rather than a single point estimate, in this sense the former accounts for uncertainty in predictions.

A measure of statistical guarantee or confidence in machine learning predictions is becoming an increasingly important requirement for their usage in several industrial applications where the cost of false positives is really high. For instance, in medicine where the task is to decide whether to operate on a patient or not based on a classification exercise of cancerous concentrations of cells, the probability of a correct prediction is extremely important. Bayesian nonparametrics is the only current paradigm that allows the user to stipulate a posterior belief or a prediction in terms of a probability.

She is equally interested in the potential of existing machine learning methodologies to data driven problems in particle physics, astronomy, quantitative finance and medicine. While still in early stages and subject to modifications, she has titled her thesis – “Probabilistic Kernel Machines: Theory and Applications” to encompass the full extent of work under her PhD.

Chanuki Seresinhe

Warwick

Bio

Chanuki Illushka Seresinhe is a third year doctoral researcher from the Data Science Lab, Warwick Business School. Chanuki’s research interest entails using online data from such sources as Flickr and Google Street View to understand how the aesthetics of the environment impacts human wellbeing. Prior to returning to university, Chanuki worked in the digital design sector for over ten years.

Research

Using data from Scenic-Or-Not, a website that crowdsources ratings of “scenicness” of 1 km grid squares of Great Britain, combined with data from the Census, we find that residents of more scenic environments report better health (see Quantifying the Impact of Scenic Environments on Health, Seresinhe, Preis & Moat, 2015) even when taking core socioeconomic indicators, such as income, into account.

However, visual environments can vary dramatically throughout 1km grid squares, especially in cities. This research project will apply deep learning techniques to images of urban locations, in order to explore which visual features of the environment might impact the well being of city residents.

Pankaj Pansari

Oxford

Bio

Pankaj is a second year PhD student in the Engineering Science department at University of Oxford and an enrichment year student at The Alan Turing Institute. His research interests lie in optimisation and machine learning. At Oxford, he is a member of the OVAL group under Prof. Pawan Mudigonda.

Research

Pankaj’s current project is on the optimisation of convolutional neural network (CNN) architecture. The multi-layer topology of CNNs with hierarchical feature representation has been an important factor for their success in computer vision applications. However, there is still a lack of a principled approach to selecting the right architecture for a given problem. In this project, his research group attempts to present a new optimisation framework for simultaneously learning both the CNN structure and the network weights for a given problem. They aim to later extend the method to recurrent neural networks.

 

Elena Kochkina

Warwick

Bio

Elena is a second-year PhD student in Computer Science at the University of Warwick supervised by Professor Rob Procter and Dr. Maria Liakata. Her research interests are Machine Learning, Data Mining, Deep Learning, Natural Language Processing, Social Media, Multi modal Learning.

Research

Elena’s PhD project is focused on integrating text and image data for content verification and opinion mining in social media. The motivation for this is the fact that in recent years images in social media (e.g. Twitter) have become a very powerful tool to transfer information and express emotions. Her research seeks to combine information extracted from images and text to gain insights on people’s stance towards various issues and to understand phenomena such as rumour spread and opinion formation.  She is interested in the application of Recurrent Neural Networks and Multi modal Distributional Semantic Models for this purpose.

Abhinav Mehrotra

UCL

Bio

Abhinav is a third year computer science PhD student at the University of Birmingham. He works under the supervision of Mirco Musolesi and Robert Hendley. His main areas of interest include social computing, context-aware computing, digital health and human behaviour modelling.

Research

Abhinav’s current research focuses on understanding and predicting human behaviour for interacting with mobile devices by using contextual information obtained from the embedded sensors. He also work on the projects related to monitoring and predicting the cognitive context (such as mood) as well as the health of people through the analysis of mobile phone data.

Adam Tsakalidis

Warwick

Bio

Adam is a third-year PhD candidate in Urban Science with the Warwick Institute for the Science of Cities, supervised by Dr. Alexandra I. Cristea and Dr. Maria Liakata. His main research interests are in the areas of Social Media Mining and Natural Language Processing, with a primary focus on sentiment analysis from social media and their implications in real-world events.

Research

Over 70% of the global population is expected to live in big cities by the next 30 years. At the same time, the use of social media is growing at tremendous rates. Motivated by these basic facts, Adam’s work focuses on opinion mining of social media users, as an attempt to model and predict urban-related indices (e.g., elections, wellbeing, etc.). The effective modelling of such domains is a challenging and important task, as it can provide additional “sensors” for informing citizens and policy makers on the current state of an urban environment, but also on possible consequences of their actions.

Jacques Dark

Warwick

BIO

Jacques is a third year postgraduate student at the University of Warwick, working towards a PhD in Computer Science under the supervision of Graham Cormode. Previously, he completed a Master of Mathematics qualification, also at the University of Warwick.

RESEARCH

Jacques’ research is in algorithms for constructing sketches (small summaries) of data when the data is provided in an access-limited form, such as a stream. The sketches can then be examined at a later time to approximately answer questions about the original data. Such techniques can be extremely valuable whenever storing and analysing the full data would be too costly: for example, database query optimisers use estimates of the size of intermediate results to try to choose fast execution plans.

Guillem Mosquera

Warwick

BIO

Born and bread in the coast of Catalonia, Guillem graduated in physics at Barcelona and is now doing a PhD at Warwick University. With broad research interests in statistical physics and network theory, his current focus is on the study of human geography from the perspective of complexity science.

RESEARCH

With ever growing computing and data-mining power available, complex networks cast light on key properties of many artificial and natural systems. Social systems are not an exception, and in an increasingly urbanized world complex networks provide a global perspective on collective human interactions. In this context, Guillem’s research focuses on networks where nodes are real cities from around the globe, whose sustained interactions give rise to trade, immigration, cultural exchange or war, amongst other phenomena. My aim is to clarify this contact patterns and investigate their relation to the geographical and economical environment of cities.

Alessandro Barp

Imperial College London

BIO

Alessandro is a french and italian 2nd year PhD student that studied math and physics in Warwick (focus on continuum and fluid mechanics) and theoretical physics and geometry in Cambridge. He is now working in the math department of Imperial (supervised by Prof Girolami) on information geometry and statistics.

RESEARCH

Alessandro works on sampling methods (mainly Hamiltonian Monte Carlo methods) as well as Information geometry. More generally he is interested in all applications of geometry (including functional analysis) in science.

Tim Pearce

Cambridge

BIO

Tim is a PhD student in engineering at the University of Cambridge (since 2016). Prior to that, he completed an M.Eng with a Master’s project in artificial intelligence. He also worked in the finance sector with EY (Ernst & Young), with whom he qualified as a Chartered Accountant. His work was focused around analytics and building forecasting models to support decision-making.

RESEARCH

Tim is researching applications of data science to the manufacturing industry: how data-driven machine learning techniques might offer improvements over existing high-level statistical models. Currently he is investigating the optimal neural network architecture for predicting machine failures from time-series sensor data. He will then compare their performance with population-level forecasting models. He also plans to investigate the automation of the approval/rejection of warranty claims.

Lewin Strauss

Queen Mary University of London

BIO

Having grown up in Germany, Lewin came to the UK in 2012 to do his undergraduate degree in mathematics at Durham University. He obtained his master’s in 2016, subject to his project work in the area of topological data analysis. Lewin is currently looking forward to transitioning into the second year of his PhD in stochastic topology at the School of Mathematics at Queen Mary University of London.

RESEARCH

Simplicial complexes are topological objects that can be viewed as abstractions of complex networks such as the internet or the human brain. The need to model such increasingly large networks has given rise to the study of random simplicial complexes: it turns out much can be said about large networks if we model them as randomly generated simplicial complexes that grow asymptotically infinitely large. In some sense the study of random simplicial complexes is a higher-dimensional generalisation of the classical study of random graphs. Lewin’s research interests further include topological data analysis, as well as neural networks and AI.

Henry-Louis de Kergolay

Edinburgh

BIO

Henry-Louis is a PhD student in Mathematics at the University of Edinburgh. Prior to starting his PhD in 2015, he studied at Wesleyan University, Connecticut, USA, for his Bachelor, and at the University of Cambridge where he completed Part III of the mathematical tripos.

RESEARCH

Henry-Louis is interested in Diffusion Maps, a way to perform non-linear dimension reduction on high-dimensional data sets. More generally, he is interested in developing techniques to extract geometric features of data sets. He is also working on Topological Data Analysis, and hopes to better understand how techniques from the former can be applied to the latter, and vice versa.

Charlie Dickens

Warwick

BIO

Charlie is currently a second year PhD Student in Computer Science at the University of Warwick and is supervised by Professor Graham Cormode. Prior to this he completed his MSci in Mathematics from the University of Birmingham in 2016.

RESEARCH

Large datasets are ubiquitous in modern society but a common problem is how to deal with such massive datasets? Charlie’s research is concerned with reducing large datasets into smaller, manageable summaries which can be used to approximate key properties of the data. He is also interested in the related questions of whether these approximations can be performed in a reasonable amount of time and whether they can be used in different models of computation; for example, when data arrives one item at a time and only a small amount of it can be kept. These issues span both theoretical and practical backgrounds and will be of interest to anyone who performs large-scale data analysis.

Bertram Vidgen

Oxford

BIO

Bertie has just finished the second year of his PhD at the Oxford Internet Institute, University of Oxford. Previously, he has completed an MA (Distinction) at the University of Essex and a BA Hons (First) from Warwick. During the Summer of 2017 he was a Summer Associate at BCG and in 2016 was a PhD Intern at the Alan Turing Institute.

RESEARCH

The aim of my PhD research is to understand the drivers and patterns of xenophobic behaviour within UK political groups on social media. Drawing on existing social-science theory, I am testing two hypotheses: that (i) social interactions and (ii) large news events drive xenophobic behaviour. To test different types of effects I am conducting four studies. In my main study, I track the behaviour of around 30,000 Twitter users who have followed different right-wing political parties over a 9-month period. Given the size of the dataset (we are expecting to collect around 20 million Tweets) I plan on developing an automatic classifier to detect xenophobic content. More broadly, I am interested in how the Internet, and social media in particular, has transformed political practices, revolutionishing how people connect, communicate and interact. Methodologically, I am keen to deepen my understanding of natural language processing and network analysis.

Louis Ellam

Imperial College London

BIO

Louis Ellam is a third-year doctoral student in the Statistics Section at the Department of Mathematics, Imperial College London. His research focuses on Bayesian inference in socio-economic systems, including retail structure and criminal activity, with a focus on model-based approaches. Prior to his doctoral studies, Louis graduated with a bachelor’s degree in Mathematics and Physics before graduating with a master’s degree in Scientific Computing, both from The University of Warwick.

RESEARCH

Mathematical models are an integral part of quantitative analysis in many branches of science and engineering. Whilst challenging, understanding human behaviour is of paramount importance for policy and decision making. Recently, many socio-economic systems, including retail structure and criminal activity, have been simulated from differential equations and agent based models to gain insights. Moreover, large data sets are becoming routinely available and either ignoring either mathematical models or the available data is unwise. Louis’ research interests are within the field of computational statistics, and relate to model-based Bayesian inference of socio-economic systems.

Nathan Cunningham

Warwick

BIO

Nathan is a 3rd year PhD student in the University of Warwick, a member of the OxWaSP cohort (Oxford Warwick Statistics Programme). His research looks at the development of methods for the analysis of large-scale, complex biological data. He is supervised by Prof David Wild (Warwick) and Prof Jim Griffin (Kent). Previously, he studied Economics and Finance in University College Dublin (UCD) before completing a Master’s in Statistics in UCD also. Prior to beginning his PhD he worked with the Economic and Social Research Institute (ESRI) in Dublin, where he was involved in research projects focused on the Irish healthcare system.

RESEARCH

Nathan’s research focuses on the development of approaches for the integrated statistical modelling of information from disparate sources, such as gene expression data with genomic or clinical indicators, integrated with proteomic or metabolomic measurements. His research focuses on the application of particle Monte Carlo methods to uncover groups of genes which tend to show similar behaviour patterns incorporating information from these various sources in order to aid the understanding of how genetic variation between individuals (the genotype) translates into the variation we can see or measure (the phenotype).

Gianluca Detommaso

Bath

BIO

Gianluca is an Italian PhD student at the University of Bath. He obtained a two-year MSc in Mathematics at the University of Turin (Italy), in parallel with a honour program at the Collegio Carlo Alberto rewarding a MA in Statistics and Applied Mathematics. He started an integrated PhD program at the University of Bath, obtaining a MSc in Statistical Applied Mathematics from the first year, then moving into research. He is currently in the third year of his PhD program. Very active person, he enjoys keeping himself busy and pursuing activities with passion. He loves sports, travelling, food and sticking around with smiling people.

RESEARCH

Gianluca’s research aims to bring together techniques from statistics, numerical analysis and applied mathematics to accelerate Bayesian sampling. In particular, he deals with computationally expensive high-dimensional problems, trying to beat down the cost per iteration and performing algorithms that scale well in high-dimension. Gianluca’s interests are very broad: he loves interactions among different research fields, bringing together knowledge and experimenting new ideas. He is always keen on trying out new potential sampling accelerations, or to apply his machinery to other topics. Some keywords of his current research are multilevel methods, MCMC algorithms, transport maps and Bayesian inverse problems.

Zhangdaihong (Jessie) Liu

Warwick

BIO

Zhangdaihong (Jessie) Liu is a third-year PhD student at the CDT of Mathematics for Real-world Systems at University of Warwick. She is jointly supervised by Prof. Thomas Nichols and Prof. Jianfeng Feng. She completed the MathSys MSc in 2015. Before University of Warwick, she obtained a MSc in Mathematical Finance at Loughborough University and a BSc in Mathematics at Shandong University, China.

RESEARCH

Jessie is currently working on linking brain connectivity, demographics and behaviour using HCP data. Her research interests are in machine learning, factor analysis, statistical methods and multi-modal prediction. She hopes to work with the UK Biobank data on predicting psychiatric related features at the Turing.

Ayman Boustati

Warwick

BIO

Ayman Boustati is a second year PhD student in the Mathematics for Real-World Systems CDT at the University of Warwick. Prior to that, he completed a four year degree in Mathematics, Operational Research, Statistics and Economics in 2015, and an MSc in Mathematics for Real-World Systems in 2016, both at the University of Warwick. His research interests lie in developing novel methodologies for transfer learning and their application in medical diagnostics. He is supervised by Dr Richard Savage.

RESEARCH

The goal of Ayman’s PhD project is to develop novel methods for Transfer Learning, a machine learning framework that enables information sharing between related learning tasks to improve the quality of their predictions. The work involves adapting Gaussian process models for use in different transfer learning scenarios such as multitask learning and asymmetric inductive transfer learning . One of the end goals of this project is to use these models to improve non-invasive medical diagnostic techniques.

Iacopo Iacopini

Queen Mary University of London

BIO

Iacopo Iacopini is a second year PhD Student in Mathematics at Queen Mary University of London within the Complex Systems and Networks Research Group, where he works under the supervision of Vito Latora and Elsa Arcaute (CASA, UCL). He is also collaborating with the Dynamical Systems and Statistical Physics Group as part of the LoBaNet project. Before, he was a Researcher at ISI Foundation (Turin) and a Data Science Intern at United Nations – UPU (Bern).

RESEARCH

Iacopo’s research interests include Complex Networks and Data Science, with a particular focus on spatial and social networks and their data driven application to the study of human behaviour in the urban environment. His main PhD project deals with the mathematical modelling of urban systems and the interplay between spatial interaction models and mobile phone data.

Nicolas Anastassacos

UCL

BIO

Nicolas is a 1st year PhD at UCL. Having completed a BSc Cognitive Science from the University of Edinburgh and MSc Data Science from UCL, Nicolas looks to combine these disciplines in his research to understand behaviour and social contexts from a data-driven perspective.

RESEARCH

Nicolas’s current research interests involve studying human behaviour on both an individual and collective scale, looking to analyze how behaviour changes with respect to the environment and determining optimal ways of behaving using reinforcement learning techniques and game theory. His other interests involve personality-based modelling and developing multi-agent systems.

Zhenzheng (Helen) Hu

UCL

BIO

Helen is a first year Statistics PhD student at UCL supervised by Dr. Ioanna Manolopoulou and Dr. Ioannis Kosmidis. Her main research interests are Statistical Learning, Statistical Inference, and Bayesian Modeling. Helen has recently graduated from University of Edinburgh with a masters in Statistics and Data Science and holds a BA from University of California, Berkeley with a double degree in Mathematics and Statistics.

RESEARCH

Helen will be working on addressing a common challenge in binary studies: observations which are potentially mislabelled or may be missing, but whose mislabelling or missing mechanisms are related to the underlying process itself. The goal is to develop a formal framework of statistical modeling in the presence of inherent missingness and sampling biases. This topic relates to many applied research in Turing.

Andrew Burnie

UCL

BIO

Andrew is a 1st year PhD student in UCL’s Computer Science Department. He specialises in the application of open-source tools (e.g. R and Python) and data science methodologies to the problems of prediction (e.g. of risk, value and retention), isolating relevant value drivers and structuring and analysing unstructured data. Previously, he was a data scientist at Hitachi Consulting and at ERS insurance (a Lloyd’s syndicate), where he set up the data science team. He has a BA in Economics and Management Studies (Cambridge University) and an MSc Finance, culminating in a publication (Burnie and Mchawrab, 2017).

RESEARCH

The popularity of cryptocurrencies has surged since their inception in 2009, bringing their current market capitalization to over 135 billion USD. But they are not backed by either central banks or tangible assets and suffer from exceptionally high volatility, bringing into question whether such investments are financially prudent. Andrew’s research, using tools such as Natural Language Processing, Neural Networks and Anomaly Detection, aims to better understand the drivers of this volatility. The broader relationship between web media data (e.g. social media and web search data) and financial asset values (cryptocurrencies, fiat currencies and gold) is also being explored.

Albese Demjaha

UCL

BIO

Albese Demjaha is joining the Alan Turing Institute as a first year doctoral student at UCL, supervised by David Pym and Angela Sasse. Her research interests are in information security, privacy and human behavior. More specifically, she is interested in philosophical and economic analysis of info-sec problems. Her MSc in Information Security at UCL focused on exploring the need and possibility for new cryptographic metaphors. Albese also has a BSc in Information Systems and Management from the South East European University in Skopje.

RESEARCH

Albese will be working in the area of the socio-economic and philosophical foundations of information security and privacy, with a particular concern for policy questions that arise in the context of the management of large, highly distributed databases containing sensitive and confidential data. Her work will also discuss the economic implications of privacy in relation to human behavior and people’s mental models of privacy.

Prateek Gupta

Oxford

BIO

Prateek is a first-year PhD student at the University of Oxford supervised by Dr Pawan Kumar. His research interests lie in applied graphical models and deep learning.

RESEARCH

Prateek’s project is aimed at inventing new principled algorithms for deep learning, for example, via convolutional neural networks (CNN). He will exploit the special structure of the widely used CNNs. He will formulate the problem of parameter estimation for DC-CNNs as a difference-of-convex (DC) program. The main challenge is to come up with a DC formulation that is amenable to efficient optimisation while still retaining the accuracy of CNNs.

Sanna Ojanperä

Oxford

BIO

Sanna is a first-year doctoral candidate at the Oxford Internet Institute (OII), University of Oxford. Before commencing her doctoral studies she worked as the quantitative research lead in an ERC-funded GeoNet project which studies how new economic practices and processes are taking root in Sub-Saharan Africa as a result of changing connectivities. Before joining the OII, Sanna worked with the Digital Engagement team of the World Bank Governance Global Practice and the Inter-American Development Bank’s Strategic Planning and Development Effectiveness Unit. She earned a Master’s Degree from American University’s School of International Service (Washington, DC) in 2013. At SIS she studied international development, governance, and innovative research methods as an ASLA-Fulbright Fellow.

RESEARCH

While the informal economy is not a new phenomenon, there is renewed interest in understanding it better, because, contrary to expectations, it has not only persisted, but emerged in new forms and places. In many countries, the persistence of informal economic activities coincides with the development of a digital economy, but little is known about the ways in which these complex digitally-enabled activities relate to the informal economy. Sanna’s research investigates whether accessing work through online platforms may foster new practices of informality or lift individuals out of informality, and how these developments impact inequality, social exclusion, individual well-being, and the government’s ability to collect taxes. Her research interests include digital governance, internet and information geographies, knowledge economies, ICT for development, connectivity, inclusion, and equality.

Julien Vaes

Oxford

BIO

Julien Vaes is a final year Master student in Applied Mathematics Engineering at the Université catholique de Louvain in Belgium. His research interests lie primarily in mathematical modelling and programming applied to energy power systems.

RESEARCH

Julien’s supervisor, Pr. Raphael Hauser, investigated, with his former student Miha Troha, the problem of estimating the term structure of future prices in power markets on the basis of risk-averse optimal trading by producers and wholesalers, subject to the physical constraints of each power plant connected to the grid. In his PhD project, Julien’s objective will be to further develop the structure problem established by Hauser-Troha by adding three important components that are currently missing: firstly, the market clearing auction as a last decision stage, secondly, the network capacity and regulatory constraints, and, thirdly, the renewable power sources into the energy mix considered.

Michael Murray

Oxford

BIO

Michael started his DPhil in Mathematics at Oxford in October 2017. He is supervised by Professor Jared Tanner and Dr Hemant Tyagi. Prior to joining Oxford and the ATI he worked as a strategy consultant and before that as an engineer for a startup building products in the smart home space. Michael completed his Masters in Electrical and Electronic Engineering at Imperial College London in 2014.

RESEARCH

Testing a hypothesis typically requires extracting information from the relevant signals and data available. Sadly this process is not always easy; signals can be corrupted, sensors lacking the necessary resolution and data too unwieldy to process. In many cases however the data in question can be explained by simple structures with limited degrees of freedom, which better lend themselves to analysis. Michael’s research is focused on investigating these structures and subsequently deriving efficient algorithms to acquire information from data which is incomplete, noisy or poorly formed.

Francesco Consentino

Oxford

BIO

Francesco is a first year Ph.D. student in Computer Science at University of Oxford, supervised by Prof. Alessandro Abate. Before joining the Turing he developed expertise in the financial industry as FX risk analyst. He holds a Bachelor and an M.Sc. in Mathematics from University of Turin, Italy, with focus on Probability, Finance and Insurance, and an M.A. in Financial Economics from Collegio Carlo Alberto, Turin.

RESEARCH

He is interested in optimization of stochastic environments from a computer science and mathematical point of view. His research focuses on stochastic optimal control driven by the massive amount of data generated today by electronic systems, using statistical techniques and automated verification. In particular, he is attracted by financial applications, which are relevant thanks to the dynamical setting of the problems, e.g. derivatives pricing and utility maximization.

Alex Mansbridge

Cambridge

BIO

Alex is a first year PhD student at the University of Cambridge. After graduating with a masters degree in Mathematics, he worked for 18 months in a risk modelling position at a major Danish Bank. Following this, he worked for two years as a senior data scientist at Under Armour Connected Fitness. Here, he worked with an accomplished team of data scientists to implement deep learning algorithms across Under Armour’s suite of fitness apps, with the aim of improving the health and well being of millions of users worldwide.

RESEARCH

Alex’s research interests lie at the intersection of deep learning and natural language processing. His research is focused on tackling problems in the field of question answering, through the use of memory augmented neural networks. He is interested in how the concept of external memory can be used to improve the performance of neural networks, and enable them to handle information from complex graphical data structures more effectively.

Taha Caritli

Edinburgh

BIO

Taha is a first year Ph.D. student registered at University of Edinburgh working under the supervision of Chris Williams. He received his B.Sc. (2015) and M.Sc. (2017) at the Bogazici University. His research interests include Bayesian inference in probabilistic graphical models with the applications for data analytics.

RESEARCH

In data analytics projects, a significant amount of time and effort are given to data wrangling process by an analyst or a data scientist or a researcher where the goal is to transform raw data into a format that can be used with computer models. Hence, it is crucial to develop systems that perform some data preprocessing steps automatically. Taha’s studies focus on machine learning methods to automate data summarization and cleaning process which includes tasks such as understanding data, extracting useful features, identifying missing or anomalous data, etc.

Florentin Goyens

Oxford

BIO

Florentin Goyens is a first year doctoral student from Oxford University. He is coming from Belgium as an engineer in applied mathematics from the Université Catholique de Louvain (2016). After previous work with Prof. Pierre-Antoine Absil and Raphaël Jungers in Belgium, he is now completing his PhD under the supervision of Coralia Cartis (Oxford) and Armin Efthekari (Edinburgh). His research interests include nonlinear optimisation problems defined on Riemannian manifolds.

RESEARCH

Florentin’s MSc dissertation analysed variant of quasi-Newton method for nonsmooth optimization problems. During his PhD, he will look at various optimization methods to solve problems defined on Riemannian manifolds. The field of Riemannian optimization is in great expansion as it offers an alternative to nonlinear problems which are often hard to solve, and linear problems that can be simplistic models. Numerous questions in data processing and machine learning are naturally expressed as optimisation over manifolds.

Nikolas Kuhlen

Warwick

BIO

Nikolas is a first-year PhD student in Statistics. Prior to coming to The Alan Turing Institute, he obtained a BSc in Economics from the University of Muenster, an MSc in Economics from the University of Bonn and an MSc in Statistics from the London School of Economics and Political Science (LSE). During his studies, he spent one semester at Aix-Marseille University.

RESEARCH

Nikolas’ research interests lie at the intersection of Computational Statistics, Econometrics, Machine Learning, and Economics. In the past, he has worked on topics such as bandwidth selection for kernel density estimation and the estimation of income distributions.

Amartya Sanyal

Oxford

BIO

Amartya Sanyal is a first year doctoral student in the Dept. of Computer Science, University of Oxford jointly supervised by Dr. Varun Kanade and Dr. Phil Torr. He graduated with a B.Tech in Computer Science And Engineering with a minor in Linguistic Theory in the top 7% of his class from the Indian Institute of Technology, Kanpur in the year 2017 where he was a student of the Department of Computer Science And Engineering where he worked on projects in non-convex optimization, deep learning and extreme learning. He has interned in the Montreal Institute of Learning Algorithms under Prof. Yoshua Bengio and Twitter Cortex NYC among others where he has worked on developing generalized sequence learning models and adversarial models for natural language modelling.

RESEARCH

Deep neural networks have revolutionized the field of machine learning and their use has improved on the prior state of the art significantly in diverse domains such as vision, natural language processing, speech, reinforcement learning, etc. Optimization methods used in the training of these networks can be somewhat ad hoc and lack a unified understanding, primarily because of the non- convex nature of these optimization problems. The work will focus on developing new methods for training networks that involves both changes to optimization methods as well as network design. The research will involve both experimental validation on real-world and synthetic data and mathematical analysis of the proposed methods.