Skip to main content

WDSI People

Click on someone's name below to visit their home page.

WDSI Director: David Firth
WDSI Administrator: Eleanor Ingram <wdsi dot enquiries at warwick dot ac dot uk>

B

Barons, Martine

Barons, MartineDepartment of Statistics

Director of the Applied Statistics & Risk Unit

The AS&RU exists to encourage and co-ordinate external partnerships which enable the early applications of theoretical, methodological and algorithmic developments.

Martine J. Barons works on networking probabilistic models for decision support, with applications in household food security.


Becker, Sascha O.

Becker, SaschaDepartment of Economics

Sascha O. Becker works on Labour Economics, Education Economics, Public Economics and Economic History, often using large data sets. His current research looks into the deep roots of long-run economic development, using historic data sources. He has an interest in text analysis to capture cultural change as a factor in long-run development.


Bhalerao, Abhir

Abhir BhaleraoDepartment of Computer Science

My research expertise is in image analysis and pattern recognition, with particular emphasis on application areas such as fingerprint analysis, face recognition and other areas of digital forensic analysis.

I work with several industry partners including Warwick Warp (for which I am a founding director) and Pattern Analyics.


Bretschneider, Till

Till BretschneiderWarwick Systems Biology Centre

Till Bretschneider has expertise in working with large scale, high-resolution image time-series data acquired by live cell microscopy. His main research interest is cell motility during development and self-organisation of the cytoskeleton. His research group develops software for single cell tracking in 2D and 3D, and detailed analysis of cell shape, where mathematical modelling is an integral part to understand complex spatio-temporal patterns of cellular signalling.

The latest microscopy techniques easily generate data in the range of many Terabytes per single experiment. To cope with the amount of data real-time processing using massively parallel GPU computing becomes more and more important.


Brettschneider, Julia

Julia BrettschneiderDepartment of Statistics

With a background in both probability theory and applied statistics, I am working on big data sets collected in a range of disciplines including genomics, microscopy, cancer research and finance. Particular challenges coming with high-dimensional data sets include data-quality assessment and extraction of relevant information supporting decision processes.

Current projects include:

  • Modeling decision-making processes involving genomic cancer recurrence risk
  • Quantifying occurrence and colocalisation of proteins observed by confocal fluorescent microscopy
  • Describing and understanding mechanisms of individual trading behaviour
  • Finding disease predictors in genomic data sets

C

Chen, Mingli

Chen, MingliDepartment of Economics

My research fields are Econometrics, Time Series Econometrics, Financial Econometrics and Industrial Organization. In particular, my papers are on nonlinear panel data models with factors, detection of structural breaks, large network estimation and the study of consumer payment choice.


Connaughton, Colm

Colm ConnaughtonWarwick Mathematics Institute and Centre for Complexity Science

Colm's main research interests are in non-equilibrium statistical mechanics and turbulence. He is particularly interested in applications in the Earth Sciences such as inverse problems in droplet coagulation.

He is also interested in applications of time-series analysis to industrial problems and is a founding member of the EPSRC Early Career Forum in Manufacturing Research.


Cormode, Graham

Graham CormodeDepartment of Computer Science

My interests are in all aspects of the "data lifecycle", from data collection and cleaning, through mining and analytics, and private data release.

Areas of interest:

  • Streaming Algorithms
  • Distributed Data Analytics
  • Privacy & Anonymization
  • Scholarly (Bibliographic) Data

Cremona, John

John CremonaWarwick Mathematics Institute

John Cremona works in computational number theory, in the area of elliptic curves and modular forms. He is interested in tabulating these systematically, and his database of elliptic curves, first published within a book (Algorithms for Modular Elliptic Curves, CUP (1992)), is a standard reference in the field.

Over the last five years the elliptic curve database project has become part of a much large world-wide research project called the L-Functions and Modular Forms Database (LMFDB) which was originally funded by a $1.2M US NSF FRG grant, and since 2013 is funded by an EPSRC Programme Grant worth £2.2M, shared between Bristol and Warwick, of which John Cremona is the PI. As well as Bristol and Warwick, the project has a large international team of collaborators. The project consists of a large database, hosted at Warwick, containing a substantial amount of number-theoretical data (for example including over 100 billion zeros of the Riemann Zeta Function, each to 100-bit precision) accessible by the website www.lmfdb.org, also hosted at Warwick, allowing different views of the data and showing clearly the links between different mathematical objects, some of which are still only conjectural and all of which are the subject of a large amount of research worldwide. As well as new mathematical theory, the project involves much algorithmic development, realised and implemented entirely as open source software, so that both the data and the code used to create it and display it is all fully open.


Cristea, Alexandra

Alexandra CristeaDepartment of Computer Science

Alexandra Cristea's research includes user modelling and personalisation, semantic web, social web, authoring, with over 200 papers on these subjects.

Relevant projects include FP7 Blogforever project, where semantics from weblogs was extracted for data preservation, and smaller sized projects on sentiment analysis on Twitter data for automatic pairing with appropriate advertising, as well as projects on adaptive advertising and open web personalisation, among others.


Czumaj, Artur

Artur CzumajDepartment of Computer Science

My interest lies in the theoretical analysis of large networks and the design of efficient algorithms to understand networks and combinatorial structures.

  • Sublinear Algorithms
  • Property Testing
  • Fast Algorithms for Big Datasets

D

Damoulas, Theo

Damoulas, TheoDepartment of Computer Science and Department of Statistics

My research area is machine learning, at the intersection of computer science with Bayesian statistics. This includes:

  • Spatio-temporal inference, non-stationary processes
  • Multiple kernel/task learning and kernel constructions
  • Approximate Bayesian inference
  • Large-scale problems in Urban Science and Computational Sustainability

E

Epstein, David

Epstein DavidWarwick Mathematics Institute

I work with biologists and computer scientists, making multiple images of the same tissue section, extracting different data from different images. One might compare healthy with diseased tissue. The data is voluminous and requires considerable analysis to make sense of it. The projects of interest are biomedical at the molecular level. I am concerned mostly with a project funded by BBSRC which is fundamental research that might help with diabetes.

As a separate, but related, issue, I am interested in discovering the geometry of objects that could give rise to an observed set of points in a space of many dimensions. This entails using a mixture of mathematics, computer science, statistics, intuition and common sense.

F

Finkenstädt Rand, Bärbel

FinkenstadtDepartment of Statistics

My area of research is in statistics and mathematical biology.

In particular, I am interested in analysing temporal and/or spatio-temporal data from biological systems (from single cells to meta-populations). My recent collaborations include addressing questions in chronobiology with collaborators in the Biological Sciences and Medicine. In collaboration with Francis Lévi, I am researching statistical methods for non-stationary statistical modeling and inference for circadian oscillations for research in cancer chronotherapy (see BBC video).


Firth, David

Department of Statistics; Director of WDSI

I work on statistical theory, methods and computation, and applications in many disciplines, especially the social sciences. I have published several widely-used R packages on CRAN, one of which (gnm, written with Heather Turner) won the John M Chambers Statistical Software Award of the American Statistical Association. My research work led to the highly successful exit-polling methods that have been used by all the major broadcasters at recent UK general elections.

Some current research topics:

  • inference and computation for generalized nonlinear models (with Heather Turner and Ioannis Kosmidis);
  • models of competition and the analysis of pair-comparison data (with Manuela Cattelan, Cristiano Varin and Heather Turner);
  • penalized likelihood methods, especially for modelling discrete data (with Ioannis Kosmidis);
  • working with intractable likelihoods (with Helen Ogden, as part of the i-like programme);
  • GLM computation with huge data (with Ioannis Kosmidis).

G

Girolami, Mark

Mark GirolamiDepartment of Statistics

Mark Girolami is Professor of Statistics and is honorary Professor of Computer Science at Warwick. His research and that of his group covers the investigation and development of advanced foundational statistical methodology driven by applications in the life, clinical, physical, chemical, engineering and ecological sciences.

He also works closely with industry where he has several patents leading from his work on e.g. activity profiling in telecommunications networks and developing statistical techniques for the machine based identification of counterfeit currency which is now an established technology used in current Automated Teller Machines manufactured by National Cash Registers (NCR).

He is an EPSRC Established Career Fellow (2012 - 2017) and previously an EPSRC Advanced Research Fellow (2007 - 2012). He is also honorary Professor of Statistics at University College London, is the Director of the EPSRC funded Research Network on Computational Statistics and Machine Learning and in 2011 was elected to the Fellowship of the Royal Society of Edinburgh (FRSE) when he was also awarded a Royal Society Wolfson Research Merit Award.


Griffiths, Nathan

Nathan GriffithsDepartment of Computer Science

Machine learning for driver and vehicle monitoring based on telemetry and CAN-bus data. Using machine learning techniques we are able to make predictions about a vehicle's current environment (such as current road type) without relying on GPS and mapping data, and are able to monitor the driver's cognitive load in terms of whether they are distracted or stressed. This work is in collaboration with Jaguar Land Rover.

Influence and behaviour propagation in online and offline social networks. Using agent-based modelling and machine learning we are able to identify potentially influential nodes and targets for disruption in social networks. This has applications to social settings such as smoking cessation behaviour and product purchasing, and to law enforcement for gathering intelligence. This work is in collaboration with Sandtable Limited and West Midlands Police.

H

Hills, Thomas

Hills, ThomasDepartment of Psychology

As a behavioural and data scientist, I am interested in quantitative approaches to language, wellbeing, memory, and decision making. My work involves using large volumes of data to understand psychological change over cultural time; understanding language learning using network analysis; computational modeling of memory representations and age-related cognitive decline; and information search in decison making.

Hollingsworth, Deirdre

Hollingsworth, DeirdreWarwick Mathematics Institute and School of Life Sciences

Deirdre is an infectious disease epidemiologist with a particular interest in neglected tropical diseases, which predominantly affect the poorest populations in the world. She uses a range of population and risk mapping data to assess the size of the population at risk of these diseases and project the impact of interventions.

I

Icardi, Matteo

Matteo IcardiWarwick Mathematics Institute

I am interested in the modelling and simulations of complex flows arising in problems such as electrochemical devices, subsurface and porous media, turbulent multiphase reactors. These problems are typically characterised by multiple physics and length scales and, due to their complexity, have to be addressed with an integrated modelling and data-driven approach. I am therefore interested in calibrating and validating mathematical models and estimating uncertainties by means of Bayesian inference. I am also interested in the upscaling and model reduction.

J

Jacka, Saul

Saul JackaDepartment of Statistics

Saul Jacka's research is in many areas of probability theory with special interests in

  • mathematical finance,
  • stochastic processes,
  • stochastic control,
  • optimal stopping,
  • conditioned processes and
  • probability on trees and related structures.

Jarvis, Stephen

Stephen JarvisDepartment of Computer Science

My background is in High Performance Computing but more recently my research has focused on compute architectures and codes for data-intensive problems.

Two examples of this include: funded research with HMG on secure on- and off-line analytics at scale; a focus on urban data (urban analytics), which is realized through Warwick’s Urban Science CDT for which I am PI and Director.


Jenkins, Paul

Paul JenkinsDepartment of Statistics and Department of Computer Science

My research interests in statistics include simulation, Monte Carlo methods, and inference from stochastic processes.

I am particularly interested in the application of these areas to inference from genomic data. I am also interested in the development of models in population genetics, such as coalescent theory and diffusion processes of gene frequency evolution.


Jhumka, Arshad

Department of Computer ScienceJhumka, Arshad

My primary research area is dependability. Lately, I have been using machine learning to drive the design of dependable systems. One current area is the application of ML techniques to failure logs of large-scale computing systems (such as cluster systems) for error detection and failure prediction. I have also developed similar approaches for dependable SW systems. I am also using ML techniques to investigate security issues such as characterisation of various types of malware.

Johansen, Adam

Adam JohansenDepartment of Statistics

Adam Johansen's interests lie predominantly in the field of computational statistics.

He has particular interests in the field of time series and particularly the conduct of inference in real time as data becomes available, and inference for intractable models. He is also currently involved in a number of projects related to challenging problems in neuroimaging.


Joy, Mike

Mike JoyDepartment of Computer Science

I am an educational technologist and an information systems engineer in the Department of Computer Science. Much of my current work involves deploying software (and sometimes hardware) solutions to educational problems and evaluating their effectiveness using data collected from students and elsewhere, and involves a mixture of both quantitative and qualitative methods. The types of research problems being addressed are both technical, grounded in computer science, and educational.

L

Latuszynski, Krzysztof

Krzysztof ŁatuszyńskiDepartment of Statistics

Krzysztof Łatuszyński's research Interests include:

  • Scalability of MCMC and related computational algorithms;
  • Monte Carlo inference for intractable likelihood problems;
  • Large-scale model and variable selection.

Lee, Anthony

Anthony LeeDepartment of Statistics

I am interested in the development, analysis and application of Monte Carlo methods in statistics, a key tool in the analysis of data. This typically requires a blend of statistical, probabilistic and computational skills.

Large datasets hold immense promise from a statistical perspective, and I am particularly interested in the computational challenges posed by their analysis.


Leeke, Matthew

Matthew LeekeDepartment of Computer Science

I research computer science topics related to geospatial data, with particular reference to mobile sensing and location-aware systems.

Recent work at Mobility-IoT’14 reports on the impact of data sparsity and duration on location prediction applications.


Leng, Chenlei

Chenlei LengDepartment of Statistics

Chenlei is interested in high dimensional data analysis, graphical models, and social network data analysis.

He is particularly interested in the methodological aspects of these areas.

An emphasis of his research is to develop novel statistical models for analysing big data with sound theory and fast computation.


Li, Chang-Tsun

Chang-Tsun LiDepartment of Computer Science

I lead research at Warwick on information security and forensics, machine learning, pattern recognition and multimodal biometrics. I am the former Editor-in-Chief of the International Journal of Digital Crime and Forensics and I am the coordinator of the international joint project Digital Image and Video Forensics funded through the Marie Curie Industry-Academia Partnerships and Pathways (IAPP) under EU FP7. My research on digital forensics has been commercialized and is in use by the police and by INTERPOL.


Liakata, Maria

Maria LiakataDepartment of Computer Science

My research interests include text and data mining, natural language processing, biological text mining, computational semantics, scientific discourse structure, sentiment analysis.

My work has contributed to advances in knowledge discovery from corpora, automation of scientific experimentation and automatic extraction of information from the scientific literature. In recent years I have focussed on the annotation and automatic identification of discourse structure in scientific literature in terms of the core components of scientific investigations. I have used these annotations to create automatic summaries and in a number of other biomedical applications with a potential impact in healthcare, such as the dynamic enhancement of drug package information interactions and the facilitation of cancer risk assessment.

M

MacKay, Robert

Robert MacKayWarwick Mathematics Institute and Centre for Complexity Science

Robert MacKay pioneered topological data analysis long before the Edelsbrunner/Carlsson wave (though this has gone largely unnoticed). The work was mostly carried out by postdoc Mark Muldoon at Warwick on an EPSRC grant with David Broomhead (Manchester): Muldoon, MacKay, Broomhead and Huke: Topology from time series. Physica D 65 (1993) 1–16. More recently MacKay had an undergraduate project student apply topological data analysis to data from a CAT scanner in WMG, and one of our Erasmus Mundus masters students applied it to EEG data with the Institute for Digital Health.

MacKay is director of the EPSRC&MRC CDT on Mathematics for Real-world systems, successor to the EPSRC DTC on Complexity Science. The CDT will train students in the development and application of models and methods to analyse data from areas like health, industry and finance. It is supported by 12 external partners: JLR, NatGrid, Thales, BT, PHE, PHW, Sciteb, Simpact, Polymaths Consulting, University Hospitals Birmingham, Birmingham School of Cancer Sciences, Pirbright, all of whom are keen to have our students work on their data. Jointly with the Urban Sciences CDT at Warwick, we have been awarded funds for a Data Analytics Platform to assist in this.

MacKay is partner in a Global Collaboration on Financial Systems Stability, coordinated by Sciteb. It has recently received funding from Barclays and Standard Chartered to plan methods to analyse stress test data for the Bank of England.

MacKay is partner in an EPSRC Maths programme bid on Urban Mathematical Analytics, led by Des Higham (Strathclyde) with Peter Grindrod (Oxford), which has recently passed outline stage. In this programme we plan to develop mathematical approaches to the analysis of data from time-dependent multi-layer networks provided by cities. The programme will link with the CUSP and other smart cities initiatives and has support from BT, Capita, IBM, Microsoft, Scottish Power, Siemens, Lloyds bank, Dunnhumby, PA Consulting, Scottish & Southern Energy, Vodafone, Bloom, Cisco, and Xerox.


McInerny, Greg

McInerny GregCentre for Interdisciplinary Methodologies

My research is a blend of science, software and visualisation. This ‘joined-up’ ethos has emerged from the multiple methodological challenges inherent to issues concerning planetary health, such as climate change and engaging stakeholders with uncertainty.

Currently, my research focuses on projects in reproducibility and usability in ‘Species Distribution Modelling’ software (ZOON) and developing visualisation techniques for analytical and communication tasks within science and statistics.

Moat, Suzy

Warwick Business School

Moat, Suzy 2Suzy Moat is an Associate Professor of Behavioural Science at the University of Warwick. She directs the Data Science Lab at Warwick Business School together with her colleague Tobias Preis, and is also a Faculty Fellow of the Alan Turing Institute. Moat's research investigates whether data on our usage of the Internet, from sources such as Google, Twitter, Wikipedia and Flickr can help us measure and predict human behaviour in the real world. The results of her work have been featured by television, radio and press worldwide, by outlets such as CNN, BBC, The Guardian, Wall Street Journal, New Scientist and Wired. She has also acted as an advisor to government and public bodies on related topics. Moat studied Computer Science at UCL and Psychology at Edinburgh, during which time she won prizes including the UCL Faculty of Engineering Medal.

N

Nichols, Thomas

Department of Statistics and Warwick Manufacturing Group nicholst-headshot-thumb.jpg

Dr. Thomas Nichols' research focus is on developing modelling and inference procedures for brain imaging data, usually from Magnetic Resonance Imaging (MRI).

MRI data is intrinsically "large data", but becomes massive when collected as part of population based studies, like the Human Connectome Project (1,200 subjects) and the Biobank Imaging Extension (100,000 subjects). He specialises on computationally efficient techniques for combining imaging data across studies (meta-analysis) and with genetic data ("Imaging Genetics").

O

Ott, Sascha

Sascha OttWarwick Systems Biology Centre

A key component of my work is to extract meaningful information from large high-throughput sequencing data sets including:

  • ChIP-chip/ChIP-seq data - sites of DNA-binding for a particular protein
  • DNase-seq data - sites of DNA-binding for many proteins
  • MeDIP-seq data - methylated sites in DNA
  • Hi-C data - regions of high cross-linking frequency in DNA
  • RNA-seq data - RNA sequences (and gene expression levels)
  • RIP-chip data - sites of RNA-binding for a particular protein
  • Ribosome profiling data - exact sites of RNA-binding for ribosomes
  • TCR repertoire sequencing data - RNA sequences of T-cell receptors

P

Pollock, Murray

Murray PollockDepartment of Statistics

I am currently a Postdoctoral Research Fellow based in Warwick Statistics department working on the EPSRC programme grant ''Intractable Likelihood: New Challenges from Modern Applications (i-like),'' held jointly with Bristol, Lancaster, Oxford and Warwick universities. My research interests include:-

  • Monte Carlo methodology (particularly Retrospective Monte Carlo, Sequential Monte Carlo and Markov Chain Monte Carlo with application to large data sets).
  • Computational methods for Stochastic Differential Equations (particularly Exact Simulation methods).
  • Inverse Problems.
  • Computational Statistics (including inference for intractable models).

Preis, Tobias

Preis TobiasWarwick Business School

Dr Tobias Preis is an Associate Professor of Behavioural Science and Finance at the University of Warwick. Together with his colleague Dr. Suzy Moat, he directs the Data Science Lab at Warwick Business School. His recent research has aimed to analyse and predict real world behaviour with the volumes of data being generated by our interactions with technology, using data from Google, Wikipedia, Flickr and other sources. His research is frequently featured in the news, by outlets including the BBC, the New York Times, the Financial Times, Science, Nature, Time Magazine, New Scientist and the Guardian. He has given a range of public talks including presentations at TEDx events in the UK and in Switzerland. More details can be found on his website http://www.tobiaspreis.com.


Procter, Rob

Rob ProcterDepartment of Computer Science

I work in the area of social informatics, the study of factors that shape ICT adoption and use. A major part of my work in the past 10 years has focused on innovations in computational research infrastructure, tools and methods. Recently, I have been applying these innovations in the large-scale study of uses of social media. I led a multidisciplinary team to work with the Guardian/LSE on the Reading the Riots project, analysing tweets sent during the August 2011 riots. This work is continuing with JISC, ESRC and EU FP7 support.

I continue to research the impact of 'big data' on strategic and operational decision-making, including the use of social media by emergency services and the emergent role of social media in civil society and the everyday life of communities.

I am on the editorial board of the new journal Big Data and Society.

R

Rajpoot, Nasir

Rajpoot, NasirDepartment of Computer Science

Current focus of research in my lab is on developing algorithms for the analysis of large multi-gigapixel digital pathology images, with applications to computer-assisted grading of cancer and predictive markers for prediction of cancer progression and survival. I work very closely with the Pathology department at our local UHCW NHS Trust and collaborate with industrial partners in the emerging area of Digital Pathology.


Rathelot, Roland

Rathelot, RolandDepartment of Economics

Roland Rathelot works on Labour Economics, often using large administrative datasets and data collected from the web. His current research looks into:

  • frictions and mismatch on the labour market and how intermediation can help
  • ethnic discrimination

He has an interest in statistical methods used to deal with large samples as well as natural language processing.


Redoano, Michela

Redoano, MichelaDepartment of Economics

Michela works on political economy and public economics and behavioural economics often using large datasets.

A recent area of research is the use of Facebook data to explore how users form their political preferences.

Roberts, Gareth

prof_gareth_roberts.jpgDepartment of Statistics

Gareth Roberts is Director of the EPSRC-funded Centre for Research in Statistical Methodology. He is also Principal Investigator of the i-like project which has a strong focus on scalable computational methods for likelihood-based statistical interest, and an investigator on the EQUIP project on Bayesian computationally intensive methods for statistical inverse problems. He was elected Fellow of the Royal Society in 2013.

He has wide interests across statistics and applied probability, with a particular focus towards

  • Computational Statistics, particularly MCMC, sequential Monte Carlo, Monte Carlo likelihood
  • Stochastic processes, especially stability theory for Markov chains, Stochastic Differential Equations
  • Stochastic simulation
  • Inference for Stochastic Processes
  • Statistical methodology for missing data
  • Bayesian statistics
  • Statistical inference for Infectious diseases.

Robertson, Ian

Department of Computer ScienceRobertson, Ian

I am a visiting academic funded by the Royal Academy of Engineering. My background is in industry and I have worked in information assurance and cyber-security for 20 years at IBM. I have lead security architecture and consulting projects in the private and public sectors and have many contacts in the security industry.

S

Savage, Richard

Richard SavageWarwick Systems Biology Centre

My main research interest is statistical machine learning and its application to important problems in medicine, in particular medical informatics and translational medical research.

This principally involves the use of probabilistic methods for extracting scientific knowledge from large, potentially complex data-sets.


Steel, Mark

Mark SteelDepartment of Statistics

Mark Steel is interested in theoretical and applied Bayesian statistics, and part of his interests stem from his background in economics. His current and recent research interests include

  • Distribution theory, focusing on modelling with non-normal distributions, incorporating data aspects such as skewness, fat tails, etc.
  • Bayesian nonparametrics, specifically introducing meaningful dependence mechanisms that allow for the introduction of covariates.
  • model uncertainty, in particular regarding the choice of covariates using Bayesian model averaging, with applications in economics (relatively large model spaces of the order of 2^100 models) and genetics (very large model spaces of the order of 2^10000 models).
  • computational tools such as adaptive MCMC methods, specifically in the context of Bayesian model averaging.
  • spatiotemporal modelling using non-Gaussian processes that vary continuously in space and time with nonseparable covariance functions.
  • survival modelling, focusing on heterogeneity and including applications to university dropout and graduation.

T

Tiskin, Alexander

Alexander TiskinDepartment of Computer Science

My research in the area of string algorithms is concerned with efficient methods of string comparison and approximate string matching, their connections with computational geometry and algebra, and their applications in computational molecular biology (such as the one described here and here).

My research in the area of parallel computation is concerned with the design and analysis of efficient algorithms and programming techniques for parallel computers.

V

Vollmer, Sebastian

Sebastian Vollmer photoDepartment of Statistics

Sebastian Vollmer works on methodology and theory for computational statistics and machine learning. One focus is on Markov chain Monte Carlo algorithms for large scale Bayesian inference.


van den Berg, Hugo

Hugo van den BergWarwick Mathematics Institute

Mathematical biology.

The fine structure of the T-cell receptor repertoire and its development over time.

Present research is aimed at elucidating the relationships between immune response efficacy and the statistical distributions of T cell specificities among the naive repertoire and among T cell populations which respond to given antigenic challenges.

W

Wade, Sara

Wade, SaraDepartment of Statistics

My research interests lie in Bayesian nonparametrics and machine learning. Motivated by the increasing variety and complexity of datasets, my work involves developing appropriate models and prior distributions to accurately capture the data generating mechanisms. I am particularly interested in Bayesian clustering and feature allocation models and extensions, for example, to include covariate, temporal, or spatial information. From an applied perspective, I have worked on Alzhemier disease studies, utilising neuroimaging, clinical, and biological data for diagnosis and disease monitoring.


Wild, David

David WildWarwick Systems Biology Centre

My research interests are in the field of statistical bioinformatics; in particular in the application of Bayesian statistical machine learning techniques to problems in systems biology, functional genomics and proteomics.

In the forthcoming era of genomic medicine and agriculture, genome sequences and other forms of high-throughput data such as gene expression, alternative splicing, DNA methylation, histone acetylation, and protein abundances will be routinely measured for large numbers of people and crop species. New approaches to synthetic protein design offer the long-term potential for novel biotechnological and therapeutic applications. Statistical and computational methodology will be essential to realizing the promise of these technological developments.

To data-science researchers at Warwick:

If you want to:

  • amend your entry on this page; or
  • register with the online WDSI Forum, to receive information from WDSI and take part in online discussions; or
  • become a member of WDSI and be listed on this page,

then please visit the WDSI Intranet pages for more information.