Find Academic Supervisors

• Statistics
• Econometrics
• Applied Mathematics
• +4 more

Professor Guy Nason is the Chair in Statistics at Imperial College London's Department of Mathematics, part of the Faculty of Natural Sciences. He leads the NeST Programme Grant, a UKRI-funded initiative involving nine investigators across six universities, focusing on network stochastic processes and time series. He also serves as co-Chair of the Home Office Science Advisory Council, advising the UK Government on scientific matters. His research spans time series analysis, network time series (e.g., GNAR models), multiscale methods, and applications in official statistics, energy, and epidemiology. His work emphasizes practical methodologies, with over 40 collaborators and significant industry partnerships (e.g., EDF Paris, Shell). He develops R packages like 'GNAR' and 'NetTree' for statistical analysis and forecasting. His contributions include advancing locally stationary time series modeling and network-based forecasting techniques, particularly for high-dimensional and missing data scenarios. Key roles: Director of NeST Programme Grant, Home Office Science Advisory Council co-Chair, Head of Statistics Section at Imperial Research themes: Network time series, multiscale wavelet methods, official statistics Software contributions: 18+ R packages including 'wavethresh', 'GNAR', and 'NetTree' His research addresses challenges in forecasting, variance stabilization, and network-driven data analysis, with applications to public health (e.g., Mumps and COVID-19 modeling), economic indicators, and political dynamics (e.g., Early Day Motion analysis in UK Parliament).

• Dynamic Network Analysis
• Bayesian Statistics
• Latent Variable Models
• +6 more

Francesco Sanna Passino is an Assistant Professor in Statistics at Imperial College London's Department of Mathematics, within the Faculty of Natural Sciences. His research focuses on statistical analysis of dynamic networks, Bayesian methods, latent variable models, and applications in cybersecurity, social networks, and transportation systems. He holds a PhD in Statistics from Imperial College London (2021), an MSc in Statistics from Imperial College London (2017), and dual BSc degrees in Statistics from the University of Glasgow (2016) and the University of Bologna (2016). Research interests span dynamic network analysis, event-time data, latent factor models, and model-based clustering. Notable applications include statistical cybersecurity (e.g., attack pattern detection), social network dynamics, and infrastructure systems like bike-sharing. His work integrates advanced statistical techniques such as latent variable models, point processes, and spectral embedding. Publications emphasize methodological innovations in network time series, synthetic data generation, and graph-based processes. Collaborations include Imperial's Artificial Intelligence Network. No awards are explicitly listed, but his research aligns with cutting-edge topics like AI-driven financial trend detection and privacy-preserving synthetic data. He advises no listed students and has no documented grants in the provided texts. His work is housed in the Huxley Building on the South Kensington Campus. For further details, visit his Imperial webpage or Google Scholar profile .

• Statistics
• Applied Mathematics
• Econometrics
• +6 more

Almut Veraart is a **Professor of Statistics** and **Head of the Statistics Section** at the **Department of Mathematics**, **Faculty of Natural Sciences**, Imperial College London. She co-directs the **Center for Doctoral Training on Mathematics for our Future Climate (MFC CDT)**. Her affiliations include the **Grantham Institute**, **Mathematics of Planet Earth**, and **Stochastic Analysis** groups. Her research focuses on statistical methodology for stochastic processes, financial mathematics, climate modeling, and energy systems. **Research Interests**: Her work spans ambit stochastics, stochastic volatility models, extreme value theory, and applications to energy markets and environmental systems. Notable contributions include trawl processes for high-dimensional time series, stochastic modeling of electricity prices, and quantitative energy finance. **Professional Roles**: In addition to her academic leadership roles, she contributes to Imperial's broader research initiatives in climate science and sustainable energy. Her publications address topics like volatility modeling, deep learning applications in finance, and spatio-temporal statistics. **Labs/Teams**: Her research is closely tied to Imperial's **Grantham Institute** and **Mathematics of Planet Earth** initiatives, focusing on interdisciplinary challenges in climate and energy systems.

• Robust Statistics
• Machine Learning
• Biostatistics
• +3 more

Matias Salibian-Barrera is a full Professor in the Department of Statistics at the University of British Columbia's Faculty of Science, where he leads research in robust statistical methodologies with applications across machine learning, biostatistics, and environmental sciences. His research focuses on developing computationally efficient robust techniques resistant to outliers and model misspecification. Key areas include: Robust regression and inference for high-dimensional data Robust causal inference with complex observational data Robust functional and spatial data analysis Integration of robust methods with deep learning architectures Applications in genomics, climate science, and finance His recent publications demonstrate a strong trend toward unifying robust statistics with modern machine learning, particularly in adversarial settings and high-dimensional inference, while maintaining rigorous theoretical foundations. Notable honors include: CRM–SSC Prize in Statistics Fellow of the American Statistical Association Killam Research Prize Coxeter-James Prize He actively supervises doctoral students and secures major grants from NSERC and CIHR, focusing on robust methods for biomedical and environmental data. His work involves collaborations with UBC's Machine Learning Group and the Pacific Institute for the Mathematical Sciences, where he develops open-source software for robust statistical computing.

Nic LaneView profile

Professor

• Machine Learning Systems
• Federated Learning
• Computer Architecture
• +12 more

Nic Lane is a Professor of Machine Learning Systems at the Department of Computer Science and Technology, University of Cambridge. He leads research at the intersection of machine learning systems, computer architecture, and edge computing. Research Focus : Scalable and robust end-to-end ML systems, multi-modal data processing (speech, vision, location), distributed execution, and hardware-software co-design. Teaching : Offers courses on Deep Neural Networks, Federated Learning, and Principles of Machine Learning Systems. Labs : Leads the Cambridge Machine Learning Systems (CaMLSys) group. His recent work emphasizes Federated learning for large language models Hardware-efficient AI training and inference Carbon footprint analysis of AI systems AI regulation compliance frameworks 3D rendering acceleration via neural methods

Jean-Marie DufourView profile

Professor

• Econometrics
• Macroeconomics
• Finance
• +5 more

Jean-Marie Dufour is a Professor of Economics and William Dow Chair of Political Economy at McGill University, holding roles since 2007. Previously, he was at Université de Montréal (1979–2007) and held the Canada Research Chair in Econometrics. He is a leading figure in econometrics and macroeconomics, with expertise in statistical methods for economic data analysis. His research spans structural models, volatility modeling, inequality measurement, and financial econometrics. Educations: B.Sc. (Honours) in Mathematics, McGill University (1971) M.Sc. in Statistics, Université de Montréal (1973) Ph.D. in Economics, University of Chicago (1979) Research Interests: Focuses on finite-sample econometric methods, simulation-based inference, nonparametric techniques, causality analysis in time series, and applications in macroeconomics and finance. His work emphasizes robust statistical tests and identification challenges. Publications: Over 150 articles in top journals, including Econometrica , Journal of Econometrics , and Journal of Business and Economic Statistics . Recent work addresses stochastic volatility models, Granger causality, and factor pricing in finance. Awards: Officier de l'Ordre du Canada (2008) Killam Prize for Social Science (2006) Fellowships from Royal Society of Canada, Econometric Society, and American Statistical Association Grants & Advising: Extensive grants from SSHRC and other agencies. Advised numerous students and led research groups on financial modeling and risk management. Organized major conferences like the 2015 World Congress of the Econometric Society.

Silvia GoncalvesView profile

Professor

• Econometrics
• Financial Econometrics
• Time Series Analysis
• +3 more

Prof. Silvia Gonçalves is a Full Professor in the Department of Economics at McGill University. Her research focuses on econometric theory, with specializations in bootstrap methods, time series analysis, and financial econometrics. She holds a PhD from the University of California, San Diego. Her work addresses challenges in statistical inference for dynamic models, volatility estimation, and factor models, with applications to finance and macroeconomics. Her research interests include developing robust inferential tools for nonlinear models, handling cross-sectional dependence, and improving volatility estimation under market microstructure noise. Notable contributions include advancements in bootstrap techniques for factor models and high-frequency financial data analysis. Her publications span top journals such as Econometrica , Journal of Econometrics , and Journal of Business & Economic Statistics , focusing on methodological advancements in econometric theory. Her work often bridges theory and application, addressing practical issues in macroeconomic policy analysis and financial risk management.

Jean-Yves PitarakisView profile

Professor

• Econometrics
• Predictive Accuracy Testing
• Predictive Regressions
• +3 more

Professor Jean-Yves Pitarakis is a 0.75 FTE Professor of Economics at the University of Southampton's Department of Economics. He holds a PhD in Economics from Boston University (1995) and has held prior roles at Boston University, Université Catholique de Louvain (CORE), University of Reading, and McGill University. His research focuses on econometric theory and applications, including predictive accuracy testing, model selection, and nonlinear time series analysis. He leads ESRC-funded projects on predictive accuracy and regime switching models. Education: PhD in Economics, Boston University (1995) Prior Positions: Boston University, Université Catholique de Louvain, University of Reading, McGill University His research interests emphasize econometric methodologies such as structural stability testing, threshold models, and predictive regressions. Recent work addresses challenges in nested model comparisons and heterogeneous predictor environments. He has contributed to high-impact journals like Econometric Theory and Journal of Business and Economic Statistics . Grants: ESRC Project (2022-2025) on episodic predictability and endogenous variables Professor Pitarakis is affiliated with the University's Econometrics group, contributing to methodological advancements in addressing economic and financial problems through rigorous statistical frameworks.

Indeewara PereraView profile

Lecturer

• Econometrics
• Statistical Analysis of Financial Data
• Bootstrap Methods
• +3 more

Dr. Indeewara Perera is a Lecturer in Economics at the University of Sheffield's School of Economics, specializing in econometrics. He holds a BSc (Honours) in Mathematics from the University of Colombo and a PhD in Mathematical Statistics from Monash University, Australia. His research focuses on model fitting, estimation, inference, and forecasting in non-linear time series models, particularly in financial data analysis using bootstrap methods and goodness-of-fit tests. Academic Background: University of Colombo (BSc), Monash University (PhD) Previous Positions: Research Fellow at Monash University, Visiting Research Associate at Michigan State University, Assistant Lecturer at University of Colombo His research emphasizes statistical analysis of financial data in nonstandard and massive datasets, with contributions to bootstrap-based methods in high-dimensional statistics. Key areas include ARCH/GARCH models, multiplicative error models, and panel data analysis. Awards include multiple academic medals from the University of Colombo and an Early-Career Development Fellowship from Monash University. Teaching includes courses such as ECN216 Econometrics and ECN6540 Econometric Methods. He actively supervises PhD students in theoretical/applied econometrics and statistics.

Emily WhitehouseView profile

Lecturer

• Time Series Analysis
• Financial Econometrics
• Asset Price Bubbles
• +3 more

Dr. Emily Whitehouse is a Lecturer in Economics at the University of Sheffield's School of Economics, where she has been since 2020. Previously, she served as a Lecturer at Newcastle University from 2018 to 2020. She holds a PhD in Economics from the University of Nottingham (2017), an MSc in Economics and Econometrics (2013), and a BSc in International Business, Finance, and Economics from the University of Manchester (2012). Her research focuses on time series and financial econometrics, with particular emphasis on explosive autoregressive processes, real-time monitoring of economic and financial time series, and forecast evaluation. Key areas include detecting asset price bubbles, assessing bubble regimes, and developing methodologies for bubble dating and crash prediction. She is actively involved in supervising graduate research in these domains. Teaching responsibilities include ECN104: Introductory Finance for Economics. While no scientific awards are explicitly mentioned, her work demonstrates significant contributions to econometric theory and financial market analysis. No grants or lab affiliations are detailed in the provided information.

• Machine Learning
• Electronics and Circuit Design
• Quantum Computing
• +3 more

Wei Xing is a Lecturer in the School of Mathematical and Physical Sciences at the University of Sheffield. Their research focuses on machine learning applications in electronics and energy systems, including Bayesian optimization for circuit design, quantum simulations, battery degradation prediction, and statistical modeling for yield analysis. They have contributed to advancements in multi-agent systems, surrogate modeling, and thermal analysis of 3D integrated circuits. Their work bridges theoretical innovations in machine learning with practical challenges in engineering and materials science. Key research areas include developing efficient algorithms for circuit optimization, improving predictive models for battery health, and addressing high-dimensional yield estimation in semiconductor manufacturing. Wei Xing’s publications emphasize interdisciplinary approaches, combining statistical methodologies with domain-specific expertise in nanotechnology and energy systems. While no specific awards or grants are mentioned, their extensive publication record reflects a strong focus on impactful computational methods. They are affiliated with the Hicks Building at the University of Sheffield and can be contacted at w.xing@sheffield.ac.uk.

Alex MandilarasView profile

Associate Professor

• International Finance
• Economics of Education
• Macroeconomics
• +3 more

Alex Mandilaras is an Associate Professor in the Department of Economics at the University of Surrey, where he also serves as Director of Learning & Teaching, Deputy Head of School, and Postgraduate Director for Taught Programmes. He completed his academic journey with a BSc in Economics from Athens University of Economics and Business (1996), an MA in Financial and Business Economics from the University of Essex (1998), and a PhD from the University of Surrey (2001), which subsequently appointed him as a lecturer in economics. His research agenda focuses primarily on International Finance and the Economics of Education , with extensive contributions to understanding exchange rate regimes, monetary policy, international financial stability, and fiscal policy. His work often employs advanced econometric techniques including Markov-switching vector autoregressions to analyze complex economic relationships. Mandilaras' publication record reveals a strong focus on the international policy trilemma, exchange rate regime transitions, and the relationship between fiscal policies and economic outcomes. His research spans both theoretical frameworks and empirical analyses, with particular attention to how countries navigate the constraints of monetary sovereignty, exchange rate stability, and financial openness. His more recent work has examined commodity markets, particularly oil futures, and how geopolitical shifts are affecting the global monetary system. Mandilaras has held part-time positions at NYU London and Santa Clara University in California, and has served as an external examiner for multiple UK universities including Sussex, Bradford, Essex, and London. He has been an active contributor to public discourse through platforms like The Conversation, where he has written on topics including the changing global role of the US dollar and the lasting impacts of the 2007-08 financial crisis. In terms of teaching, he delivers courses on Principles of Macroeconomics, Derivatives Markets, and Theory and Applications in Finance, demonstrating his expertise across both theoretical and applied economic domains.

Federico MartellosioView profile

Associate Professor

• Econometrics
• Spatial Econometrics
• Network Econometrics
• +3 more

Federico Martellosio is an Associate Professor in Econometrics at the University of Surrey's School of Economics. He holds a PhD in Economics from the University of Southampton (2006) and has held academic roles including Lecturer at the University of Reading (2006–2012) and PostDoctoral Fellow at the University of Amsterdam (2005–2006). His research focuses on theoretical and applied econometrics, spatial/network econometrics, hypothesis testing, and multivariate statistics. He currently serves as PhD Programme Director. Education: Bachelor's: Management, Economics, and Industrial Engineering, Politecnico di Milano (1999) MSc: Economics, University of Southampton (2001) PhD: Economics, University of Southampton (2006) Teaching: ECOD022 Topics in Econometrics (PhD) ECO2047 Introductory Econometrics (Undergraduate) ECO1005 Mathematics for Economics (Undergraduate) Research Trends: His publications emphasize spatial econometric methodologies, including shrinkage estimation for network spillovers and advanced techniques for spatial autoregressive models. Recent work addresses factor structured errors and higher-order properties of estimators in spatial contexts.

Kyunghyun ChoView profile

Professor

• Machine Learning
• Natural Language Processing
• Artificial Intelligence
• +6 more

Kyunghyun Cho is a Professor of Computer Science and Data Science at New York University (NYU), holding dual appointments at the Courant Institute and the Center for Data Science. He serves as the Glen de Vries Professor of Health Statistics and Co-Director of the Global Frontier AI Lab (with Yann LeCun). His research focuses on machine learning, natural language processing, and their applications in healthcare and biology. Cho received his PhD from Aalto University (2014) and has held positions at Facebook AI Research (2017-2020) and as a postdoc at the University of Montreal under Yoshua Bengio. He has organized major conferences including ICLR, NeurIPS, and ICML, and co-founded the Transactions on Machine Learning Research (TMLR). His awards include the Samsung Ho-Am Prize in Engineering (2021) and CIFAR Fellowship. Current projects include causal inference frameworks, antibody design via AI, and multimodal healthcare systems. Cho's work bridges foundational ML theory and practical applications in biomedicine, with recent focus on generative models, cross-cultural benchmarking (BLEND), and lab-in-the-loop protein engineering.

Nino KordzakhiaView profile

Senior Lecturer

• Statistics
• Probability Theory
• Stochastic Processes

Nino Kordzakhia is a Senior Lecturer at Macquarie University's School of Mathematical and Physical Sciences, affiliated with the Transforming Energy Markets Research Centre. Her expertise spans Statistics, Probability Theory, and Stochastic Processes. She has led five research projects between 2007–2014, including studies on Brownian motion applications, credit risk modeling, and commodity futures pricing. Her recent work focuses on parameter estimation in stochastic processes and multi-factor financial models. Key research outputs include advancements in diffusion process modeling, autoregressive error corrections, and uncertainty quantification in heterogeneous media. In 2023, she received the Faculty of Science and Engineering Award for Inter-School Collaboration. Her academic activities include international collaborations, visiting research positions at institutions like the Institute of Statistical Mathematics and Hitotsubashi University, and editorial contributions to academic journals.