Find Academic Supervisors

Akihiko NishimuraView profile

Assistant Professor

• Bayesian Statistics
• Statistical Computing
• Public Health Data Science
• +9 more

Akihiko Nishimura is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. He holds a PhD from Duke University (2017) and MS and BS degrees from Stanford University (2011 and 2010). His research focuses on Bayesian methods, statistical computing, and public health data science, with applications in precision medicine and observational health data analytics. PhD, Duke University, 2017 MS, Stanford University, 2011 BS, Stanford University, 2010 Nishimura's research centers on developing advanced statistical and computational methodologies for real-world health data. His work emphasizes Bayesian inference, large-scale computing, and software development for reproducible research. He is particularly interested in using observational health data to improve clinical decision-making and advance precision medicine. He co-leads the Bayesian Learning and Spatio-Temporal modeling group (BLAST Group) and the inHealth/OHDSI Lab , collaborating with clinicians and data scientists across institutions. His recent publications reflect a strong trend in methodological innovation in Monte Carlo methods (e.g., Hamiltonian and Zigzag samplers), scalable Bayesian inference, and applications in pharmacovigilance, diabetes management, and infectious disease modeling. The articles span disciplines including biostatistics, computational statistics, public health, and bioinformatics, demonstrating a consistent focus on high-impact, computationally intensive problems in health data science. Nishimura actively contributes to the scientific community through methodological development and open science. He develops statistical software and shares teaching materials on GitHub, emphasizing reproducibility and performant computing. His involvement in the OHDSI community enables large-scale, multi-institutional studies that would not be feasible with single-source data. His work has been recognized through publications in top-tier journals such as the Journal of the American Statistical Association , Biometrika , and JAMA Ophthalmology , and has been picked up by numerous news outlets and social media platforms, indicating broad scientific and public impact. Nishimura teaches courses on performant statistical computing and advanced Monte Carlo methods, training the next generation of data scientists in efficient algorithm and software design. He mentors students and collaborators in statistical methodology and software development, fostering a culture of rigorous, reproducible, and impactful research.

Gareth RobertsView profile

Professor

• Computational Statistics
• Stochastic Processes
• Bayesian Statistics
• +5 more

Professor Gareth Roberts is a Professor in the Department of Statistics at the University of Warwick. His research focuses on Computational Statistics, particularly MCMC methods, stochastic processes, Bayesian inference, statistical privacy, and applications in infectious disease modeling and sports analytics. He leads the OCEAN project with Eric Moulines, Michael Jordan, and Christian Robert, and teaches the ST923 lecture course on advanced statistical methods. His research interests include developing efficient sampling algorithms (e.g., MCMC, PDMP), statistical methodology for missing data, and privacy-preserving statistical techniques. Recent work emphasizes high-dimensional Bayesian models, quasi-stationary Monte Carlo, and scalability of computational methods. Publications span innovations in MCMC theory, applications to epidemiology, and sports probability modeling. His work on the Zig-Zag process and stereographic MCMC demonstrates contributions to PDMP-based sampling. Collaborations include interdisciplinary projects on bacterial transmission dynamics and statistical methods for big data. He actively participates in academic leadership, including organizing courses and contributing to the statistical community through projects like OCEAN. Contact: Gareth.O.Roberts@warwick.ac.uk .

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +4 more

Alain Durmus is a Professor at École Polytechnique, affiliated with the applied mathematics department (CMAP). His research focuses on computational statistics, machine learning, and stochastic methods, including Monte Carlo algorithms, Bayesian inference, and optimization. He explores topics such as Markov chain Monte Carlo (MCMC), stochastic approximation, and generative models. His work emphasizes theoretical guarantees for algorithms like Langevin Monte Carlo and Hamiltonian Monte Carlo, with applications to high-dimensional Bayesian inference and inverse problems. Key contributions include hypocoercivity analysis of piecewise deterministic MCMC processes, convergence guarantees for stochastic gradient methods, and the development of efficient sampling techniques. He has also contributed to Bayesian imaging and federated learning through works like the QLSD algorithm. Awarded the Best Student Paper Award at ICASSP 2020 for his work on the Sliced-Wasserstein distance. His teaching spans mathematical statistics, stochastic methods, and probability at École Polytechnique and ENS Paris-Saclay. He has also contributed to conferences and workshops on topics ranging from MCMC convergence to optimization in machine learning.

• Markov Chain Monte Carlo
• Bayesian Statistics
• Computational Statistics
• +3 more

Christophe Andrieu is a Professor in Statistics within the School of Mathematics at the University of Bristol. His research bridges theoretical probability, computational statistics, and applied mathematics, with significant contributions to Markov Chain Monte Carlo methodologies and Bayesian inference frameworks. He maintains active collaborations across engineering and data science domains. His educational background includes: M.A. from List.Natnl.Scis.App.Lyon Additional M.A. (institution unspecified) Ph.D. from Paris Andrieu's research focuses on Markov Chain Monte Carlo theory , where he develops convergence guarantees and efficiency bounds for complex samplers. His work extends to non-reversible MCMC algorithms , piecewise deterministic processes , and gradient-free optimization techniques. Recent publications demonstrate innovative approaches to state-space models and numerical integration, often addressing high-dimensional statistical challenges through stochastic approximation methods. His fingerprint reveals deep specialization in Markov chain convergence analysis and computational Bayesian statistics. His 15 most recent publications (2021-2025) exhibit consistent focus on theoretical foundations of Monte Carlo methods, particularly convergence analysis of Markov chains and novel sampler designs. Key trends include the application of weak Poincaré inequalities to pseudo-marginal MCMC, development of self-organizing state-space models, and exploration of hypocoercivity in piecewise deterministic processes. The work spans both theoretical advancements and practical implementations for engineering and statistical applications. Andrieu has secured significant research funding including: COmputational Statistical INference for Engineering and Security (COSINES) (2018-2023) New Approaches to Data Science (2018-2023) He has supervised 5 research students and maintains active collaborations in computational statistics and machine learning. His network shows strong connections with probability theory and engineering research groups.

Sam PowerView profile

Lecturer

• Computational Statistics
• Monte Carlo Methods
• Markov Chain Monte Carlo (MCMC)
• +4 more

Sam Power is a Lecturer in the School of Mathematics at the University of Bristol . He holds a PhD in Mathematics from the University of Cambridge (awarded January 2021) and an MMath. His research focuses on computational statistics, Monte Carlo methods, and probabilistic modeling. Education PhD, University of Cambridge (30 Aug 2016 – 30 Jan 2021) MMath, University of Cambridge Research Interests Dr Power’s work lies at the intersection of probability theory , statistics , and machine learning . He investigates advanced Monte Carlo techniques including Markov Chain Monte Carlo (MCMC), particle methods, and piecewise-deterministic Markov processes. His recent projects explore convergence guarantees via functional inequalities such as Poincaré and log-Sobolev inequalities, state-space models for online learning, and uncertainty quantification. Publication Trends Across 20+ publications (2019–2025), Power has consistently advanced theoretical understanding and practical performance of sampling algorithms. Key themes include error bounds for particle and gradient-based methods, weak Poincaré inequalities, and applications in machine-learning systems such as online skill rating and Bayesian active learning. Scientific Awards No awards explicitly listed in the provided material. Students & Grants No explicit information on supervised students or funded grants is present. Labs & Teams Dr Power is affiliated with the School of Mathematics at Bristol; no specific laboratory or research group name is provided.

• Computational Statistics
• Probabilistic Machine Learning
• Markov Chain Monte Carlo
• +7 more

Professor Christopher Nemeth of Lancaster University's School Of Mathematical Sciences is a leading researcher in computational statistics and probabilistic machine learning. His work focuses on Markov chain Monte Carlo (MCMC), sequential Monte Carlo (SMC), Gaussian processes, and approximate Bayesian computation, with applications in environmental science, target tracking, and econometrics. He currently holds a UKRI Turing AI Acceleration Fellowship and leads the ProbAI research hub. Research Interests: Development of probabilistic AI algorithms for large-scale learning, state-space modeling, and intersections between sampling and optimization algorithms. Grants: £9M UKRI-EPSRC ProbAI hub (2024-2029), £1.1M Turing AI Acceleration Fellowship (2021-2026), and multiple NERC grants. Academic Roles: Turing University Academic Liaison (2023-present), Associate Editor for ACM Transactions on Probabilistic Machine Learning (2023-present), and leadership roles in the Royal Statistical Society. Supervision: Completed supervision of 7 PhD students with projects on scalable Gaussian processes, Monte Carlo methods, and network modeling.

Pierre NyquistView profile

Associate Professor

• Probability Theory
• Mathematical Statistics
• Applied Mathematics
• +8 more

Pierre Nyquist is an Associate Professor and docent in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. His research is sponsored by the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). He is also an elected member of the Young Academy of Sweden for the period 2024-2029 and has served as a scientific ambassador for EURANDOM since November 2021. Dr. Nyquist's research interests lie at the intersection of probability theory, mathematical statistics, and applied mathematics. His main expertise is in probability theory, with a focus on large deviations theory and stochastic numerical methods. He has a general interest in all aspects of probability theory and much of what is categorized as applied mathematics, particularly questions related to partial differential equations, optimization, and stochastic optimal control. Recently, he has become increasingly interested in the mathematical foundations of complex data analysis and modeling, and the interplay with ideas from physics. His current research interests include large deviations, gradient flows and their generalizations, stochastic numerical methods, statistical learning theory, stochastic processes, and random dynamical systems. Pierre Nyquist has received research funding from several prestigious sources including the Swedish Research Council, the Swedish e-science Research Center (SeRC), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP). His publications demonstrate a consistent focus on theoretical aspects of probability with applications to computational methods and data analysis, showing increasing integration with machine learning techniques in recent years. elected member of the Young Academy of Sweden (2024-2029) scientific ambassador for EURANDOM (since November 2021) Dr. Nyquist is actively involved in mentoring the next generation of researchers. He currently supervises several PhD students including Cinja Arndt (starting Aug. 2025), Niki Wilhemlson (started Aug. 2024), and Viktor Nilsson (started Aug. 2020). He has previously supervised successful PhD students such as Federica Milinanni (Aug. 2020-May 2025) and Carl Ringqvist (Aug 2015-June 2021). He regularly teaches graduate-level courses including "Modern methods of statistical learning" and has supervised numerous MSc theses on topics ranging from deep learning for time-series radar signals to neural network embedding in insurance pricing. His research group is active in both theoretical developments and practical applications, with current projects spanning from mathematical foundations of probability to applications in machine learning and data science. Dr. Nyquist maintains strong international collaborations, as evidenced by his frequent travel for conferences and research visits to institutions such as Brown University and TU Delft.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +5 more

Alain Durmus is a Professor at École Polytechnique, affiliated with the CMAP (Applied Mathematics Department). His research focuses on computational statistics, machine learning, and stochastic methods for Bayesian inference and generative models. Key areas include Monte Carlo methods, stochastic optimization, and Riemannian geometry in optimization. He has contributed to theoretical advancements in Markov processes and their applications in sampling algorithms. Affiliations: École Polytechnique (CMAP), ENS Paris-Saclay (teaching roles), and other academic collaborations. Teaching: Courses on mathematical statistics, stochastic methods, probability theory, and optimization at ENS Paris-Saclay and École Polytechnique. Research Interests: Development of Monte Carlo algorithms, stochastic approximation schemes, and theoretical analysis of Markov chain convergence. Specializes in Bayesian statistics, generative models, and high-dimensional inference. His work bridges theory and application, with notable contributions to Langevin dynamics, nonreversible MCMC methods, and federated learning. He has received a Best student paper award at ICASSP 2020 for collaborative research on Bayesian computation using Sliced-Wasserstein distances. Alain’s publications span prestigious journals like the Annals of Applied Probability and SIAM, with a focus on algorithmic innovation and rigorous mathematical foundations.

Xiaolin ZhuView profile

Assistant Professor

• Probability Theory
• Stochastic Processes
• Financial Mathematics
• +3 more

Xiaolin Zhu is an Assistant Professor (Education) and Programme Director for the BSc Mathematics, Statistics and Business at the Department of Statistics, London School of Economics and Political Science (LSE). She holds a master's degree from LSE's Department of Mathematics and an undergraduate degree from the University of Melbourne. Her research focuses on probability theories of excursion processes, occupation times of piecewise deterministic Markov processes, and stochastic simulation applications in option pricing and risk measurement. She has taught undergraduate courses including MA100, ST102, ST226, and ST301 during her time as a Graduate Teaching Assistant (GTA) in both the Department of Statistics and Department of Mathematics at LSE. Xiaolin's expertise bridges theoretical probability with practical applications in finance and risk assessment. She contributes to curriculum development through her role in the BSc programme and maintains strong teaching commitments across multiple disciplines.

Andi Q. WangView profile

Assistant Professor

• Monte Carlo methods
• Bayesian inference
• Quasi-stationarity
• +7 more

Andi Q. Wang is an Assistant Professor in the Department of Statistics at the University of Warwick. He holds roles including First Year Tutor and Programme Manager of APTS (with Paul Chleboun). His research focuses on computational statistics and applied probability, particularly MCMC methods, including subgeometric convergence and nonreversible methods. He previously conducted postdoctoral research at the University of Bristol and completed his DPhil at the University of Oxford under Professors David Steinsaltz and Gareth Roberts, with a prior MMath from the University of Cambridge. Education: 2015-2019: DPhil (PhD) in Statistics, University of Oxford (OxWaSP CDT) 2011-2015: MMath (Master of Mathematics), University of Cambridge Research Interests: Monte Carlo methods, Bayesian inference, quasi-stationarity Scalable and adaptive computational methods Nonreversible processes and MCMC convergence theory His work emphasizes theoretical foundations of MCMC, including convergence analysis and algorithmic innovation. Key Research Trends: His recent articles (2023-2025) explore explicit convergence bounds for Metropolis chains, weak Poincaré inequalities, and adaptive sampling techniques. These reflect a focus on advancing MCMC methodology through rigorous probabilistic analysis and algorithmic optimization. Teaching & Engagement: Teaches ST921/ST912: Comparisons of Markov Chains Active in academic service, including organizing seminars and workshops

• Differential Privacy
• Markov Chain Monte Carlo Methods
• Generative Models
• +3 more

Andrea Bertazzi is a Researcher (POST-DOCTORANT) in the Centre de Mathématiques Appliquées (CMAP) at École Polytechnique. His work focuses on stochastic processes, differential privacy in algorithms, and computational statistics, with a particular emphasis on Markov chain Monte Carlo (MCMC) methods and piecewise deterministic models. His recent research explores the theoretical guarantees of privacy-preserving algorithms and the development of adaptive sampling techniques. Research interests include differential privacy, stochastic modeling, and numerical analysis of MCMC algorithms. He has contributed to advancing piecewise deterministic Monte Carlo methods, including their convergence properties and applications in generative models. His work bridges theoretical foundations with practical computational tools for statistical inference and machine learning. Publications highlight trends in algorithmic design for privacy, stochastic process analysis, and numerical methods. While no awards or grants are explicitly listed, his active publication record reflects ongoing engagement in cutting-edge research areas at the intersection of mathematics and computer science.

• Bayesian computation
• Monte Carlo methods
• Sequential Monte Carlo
• +4 more

Nicolas Chopin is a Professor of Data Sciences at ENSAE, Institut Polytechnique de Paris. He joined ENSAE in 2006 after serving as a lecturer at the University of Bristol (2003-2006). He holds a PhD from Université Paris VI (2003) and an HDR (habilitation) earned in 2010. His research centers on Bayesian computation methodologies, including: Sequential Monte Carlo (particle filters) Markov chain Monte Carlo Variational inference Probabilistic Machine Learning He develops computational frameworks for complex statistical inference problems. Analysis of his recent publications (2022-2025) reveals strong emphasis on: Monte Carlo innovations (e.g., waste-free SMC, quasi-Monte Carlo), scalable Bayesian modeling, debiasing techniques for sequential inference, and applications in optimization/bandit problems. Theoretical rigor combined with computational efficiency is a consistent theme. Awards: Savage Award for Best Doctoral Dissertation in Bayesian Statistics (2002)

Pierre NyquistView profile

Associate Professor

• Probability Theory
• Mathematical Statistics
• Applied Mathematics
• +7 more

Pierre Nyquist is an Associate Professor in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. Previously, he held a position at KTH Royal Institute of Technology. His research focuses on probability theory, mathematical statistics, and applied mathematics, with an emphasis on large deviations, stochastic numerical methods, and statistical learning theory. He is supported by grants from the Swedish Research Council, Wallenberg AI, Autonomous Systems and Software Program (WASP), and the Swedish e-Science Research Center (SeRC). Education: Ph.D. in Applied and Computational Mathematics (2014, advisor Henrik Hult), postdoc at Brown University (2014–2016), and assistant professor at TU Eindhoven (2016–2020). Research interests include large deviations theory, stochastic processes, gradient flows, and applications to machine learning and computational statistics. His work spans theoretical developments and practical methodologies, such as Monte Carlo algorithms and uncertainty quantification tools. Recent activities include organizing seminars, supervising PhD students, and collaborating with industry partners. He has been awarded membership in the Young Academy of Sweden (2024–2029) and serves as a scientific ambassador for EURANDOM. Key publications address topics like large deviations in stochastic approximations, Metropolis-Hastings algorithms, and neural entropy estimation. His teaching includes courses on statistical learning, probability, and Monte Carlo methods.

Andreas LöpkerView profile

Professor

• Stochastic processes
• Markov processes
• Piecewise Deterministic Markov Processes
• +6 more

Andreas Löpker is a Professor of Mathematics and Stochastics at HTW Dresden, Faculty of Computer Science/Mathematics. He teaches courses like Computer Statistics for Business Informatics , Quantitative Methods for Economists , and Mathematical/Stochastic Models for Media Informatics . Research Areas: Stochastic processes, Markov processes, Queueing theory, Risk theory, Renewal theory Publication Trends: Recent works focus on stochastic gene expression modeling, binomial thinning, fluid overflow analysis, and perturbation methods in Markov chains. Many studies connect queueing theory with risk processes.

Paul DobsonView profile

Assistant Professor

• Markov Processes
• Algorithms
• Differential Equations
• +7 more

Paul Dobson is an Assistant Professor in the Department of Actuarial Mathematics & Statistics at Heriot-Watt University's School of Mathematical and Computer Sciences, Edinburgh. His research centers on probabilistic methods and computational mathematics with applications in optimization and imaging science. His primary research areas include Markov Processes , Algorithms , Differential Equations , Convergence , Approximations , Monte Carlo Methods , and Discretization . He develops computational frameworks for Bayesian inference and stochastic systems, bridging theoretical mathematics with practical imaging solutions through algorithmic innovation. Analysis of his 2019-2025 publications reveals a cohesive trajectory: advancing optimization theory through differential equation connections (2025), enhancing Bayesian imaging via Langevin sampling (2024), solving stochastic differential equations with Poisson frameworks (2024), and extending Markov processes to infinite dimensions (2023). His work consistently appears in high-impact SIAM journals, demonstrating expertise in mathematical analysis and computational implementation. No scientific awards are documented. Dr. Dobson teaches core undergraduate courses including Probability and Statistics A (F78PA) and Probability and Statistics B (F78PB), though research grants and student advising details remain unspecified.