Find Academic Supervisors

Neill CampbellView profile

Professor

• Visual Computing
• Machine Learning
• Computer Vision
• +6 more

Neill D. F. Campbell is a Professor of Visual Computing and Machine Learning at the University of Bath, Department of Computer Science. He holds an honorary Associate Professor position at University College London. His research focuses on shape modeling, machine learning, and their applications in computer vision and graphics. Campbell directs the CAMERA research centre and co-leads the Centre for Mathematics and Algorithms for Data (MAD). He leads the MyWorld project, a £46M creative hub in the Bath-Bristol region. Education: PhD in Engineering (Automatic 3D Model Acquisition from Uncalibrated Images) from the University of Cambridge (2011), MEng in Engineering from the University of Cambridge (2006). Research Interests: Modeling shape and appearance using machine learning, Bayesian nonparametric models, generative models, uncertainty quantification, and applications in entertainment, health, and sports science. Active in developing structured uncertainty prediction networks (SUPN) and exploring generative models for inverse problems. Key Projects: MyWorld (Strength in Places Fund), CAMERA, EPSRC CDT in Statistical Applied Mathematics, and collaborations with industry partners. Recent work includes anomaly detection, depth estimation, and conditional sampling techniques. Awards: Royal Society Industry Fellow (2017–present). Over 54 research outputs spanning CVPR, ECCV, SIGGRAPH, and NeurIPS, focusing on structured uncertainty, generative models, and vision applications. Labs/Teams: Director of CAMERA, co-director of MAD, and collaborator in UKRI CDT in Responsible AI. Active in organizing workshops on uncertainty quantification in computer vision (UNCV).

Mengyang GuView profile

Assistant Professor

• Uncertainty Quantification
• Gaussian Process Emulation
• Statistical Calibration
• +5 more

Mengyang Gu is an Assistant Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara. His research focuses on uncertainty quantification, Gaussian process emulation, statistical calibration, and machine learning for computational science and materials physics. He has developed open-source packages like RobustGaSP, RobustCalibration, and FastGaSP for R and MATLAB. Education: PhD in Statistical Science from Duke University (2016) Research: Uncertainty Quantification, Gaussian Processes, Materials Science, Dynamical Systems Grants: NSF awards for computer model calibration, BioPACIFIC MIP funding Awards: Scialog Fellow, Hellman Fellowship, SIAM Early Career Prize, ACM Best Paper Students: Mentored PhD graduates including Xubo Liu, Yue He, Hanmo Li, and Xinyi Fang His group collaborates with materials scientists, physicists, and biologists to develop statistical methods for complex systems. Recent projects include physics-informed ML for polymer phase identification, inverse Kalman filtering, and AI-enabled materials exploration.

Rao JammalamadakaView profile

Professor

• Mathematical Statistics
• Probability
• Bayesian Analysis
• +4 more

Rao Jammalamadaka is a Distinguished Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara. His research focuses on mathematical statistics, probability, and their applications to circular data analysis, Bayesian methods, and survival analysis. He holds a PhD from the Indian Statistical Institute. Education : PhD in Statistics, Indian Statistical Institute. Research Interests : Dr. Jammalamadaka’s work spans foundational and applied statistical theory. Key areas include circular statistics, Bayesian model selection, hypothesis testing for directional data, multivariate analysis, and applications in biostatistics and actuarial science. He has pioneered methods for analyzing circular data using von Mises and wrapped stable distributions, with applications to wind direction modeling, medical imaging (e.g., OCT data), and survival analysis in pandemic studies. Advising & Grants : While specific advising details are not listed, his extensive publications suggest mentorship in statistical methodology and interdisciplinary research. Grants and funding information are not explicitly mentioned in the provided text. Labs/Teams : No specific laboratories or research groups are detailed here, though his work implies collaboration with interdisciplinary teams in biostatistics and data science.

• Mathematical Statistics
• Non-Parametric Statistics
• Bayesian Non-Parametrics
• +5 more

Paulo Serra is an Assistant Professor at the Mathematics Department of the Faculty of Science at Vrije Universiteit Amsterdam. He holds additional roles including Board Member of the MSc Internship Board for Business Analytics, Examination Board Mathematics and Business Analytics, and the Mathematical Statistics section of VVSOR. Previously, he served as Assistant Professor at Eindhoven University of Technology and held postdoctoral positions at the University of Amsterdam, University of Goettingen, and UNINOVA in Portugal. Education: PhD in Mathematical Statistics (Eindhoven University of Technology, 2009–2013) MSc in Mathematical Sciences (Utrecht University, 2006–2008) Licenciatura in Applied Mathematics (New University of Lisbon, 2000–2005) Research Interests: Focus on non-parametric mathematical statistics, including Bayesian non-parametrics, spline estimators, statistical tracking of time-varying parameters, quantile regression, and Markov processes. His work bridges theoretical foundations with practical applications in algorithm design and implementation, particularly in biomedical and engineering domains. Teaching: Teaches courses in Stochastics, Statistics for Business Analytics, and Stochastic Processes for Finance at VU Amsterdam, as well as the Mastermath Bayesian Statistics course. Provides introductory materials on Probability and Statistics using Python and Jupyter Notebook. Supervision: Currently co-supervises three PhD students, one MSc student, two BSc students, and one MSc internship. Offers thesis projects in areas aligned with his research expertise. Key Themes in Publications: Recent work emphasizes medical applications (e.g., perioperative patient deterioration prediction) and methodological advancements in robust estimation, nonparametric Bayesian techniques, and adaptive algorithms. Earlier contributions include fuzzy logic systems for spacecraft thermal monitoring and network dimension estimation in inhomogeneous graphs.

Runze LiView profile

Professor

• High-Dimensional Data Analysis
• Variable Selection
• Feature Screening
• +3 more

Runze Li is the Eberly Family Chair Professor of Statistics and Chair of Graduate Studies at Penn State University, with joint appointments in Food Science and Technology and Nutrition. He obtained his PhD from the University of North Carolina at Chapel Hill in 2000. Research Focus: Li specializes in high-dimensional data analysis, developing methodologies for variable selection, feature screening, and nonparametric modeling. His work has applications in bioinformatics, environmental science (e.g., carbon exchange modeling), and finance. Notable contributions include the distance correlation learning method for feature screening and one-step sparse estimation in nonconcave penalized likelihood models. Honors: Recipient of the UN World Meteorological Organization Gerbier-Mumm Award (2012), ICSA Distinguished Achievement Award (2017), and NSF Career Award (2004). He is a fellow of IMS, ASA, and AAAS, and has been a Highly Cited Researcher since 2014. Teaching: Instructs graduate and undergraduate courses including Multivariate Analysis (Stat 565) and Statistical Foundations of Data Science (Stat 597). Professional Service: Served as Editor of Annals of Statistics (2013-2015) and associate editor for Journal of the American Statistical Association.

Joe AusterweilView profile

Associate Professor

• Cognitive Science
• Computational Modeling
• Decision-Making
• +3 more

Joe Austerweil is an Associate Professor in the Department of Psychology at the University of Wisconsin-Madison. His research focuses on computational models of decision-making, learning, and knowledge representation, leveraging advances in computer science and statistics. He leads the Austerweil Laboratory, which investigates how people construct flexible feature representations and make decisions using semantic networks. His work bridges cognitive psychology and machine learning through empirical studies and theoretical modeling. He has contributed to open-source projects like the snafu-py library for semantic network analysis and maintains repositories for Bayesian modeling tools. Recent professional activities include planning to co-found a new Design and Science graduate school at Chiba Tech in Japan starting June 2025. Key research interests include semantic fluency, nonparametric Bayesian inference, and the application of optimal foraging theory to human cognition. His empirical work combines online and laboratory experiments to validate computational predictions, with notable publications in journals like Psychological Review and Cognitive Psychology .

Kim Jee-SeonView profile

Professor

• Multilevel Modeling
• Latent Variable Modeling
• Causal Inference
• +5 more

Jee-Seon Kim is a Professor in the Department of Educational Psychology at the University of Wisconsin-Madison, specializing in quantitative methods. She holds a BS and MS in Statistics from Ewha Womans University and a PhD in Quantitative Psychology from the University of Illinois at Urbana-Champaign. Her research focuses on developing statistical methods for social and behavioral sciences, including multilevel modeling, latent variable modeling, causal inference, and machine learning applications with clustered data. Key areas include treatment effect estimation, longitudinal data analysis, and unobserved heterogeneity. Dr. Kim has contributed to over 50 peer-reviewed publications, with recent work addressing hybrid machine learning models for heterogeneous treatment effects and causal inference strategies in clustered observational data. Her work bridges methodological innovation and practical applications, such as improving opioid use disorder treatment policies and healthcare provider capacity. Awards: Vilas Faculty Mid-Career Investigator Award (2019) Fellow, National Academy of Education/Spencer Foundation (2004) Grants & Advising: Extensive grants focused on causal inference methods, substance use disorder policy, and educational measurement. Advised graduate students on multilevel modeling and latent variable applications. Labs/Teams: Active in interdisciplinary collaborations, including opioid treatment research and psychometric method development.

• Probability
• Statistics
• Biostatistics
• +3 more

Bee Leng Lee is a Professor in the Department of Mathematics and Statistics at San José State University. She holds a Ph.D. and M.S. in Statistics from the University of Wisconsin-Madison and a B.Soc.Sci. in Statistics from the National University of Singapore. Her research focuses on probability, statistics, and biostatistics, with a strong emphasis on Bayesian methods, clinical trial design, and survival analysis. She has contributed to high-impact works such as dose-finding algorithms for phase I trials and applications of statistical methods in resolving legal disputes. Dr. Lee teaches a wide range of courses including Applied Probability and Statistics, Bayesian Data Analysis, Survival Analysis, and Computational Statistics. She has advised numerous student projects, including studies on stepwise variable selection limitations, generalized linear mixed models for categorical data, and Monte Carlo methods for financial derivatives. Her teaching also extends to foundational courses like Calculus I and Finite Mathematics. Her research projects include collaborations with industry sponsors like IBM (Almaden Research Center) on simulations and metamodeling. She actively publishes in top journals, with recent focus on balancing effective sample sizes in Bayesian designs and resolving contract disputes via statistical experimental design. Her work bridges theoretical statistics with practical applications in medicine, engineering, and public policy. Dr. Lee's contributions to statistical education and clinical trial methodology have positioned her as a key figure in advancing data-driven solutions at both academic and applied levels. She mentors students in cutting-edge statistical programming (R) and oversees special study projects that tackle real-world data challenges.

Seul ki KangView profile

Assistant Professor

• Actuarial Mathematics
• Extreme Value Theory
• Statistical Modeling
• +3 more

Seul ki Kang is an Assistant Professor in the Mathematics Department at the University of St. Thomas's College of Arts and Sciences. With dual PhDs in Risk Management and Insurance from Georgia State University and Applied Mathematics from Texas A&M, Kang's research spans actuarial mathematics, extreme value theory, and statistical modeling of financial losses. His publications focus on innovative risk analysis methodologies for insurance applications, developing multi-step approaches for ratemaking and risk assessment. Kang's computational modeling expertise includes spectral methods for analyzing flows in heterogeneous media. Courses taught include Foundations of Actuarial Mathematics (ACSC 451), covering life insurance mathematics, annuities, and premium calculations using probabilistic approaches.

Soumendra LahiriView profile

Professor

• High-Dimensional Statistics
• Machine Learning
• Resampling Methods
• +5 more

Soumendra Lahiri is a Professor and Stanley A. Sawyer Professor in Mathematics and Statistics at Washington University in St. Louis's Department of Statistics and Data Science. He earned his PhD from Michigan State University in 1989 and has held faculty positions at Iowa State University, Texas A&M University, and North Carolina State University before joining WashU in 2019. His research spans theoretical and applied statistics with cross-disciplinary impact. Lahiri's research integrates higher-order asymptotic theory , resampling methods , and high-dimensional inference . He develops novel statistical methodologies for complex data structures in neuroscience, astrophysics, and econometrics. His recent work focuses on bootstrap techniques for modern data challenges like network analysis and machine learning model uncertainty. His publication portfolio shows a consistent focus on: Advancing resampling methods for dependent and high-dimensional data Developing theoretical guarantees for machine learning algorithms Creating inference tools for spatial, temporal, and network-structured data Bridging statistical theory with applications in natural and social sciences Honors include the endowed Stanley A. Sawyer Professorship. He maintains an active research program with recent publications in statistical methodology and interdisciplinary applications, particularly in electoral modeling, network analysis, and extreme value theory.

Carlos Misael Madrid PadillaView profile

Assistant Professor

• High-dimensional Statistics
• Nonparametric Statistics
• Change Point Detection
• +3 more

Dr. Carlos Misael Madrid Padilla is Assistant Professor of Statistics & Data Science at Washington University in St. Louis, where he researches high-dimensional statistics, change point detection, and Bayesian methods. He holds a PhD in Mathematics from the University of Notre Dame and a BS from CIMAT Mexico. His recent publications focus on advanced change point detection methodologies and functional data analysis. Current research explores temporal-spatial modeling using trend filtering and neural networks.

Xuming HeView profile

Professor

• Robust Statistics
• Quantile Regression
• Bayesian Inference
• +7 more

Xuming He is the inaugural Chair of the Department of Statistics & Data Science at Washington University in St. Louis, holding the Kotzubei-Beckmann Distinguished Professorship. Previously, he served as the H.C. Carver Collegiate Professor at the University of Michigan and held roles at the National University of Singapore and the National Science Foundation. His research focuses on robust statistics, quantile regression, Bayesian inference, and interdisciplinary applications in bioinformatics, public health, and atmospheric science. Education: BS in Applied Mathematics (Fudan University, 1984); MS and PhD in Mathematics/Statistics (University of Illinois, 1988-1989). He is a Fellow of the American Statistical Association and the American Association for the Advancement of Science, and currently serves as President of the International Statistical Institute (2023-2025). His work bridges theoretical advancements in statistical methodology with practical applications in clinical trials, environmental science, and sports medicine. Notable contributions include developing scalable quantile regression techniques and promoting reproducible statistical practices through initiatives like the ASA Task Force on Statistical Significance. Awards: Kotzubei-Beckmann Professorship, ISI Presidency, ASA/AAAS Fellowships. His advising and grants focus on advancing statistical methods for complex data structures, with collaborations in concussion research and interdisciplinary data science. He leads the SDS department’s mission to integrate cutting-edge statistical theory with real-world problem-solving.

Gabriel PaternainView profile

Professor

• Differential Geometry
• Analysis
• Dynamical Systems
• +2 more

Gabriel Paternain is a Professor of Mathematics at the Faculty of Mathematics , University of Cambridge. His research focuses on Differential Geometry , Analysis , and Dynamical Systems , with particular emphasis on Geometric Inverse Problems and Geodesic Ray Transforms . Research Areas: Differential Geometry Dynamical Systems Geometric Inverse Problems Integral Geometry Partial Differential Equations Mathematical Physics Recent Work: Non-Abelian X-ray transforms Anosov surface rigidity Twistor space analysis Yang-Mills inverse problems Stochastic Bayesian inversion

Sarah FilippiView profile

Professor

• Statistical Machine Learning
• Bayesian Statistics
• Biostatistics
• +5 more

Sarah Filippi is a Professor and Reader in Statistical Machine Learning at the Department of Mathematics, Imperial College London, and Joint Director of the EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML) at Imperial and Oxford. She holds affiliations with multiple interdisciplinary networks, including the CNRS-Imperial Abraham de Moivre UMI, AI for Healthcare, and the Wound Healing and Regeneration Network. Her research focuses on Bayesian methods, nonparametric approaches, and their applications in biomedical problems, including systems biology, pharmacology, and epidemiology. Dr. Filippi earned her Ph.D. in Statistical Machine Learning from Télécom ParisTech in 2010. She has held roles at Imperial College London (2011–2014 as an MRC Fellow, 2017–present in various senior positions) and the University of Oxford (2014–2017). Her work bridges statistical theory and practical biomedical challenges, emphasizing uncertainty quantification and scalable algorithms. Her research group develops methods for causal inference, kernel-based learning, and decision-making under uncertainty. Recent publications highlight contributions to clustering algorithms, reinforcement learning, and epidemiological studies. She has received the Medical Research Council Fellowship (2011–2014) and leads collaborative projects with clinicians and computational biologists. Dr. Filippi advises a vibrant research group, including Ph.D. students working on continual learning, Bayesian optimization, and healthcare applications. Her interdisciplinary collaborations span computational biology, chronic disease modeling, and material flow analysis, reflecting her commitment to translational research.

• Machine Learning
• Spatial Statistics
• Bayesian Inference
• +3 more

James Martin is an Honorary Research Fellow in the Statistics Section of the Department of Mathematics at Imperial College London, part of the Faculty of Natural Sciences. He previously served as a Senior Teaching Fellow, leading modules on the MSc in Machine Learning and Data Science. His research focuses on machine learning applications in engineering, spatial statistics for ecological systems, and Bayesian inference methods using likelihood-free approaches, particularly sequential Monte Carlo techniques. He is affiliated with the Artificial Intelligence Network, Mathematics research and teaching staff, and the Statistics group. His work bridges theoretical statistical methodologies with practical applications in environmental and engineering domains. Recent publications (2012–2025) emphasize advancements in Bayesian computational methods, spatial point processes, and network analysis, reflecting his interdisciplinary approach to solving complex data-driven problems. While no formal advising or grant details are provided, his academic contributions underscore a commitment to advancing computational and applied statistics. He collaborates within Imperial’s mathematics and AI communities, contributing to cutting-edge research initiatives in data science.