Find and connect with academic supervisors, professors, and scholars for your research journey.
Showing 16-30 of 436 supervisors
Profile data may be stale. It is sourced from public faculty pages.
Professor
Axel Gandy is a Professor of Statistics at the Department of Mathematics, Imperial College London. He serves as Director of the EPSRC CDT in Modern Statistics and Statistical Machine Learning , overseeing PhD supervision and advanced statistical training.
Professor
Shili Lin is a Professor of Statistics at The Ohio State University's Department of Statistics, within the College of Arts and Sciences. She joined the faculty in 1995 after serving as the Neyman Visiting Assistant Professor at the University of California, Berkeley. Her expertise spans statistical genomics, bioinformatics, high-dimensional data analysis, Bayesian statistics, and Monte Carlo methods. Lin collaborates extensively with medical researchers to address challenges in genomic data such as ultra-high dimensionality, complex dependencies, and sparsity, focusing on diseases like cancer, multiple sclerosis, tuberculosis, and diabetes. She has contributed to developing computational tools for analyzing chromatin interactions, methylation patterns, and metagenomic samples. Lin holds a PhD from the University of Washington (1993). Her professional roles include serving as an Associate Editor for Biometrics , Statistical Applications in Genetics and Molecular Biology , and Statistics in Biosciences , as well as an Editorial Board member for Genetic Epidemiology . She is a standing member of NIH's Biostatistical Methods and Research Design Study Section and has served on multiple NSF and NIH grant review panels. Additionally, she is President Elect of the Caucus for Women in Statistics and has been a member of the ASA Committee on AAAS representation for six years. Her research interests emphasize statistical methodologies tailored to genomic data, including model selection, epigenetic analysis, and integrative approaches for multi-omics data. Lin's work often combines theoretical advancements with practical applications, such as predicting relapse in immune-mediated disorders and improving imputation techniques for single-cell Hi-C analysis. She has pioneered software tools like TopKLists and GrammR to facilitate ranked list aggregation and metagenomic data analysis. Lin's scientific accolades include ASA Fellowship (2004), AAAS Fellowship (2009), and membership in the International Statistical Institute (2014). Her contributions to statistical genetics and epigenomics have been recognized through grants and editorial leadership roles. While her research group focuses on cutting-edge methods, no formal advisees or students are explicitly listed in the provided materials.
Professor
Richard Nickl is a Professor of Mathematical Statistics at the University of Cambridge, affiliated with the Department of Pure Mathematics and Mathematical Statistics (DPMMS) and the Statistical Laboratory. His research focuses on high-dimensional inference, Bayesian nonparametrics, statistics for partial differential equations, and inverse problems. He has held significant grants, including an ERC Advanced Grant (2024–2029) and an EPSRC Programme Grant (2022–2027). His work bridges statistics, probability, and analysis, with contributions to theoretical foundations and computational methods in non-linear inverse problems. Key research interests include Bayesian posterior consistency, statistical inference for diffusions, and polynomial-time algorithms for high-dimensional posteriors. Notable publications include foundational monographs such as Mathematical foundations of infinite-dimensional statistical models (2016), which earned a PROSE Award, and recent advancements in Bayesian nonparametric inference for McKean-Vlasov models (2025). His group organizes workshops, such as the 2024 Statistical Aspects of Non-Linear Inverse Problems conference. Awards: 2017 PROSE Award in Mathematics. Grants: ERC Advanced Grant, EPSRC Programme Grant. Lab/Team: Research Group in Mathematical Statistics at DPMMS, focusing on inverse problems and Bayesian methodology.
Associate Professor
Ying MacNab is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. She holds an additional affiliation as an Associate Member in the School of Population and Public Health (SPPH). Her research focuses on Bayesian hierarchical modeling, spatial epidemiology, and disease mapping with applications to public health surveillance and aging populations. She has contributed extensively to methodological advancements in Gaussian Markov random fields and spatiotemporal modeling frameworks. Her work bridges statistical theory and practical health challenges, including pandemic-related stress in older adults, opioid treatment outcomes, and infectious disease forecasting. MacNab has collaborated on projects involving mental health assessments (e.g., sleep dysfunction, anxiety/depression in iOAT patients) and has developed novel statistical tools for analyzing spatially and temporally correlated health data. Her research also addresses methodological gaps in coregionalized multivariate models and constrained Bayesian estimation. MacNab's publications reflect a multidisciplinary approach, integrating epidemiological theory with advanced computational methods. Recent trends in her work emphasize dynamic modeling of infection risks, mediation analysis in aging populations, and validation of psychometric scales for health-related stress. She has maintained an active research agenda since the early 2000s, with notable contributions to neonatal health outcomes, injury surveillance, and healthcare quality improvement.
Professor
Ingo Bojak is a Professor at the School of Psychology and Clinical Language Sciences , University of Reading. His research focuses on computational neuroscience, neurodynamics, and neural population models. He explores topics such as biological mistake-making, EEG analysis, and the effects of anesthesia on brain activity. Bojak serves as an associate editor for Neurocomputing and related journals. His work bridges theoretical frameworks with experimental data, emphasizing cross-scale biological phenomena and neural network dynamics. Bojak’s research interests include understanding spontaneous neural oscillations, cortical activity modeling, and the integration of EEG/fMRI data. He has contributed to advancements in neural field theory and Bayesian uncertainty quantification. His studies on biological mistakes highlight adaptive mechanisms across biological systems. Related affiliations include collaborations with the School of Biological Sciences at the University of Reading. Recent publications emphasize theoretical biology, computational neuroscience, and interdisciplinary approaches to understanding neural systems. His work often addresses functional adaptation through error-driven mechanisms and explores the interplay between neural excitability and inhibition.
Assistant Professor
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.
Professor
Keming Yu is a Professor and Chair in Statistics at the Department of Mathematics, Brunel University London, within the College of Engineering, Design and Physical Sciences. He is also the Impact Champion for REF in Mathematical Sciences. He joined Brunel in 2005 after holding positions at the University of Plymouth, Lancaster University, and The Open University. He earned his PhD from The Open University and earlier degrees in Mathematics and Statistics from Chinese institutions. PhD in Statistics – The Open University, UK MSc in Statistics – China BSc in Mathematics – China His research centers on quantile regression, Bayesian modeling, survival analysis, and statistical methods for big data . His work spans applications in health, finance, environment, and social sciences. He has made significant contributions to robust and flexible regression methods, including expectile, mode, and censored quantile regression. His recent publications (2023–2025) show a strong focus on streaming data, spatiotemporal modeling, high-dimensional data, and Bayesian methods . He frequently publishes in top-tier journals such as the Journal of the Royal Statistical Society Series A, B, and C , Statistica Sinica , and Computational Statistics and Data Analysis . His work often involves collaboration with international researchers, especially in China and Europe. He has contributed to methodological discussions in leading statistical journals, demonstrating active engagement with the academic community. His work on financial risk, environmental statistics, and health data analysis reflects interdisciplinary impact. Reviewed and contributed to discussions on safe testing, confidence sequences, and betting-based inference. Active in developing methods for nonignorable missing data, censored models, and functional covariates. He supervises PhD students and is involved in teaching and curriculum development, including as Course Director for the MSc Statistics with Data Analytics. His research is supported by extensive publication output and academic service. He leads or contributes to research on Bayesian models, robust regression, and scalable methods for big data , often involving collaborations in interdisciplinary teams. His lab or research group focuses on statistical methodology development with real-world applications.
Professor
David M. Higdon is a Professor and Department Head of the Department of Statistics at Virginia Tech within the College of Science. He specializes in Bayesian statistical modeling of environmental and physical systems, focusing on integrating physical observations with computer simulations for prediction and inference. Previously, he spent 14 years at Los Alamos National Laboratory as a scientist and group leader in the Statistical Sciences Group. Education: Ph.D. in Statistics, University of Washington, 1994 M.A. in Mathematics, University of California San Diego, 1989 B.A. in Mathematics, University of California San Diego, 1987 Research Interests: Higdon’s work spans space-time modeling , inverse problems in hydrology and imaging , statistical modeling in ecology and environmental science , and multiscale models . He develops methods for parallel processing in posterior exploration , statistical computing , and Monte Carlo simulations . His research addresses critical challenges in uncertainty quantification (UQ), including climate modeling, nuclear density functional theory, and geophysical imaging. Publications Trends: His recent articles emphasize Bayesian methodologies applied to complex systems, such as climate forecasting, materials science, and cosmology. A recurring theme is the development of emulators and surrogate models to handle computationally intensive simulations. Awards: Fellow of the American Statistical Association Advising & Grants: While no specific advisees are listed, Higdon has contributed to interdisciplinary collaborations in UQ and statistical modeling. His work has been supported by grants from agencies such as the National Science Foundation and Department of Energy. Labs/Teams: He leads the Statistics Department’s efforts in UQ and computational statistics, fostering collaborations across engineering, environmental science, and physics.
Professor
Radu Craiu is a Professor in the Department of Statistical Sciences at the University of Toronto. Born in Bucharest, Romania, he earned his PhD in Statistics from the University of Chicago in 2001 under the supervision of Xiao-Li Meng. He joined the University of Toronto as an Assistant Professor, was promoted to Associate Professor in 2006, and to Full Professor in 2013. He served as Department Chair from 2018–2022 and 2023–2024. Education: PhD in Statistics (2001), University of Chicago MS in Mathematics (1996), University of Bucharest BS in Mathematics (1995), University of Bucharest His research focuses on computational methods in statistics, particularly Markov chain Monte Carlo (MCMC) algorithms, Bayesian inference, copula models, model selection, and statistical genetics. He has collaborated on interdisciplinary projects in genetics, ecology, astronomy, and demography, emphasizing flexible statistical modeling and efficient computational techniques. Radu Craiu has received prestigious awards, including the CRM-SSC Prize (2016), CJS Best Paper Award (2019), and fellowships from the American Statistical Association (2022), Institute of Mathematical Statistics (2020), and International Statistical Institute (2015). He actively contributes to editorial roles for journals like the IMS Bulletin and Harvard Data Science Review .
Assistant Professor
Leland Bybee is an Assistant Professor of Finance at the University of Chicago Booth School of Business . He leverages machine learning and natural language processing to address economic and financial questions, particularly focusing on belief measurement with applications to asset pricing and behavioral economics. Ph.D. in Financial Economics, Yale School of Management (2024) M.S. in Statistics, University of Michigan (2017) B.A. in Economics, University of Chicago (2013) His research integrates computational methods with economic theory to analyze: Textual analysis of business news for macroeconomic tracking Narrative-driven asset pricing models Memory-based belief formation using kernel methods Macroeconomic determinants of currency returns He has received multiple awards including: Dimension Fund Advisors Distinguished Paper Award BlackRock Applied Research Award HEC Top Finance Graduate Award The Brattle Group PhD Candidates Award EFA Engelbert Dockner Memorial Prize Bybee teaches Machine Learning in Finance and participates in finance seminars, contributing computational tools like regIPCA (Python) and changepointsHD (R) to the research community.
Assistant Professor
Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.
Assistant Professor
Xiaoxiao Zhou is an Assistant Professor in the Department of Biostatistics at the University of Alabama at Birmingham (UAB), affiliated with multiple centers including the Center for Outcomes and Effectiveness Research and Education (COERE), Center for Clinical and Translational Science (CCTS), and the Global Center for Craniofacial, Oral and Dental Disorders (GC-CODED). She holds a PhD in Statistics from The Chinese University of Hong Kong (2022) and completed a postdoctoral fellowship at Duke University's Department of Statistical Science. Her research focuses on causal inference, Bayesian methods, longitudinal data analysis, and survival analysis, with applications in Alzheimer’s disease, cardiovascular conditions, and neurodegenerative disorders. Dr. Zhou’s work integrates advanced statistical techniques with medical and behavioral data, including neuroimaging and latent variable modeling. Key areas include handling intercurrent events in clinical trials, causal mediation analysis, and joint modeling of longitudinal and survival outcomes. She collaborates widely with clinicians and biostatisticians to address real-world challenges in healthcare and disease progression studies. Her scholarly contributions span over a dozen peer-reviewed articles, emphasizing methodological innovations in biostatistics and their practical applications. She advises students such as Zhenying Ding and actively participates in academic committees. Outside academia, she enjoys outdoor activities like mountain hiking and weight lifting.
Professor
Chuanhai Liu is an Associate Head and Professor of Statistics at Purdue University’s Department of Statistics. He specializes in computational methods for statistical inference, Bayesian analysis, and big data computing systems. His research emphasizes robust algorithms, inferential models, and probabilistic methods. Education: M.S., Wuhan University, Probability and Statistics (1987) M.A., Harvard University, Statistics (1990) Ph.D., Harvard University, Statistics (1994) Research Interests: Computational methods (EM algorithms, MCMC), statistical software systems for big data, theoretical foundations of inference, and prior-free probabilistic modeling. His work bridges computational efficiency and statistical validity, with applications in network analysis, environmental data, and machine learning. Awards: Fellow of the American Statistical Association (2007) Elected Member of the International Statistical Institute (2006) Frank Wilcoxon Prize (2000) Advising & Grants: Supervised 9 Ph.D. students, including interdisciplinary collaborations. His grants focus on distributed statistical computing and inferential frameworks. Active in software development for large-scale data analysis. Labs/Teams: Leads statistical computing initiatives at Purdue, contributing to scalable algorithms and probabilistic inference systems.
Assistant Professor
Ryan Giordano is an Assistant Professor in the Department of Statistics at the University of California, Berkeley. He holds a PhD in Statistics from UC Berkeley (2019), advised by Michael Jordan, Tamara Broderick, and Jon McAuliffe, an MSc in Econometrics and Mathematical Economics from the London School of Economics (2009), and undergraduate degrees in Mathematics and Theoretical/Applied Mechanics from the University of Illinois at Urbana-Champaign. Prior to academia, he worked as an engineer at Google and HP and served as a Peace Corps volunteer in Kazakhstan. His research focuses on variational methods , Bayesian robustness , sensitivity analysis , and statistical computing , with applications in machine learning, environmental science, and astronomy. He is particularly known for developing scalable Bayesian inference techniques and quantifying the robustness of statistical models to data perturbations. Giordano’s recent work includes studies on Laplace approximation accuracy, MCMC sensitivity to data removal, and robustness metrics for differential expression analysis. He has contributed to open-source statistical software and collaborates with Tamara Broderick’s group at MIT on postdoctoral work (pre-2019 position). His academic trajectory combines theoretical innovation with practical applications, emphasizing reproducibility and computational efficiency in statistical methodology.
Lecturer
Alex Shestopaloff is a Lecturer in Statistics at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences. Previously, he was a Research Fellow at the Alan Turing Institute (2017–2020) and a Junior Research Fellow at Campion Hall, Oxford. He holds a PhD in Statistics from the University of Toronto (2016), supervised by Radford M. Neal. His research focuses on developing efficient MCMC methods, high-dimensional time series analysis, network science, and applications in financial market microstructure. Education: PhD in Statistics, University of Toronto (2016) Supervisor: Radford M. Neal Research Interests: Bayesian online learning in non-stationary environments Limit order book modeling and trading strategies Graph clustering and network analysis Statistical methods for high-dimensional data Algorithmic trading and cryptocurrency markets His recent work spans financial engineering, machine learning, and statistical methodologies. Notable contributions include cluster-based trading strategies (ClusterLOB), generalized Bayesian filtering frameworks, and scalable graph analysis techniques. Collaborations with industry partners (e.g., Wise Plc) highlight applied research in financial systems. Advising & Alumni: Current advisees include Yichi Zhang (Oxford), Maria Fernanda Pintado (QMUL), and Dave Lui (Oxford) Alumni: Gerardo Duran-Martin (Postdoc at Oxford-Man Institute), Claudio Bellani (Citadel Securities) Labs/Teams: Leads interdisciplinary projects at QMUL and collaborates with the Alan Turing Institute on financial and network science initiatives.
