Denis Kojevnikov is an Assistant Professor of Econometrics at Tilburg University specializing in theoretical econometrics with focus on network dependence, cluster inference, and large Bayesian games. His research develops novel methods for analyzing cross-sectional dependence structures in complex data environments, including network-linked observations and clustered data with large group sizes. Recent publications establish impossibility results for cluster-robust inference, derive central limit theorems for network-dependent processes, and propose estimation techniques for Bayesian games with heterogeneous beliefs. Methodologically, his work bridges probability theory, statistical inference, and economic applications. Kojevnikov teaches graduate courses in econometric theory, panel data analysis, and nonparametric methods. His research contributes to foundational understanding of dependence structures in econometrics with applications to industrial organization and social network analysis.
Anirban Bhattacharya is a Professor and Patricia R. Smith and Dr. William B. Smith Faculty Fellow in the Department of Statistics at Texas A&M University, within the College of Arts & Sciences. His research focuses on Bayesian asymptotics, Bayesian nonparametrics, and high-dimensional data analysis. He holds a Ph.D. in Statistics from Duke University (2012) and Master's and Bachelor's degrees in Statistics from the Indian Statistical Institute (2008 and 2006). His academic journey includes advanced studies at prestigious institutions, blending theoretical rigor with computational innovation. Research interests emphasize robust statistical methods, scalable Bayesian computation, and applications in machine learning. Recent work explores challenges in high-dimensional inference, optimal transport, and fair clustering algorithms. Notable contributions include advancements in Gibbs posterior frameworks, constrained Gaussian processes, and variational inference techniques. He has been recognized with the prestigious Smith Fellowship, highlighting his impact in statistical methodology. His publications span foundational Bayesian theory to applied problems in physics and engineering. Bhattacharya’s work often bridges computational efficiency and statistical accuracy, addressing modern challenges in big data analysis. Ongoing research includes developing robust estimators for complex metric spaces and exploring fair machine learning methodologies.
Toryn Schafer is an Assistant Professor in the Department of Statistics at Texas A&M University, holding the 2024 ConocoPhillips Data Science Faculty Fellowship. Previously, he was a postdoctoral associate at Cornell University's Department of Statistics and Data Science, contributing to the NSF-funded PRISM Institute for Trans-domain Systemic Risk. His academic journey includes a PhD in Statistics (2020) from the University of Missouri, an MA in Statistics (2018), and a BSc in Statistics & Wildlife Biology (2014) from Colorado State University. His research interests focus on spatio-temporal statistics, reinforcement learning, Bayesian methods, and machine learning, applied to ecological and environmental systems. He emphasizes interdisciplinary approaches to address challenges in energy, sports analytics, and biodiversity conservation. His recent work explores advanced statistical methods for trend analysis, changepoint detection in time series, and the integration of community science data for ecological modeling. Notable contributions include Bayesian inverse reinforcement learning frameworks for animal behavior analysis and studies on renewable energy impacts on electricity markets. Education: PhD in Statistics, University of Missouri (2020) MA in Statistics, University of Missouri (2018) BSc in Statistics & Wildlife Biology, Colorado State University (2014) Research Themes: His publications reflect a trajectory toward methodological advancements in spatio-temporal modeling, with applications ranging from animal movement patterns to energy grid resilience. Recent articles highlight critical risk indicators for power systems and biodiversity status metrics inspired by financial portfolio theory. Grants & Fellowships: 2024 ConocoPhillips Data Science Faculty Fellowship (Texas A&M University). Labs/Teams: While specific lab affiliations are not detailed, his work intersects with interdisciplinary teams focused on ecological data science and systemic risk analysis.
Eric Chi is an Associate Professor in the Department of Statistics at Rice University, also affiliated with the Ken Kennedy Institute. He holds a B.A. in Physics from Rice University, an M.S. in Electrical Engineering from UC Berkeley, and a Ph.D. in Statistics from Rice University. His research focuses on statistical learning and numerical optimization, applied to large-scale data in biological and engineering domains. He has been recognized with awards including the NSF CAREER Award (2018) and the LeRoy and Elva Martin Teaching Excellence Award (2020). Chi's academic journey includes postdoctoral roles at UCLA and Rice, and prior faculty experience at North Carolina State University (2015-2021). He serves as an Associate Editor for the Journal of Computational and Graphical Statistics since 2016 and on the Editorial Board of Statistical Methods in Medical Research since 2011. His work emphasizes algorithmic development for complex data structures, including tensor decomposition, matrix completion, and convex clustering. Recent research trends include robust low-rank tensor decomposition, Bayesian inference via proximal MCMC, and convex-nonconvex regularization strategies. His publications span statistical computing, machine learning, and interdisciplinary applications. Chi advises current PhD students at Rice and has mentored multiple alumni now in academia and industry roles.
Professor Wicher Bergsma is a Professor in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on statistical modeling techniques such as I-priors, reproducing kernel methods, and empirical Bayes approaches. Notably, he co-developed the tau-star test for independence and advanced marginal models for categorical data analysis. He has held academic positions at LSE since 2005, preceded by postdoctoral work at Eurandom and Tilburg University, and a stint as an Assistant Professor at the Central European University. His applied work includes collaborations with industry partners like GlaxoSmithKline and Flextronics on clinical trials and fault detection systems. He is currently on sabbatical until Spring 2025. Education includes a Mathematics degree from Leiden University and a PhD in Social Statistics from Tilburg University. His expertise spans theoretical and applied statistics, with contributions to graphical models, conditional independence testing, and Bayesian variable selection. He has authored influential books and R packages, including Marginal Models and the cmm package for categorical marginal modeling. Research interests emphasize bridging theory and practice, particularly in developing robust statistical tools for complex data structures. His recent work explores I-prior methodologies for regression, causal inference with stochastic confounders, and scalable Gaussian process models for environmental data analysis.
Soonwoo Kwon is an Assistant Professor of Economics at Brown University, specializing in econometric theory and applied econometrics with a focus on robust methods. His work addresses estimation techniques in panel data models, shrinkage estimation, and measurement error correction. He has contributed to the development of the FEShR R package implementing shrinkage estimators for fixed effects models. Research spans topics like bias-aware inference, regression discontinuity designs, and parallel trends analysis, with publications in journals such as Econometrica , Review of Economic Studies , and Quantitative Economics . His research interests emphasize methodological rigor, including regularization in regression models and diagnostics for misspecified models. Collaborations include work with Timothy Armstrong, Michal Kolesár, and Sokbae Lee. Kwon's GitHub contributions reflect active development in statistical software, particularly in C++ and R for econometric applications.
Simon Shaw is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath and a member of the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa). His research focuses on Bayesian approaches, including Bayes linear methods, graphical models, and conditional independence analysis. He also explores operational research topics like nonparametric predictive inference for component replacement strategies. His research interests include the analysis of second-order exchangeable sequences, multivariate sampling techniques, and applications in economic activity assessment and climate modeling. He has contributed to methods for handling finite populations and separable covariance matrices in multivariate cluster sampling. His recent work includes advancements in generalized additive models for large datasets and adaptive age replacement strategies for maintenance optimization. He has published in journals such as the Journal of the Royal Statistical Society and the Journal of the Operational Research Society. While no specific awards are listed, his research has been cited over 240 times for notable contributions in statistical modeling and operational research. He has supervised one doctoral student but no names are provided.
Theresa Smith is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath, affiliated with centers including the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa), the Centre for Mathematics and Algorithms for Data (MAD), and the Centre for Therapeutic Innovation. Her research focuses on statistical methods for spatial and longitudinal data, with applications in health and social sciences, particularly clinical decision support tools. She holds a PhD in Statistics from the University of Washington (2014) and previously served as a Senior Research Associate at Lancaster University (2014–2016). Her research interests include spatial epidemiology, Bayesian inference, and biostatistical modeling. She leads projects such as the TULiPS study on psoriasis burden and the AI4CI Hub for collective intelligence research. Notable collaborations involve developing AI-driven solutions for public health challenges and advancing wastewater-based epidemiology for disease monitoring. Dr. Smith has secured grants from the NIHR, EPSRC, and the Royal Society, including a Short Industry Fellowship for predictive analytics in electronic health records. She advises students like N. Khaleel, whose thesis she supervised. Her work contributes to Sustainable Development Goals related to health and well-being. She is also part of interdisciplinary teams addressing environmental health risks and urban transport policy impacts.
Bo Wang is an Associate Professor in Statistics at the University of Leicester, affiliated with the School of Computing and Mathematical Sciences. He earned his PhD in Applied Mathematics from Shandong University in 2000, followed by research roles at institutions including IRISA (France), University of Glasgow, Newcastle University, and University of York before joining Leicester in 2011. His research focuses on functional and longitudinal data analysis, Gaussian process modelling, mortality forecasting, and computational statistical inference. He leads the Mathematics programme at the Leicester International Institute, Dalian University of Technology. Education: PhD in Applied Mathematics, Shandong University (2000) Wang's research interests emphasize developing statistical methodologies for complex data structures, particularly in mortality and demographic forecasting using Gaussian processes. His work integrates computational techniques to address challenges in longitudinal studies and functional data analysis. Recent contributions include innovative approaches to multipopulation mortality modelling and robust non-parametric forecasting frameworks. His publication trends highlight advancements in Gaussian process regression, functional clustering, and applications to public health (e.g., parasite prevalence studies). While no specific awards are noted, his extensive peer-reviewed output demonstrates scholarly impact. Supervision and grants sections remain unspecified in the provided data. He collaborates with interdisciplinary teams and maintains academic partnerships through his program leadership role at the Leicester-Dalian partnership.
Professor Dario Spanò is a faculty member in the Department of Statistics at the University of Warwick, serving as Deputy Head (Teaching and Learning). He holds a PhD in Mathematical Statistics (University of Pavia, 2003) and a Laurea in Economics (University of Pavia, 1998). His research focuses on combinatorial stochastic processes, Bayesian nonparametric statistics, and mathematical population genetics, with applications to genetics and evolutionary dynamics. His work includes contributions to Wright-Fisher diffusions, coalescent theory, and exact simulation methods. He has supervised numerous PhD students and co-organizes international conferences in statistics and probability. Research Interests: Stochastic processes and their applications in population genetics Bayesian nonparametric methods and their theoretical foundations Exact simulation of diffusion processes Coalescent theory and genealogical processes Recent Work Trends: Recent publications emphasize theoretical developments in diffusion models (e.g., Wright-Fisher processes), duality methods, and computational Bayesian inference for genetic data. Key themes include excursion theory, selection dynamics in random environments, and algorithmic advancements for simulating complex stochastic systems. Awards & Recognition: No scientific awards explicitly listed, though his research has been widely cited in the field. Teaching & Supervision: Current courses include ST343 and ST419 (data science topics). He has supervised over a dozen PhD students, focusing on interdisciplinary projects in statistics and mathematical genetics. His students have pursued roles in academia and industry. Labs/Teams: Active in the CRiSM research centre at Warwick, contributing to collaborative projects in statistical genetics and computational methods.
**Mayer Alvo** is a **Full Professor** in the **Department of Mathematics and Statistics** at the **University of Ottawa**. He holds an MSc from McGill University and a PhD from Columbia University. His research focuses on **Nonparametric Statistics**, **Sequential Analysis**, and **Spatial Statistics**, with applications in environmental modeling and ranking data analysis. Alvo has authored three books, including *A Parametric Approach to Nonparametric Statistics* (2018) and *Statistical Methods for Ranking Data* (2014). He has developed an **R package** *hypersampleplan* for hypergeometric distribution calculations. His research interests include analyzing trends in acid deposition in the Great Lakes, Bayesian statistics, and the application of statistical methods to big data. Alvo has supervised multiple graduate students, including **Rachid Bentoumi**, **Jingrui Mu**, and **Xiuwen Duan**. He teaches courses such as MAT 1371, MAT 2375, and MAT 4376, covering topics in probability, statistics, and applied mathematics. Alvo's recent work explores parametric embedding in nonparametric problems and change-point detection in stress-strength reliability. His contributions span theoretical advancements in ranking data analysis and practical applications in environmental science and finance. He is affiliated with the **Statistics and Probability Research Group** at the University of Ottawa.
Yanxun Xu is an Associate Professor of Applied Mathematics and Statistics at the Whiting School of Engineering , Johns Hopkins University, and an Adjunct Assistant Professor in the Division of Biostatistics and Bioinformatics at the Sidney Kimmel Comprehensive Cancer Center. She is also a member of the Data Science and Artificial Intelligence Institute . Her research focuses on developing Bayesian statistical methods and machine learning algorithms to address challenges in heterogeneous, large-scale datasets, particularly in clinical trials, electronic health records, cancer genomics, and HIV/AIDS studies. Key areas of expertise include nonparametric Bayes , reinforcement learning , and dynamic treatment regimes . Her work emphasizes precision medicine applications, such as optimizing antiretroviral therapies for HIV patients and analyzing cognitive outcomes in clinical trials. Funding sources include the National Science Foundation (NSF) , National Institutes of Health (NIH) , and industry partners like AstraZeneca. Xu has published over 80 peer-reviewed articles and received prestigious awards, including the 2016 Mitchell Prize from the International Society for Bayesian Analysis. Her research bridges statistical theory with real-world healthcare applications, addressing topics like hospital-level variations in COVID-19 treatment and the impact of inflammation biomarkers on neurocognitive functions in psychosis. PhD in Biostatistics (implied by academic rank) Recipient of JHU’s Center for AIDS Research Faculty Development Award Developed open-source software tools for causal inference and longitudinal data analysis
Isabel Valera is a full Professor in the Department of Computer Science at Saarland University in Saarbrücken, Germany, and an Adjunct Faculty member at the Max Planck Institute for Software Systems (MPI-SWS). She is also a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & ML (Sam) Unit. Department of Computer Science, Saarland University Adjunct Faculty, MPI for Software Systems ELLIS Fellow, Robust ML Program Sam Unit, Saarbrücken AI & ML She obtained her PhD and MSc from Universidad Carlos III de Madrid, followed by postdoctoral research at the University of Cambridge and MPI for Software Systems. She previously led an independent research group at MPI for Intelligent Systems in Tübingen and held the Humboldt Post-Doctoral Fellowship and Minerva Fast Track Fellowship. PhD in Machine Learning, Universidad Carlos III de Madrid, 2014 MSc in Multimedia and Communications, Universidad Carlos III de Madrid, 2012 Telecommunications Engineering, Technical University of Cartagena, 2009 Her research centers on developing machine learning methods that are flexible, robust, interpretable, and fair, particularly for heterogeneous, temporal, and high-stakes decision-making systems. She emphasizes applications in medicine, psychiatry, and social domains such as hiring, bail, and lending. Her methodological contributions include Bayesian nonparametric models, latent feature modeling, and temporal point processes. Her recent publications reflect a strong focus on fairness, robustness, and interpretability in machine learning. Key themes include latent feature modeling for mixed data types, clustering temporal event streams, source separation, and fair classification. Her work bridges theoretical innovation with practical applications across healthcare, social networks, and policy-relevant domains. Scientific awards and recognitions include: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow She has been actively involved in teaching and dissemination, delivering tutorials at NIPS and MLSS on temporal point processes and social network analysis. She has also supervised research assistants and mentored junior researchers. Her research has been supported through prestigious fellowships and institutional affiliations. She leads the development of open-source tools such as GLFM, HDHP, and iFDM, promoting reproducibility and accessibility in machine learning research. She is affiliated with the following labs and research groups: Max Planck Institute for Intelligent Systems (former group leader) Max Planck Institute for Software Systems (adjunct, postdoctoral) ELLIS Sam Unit (Saarbrücken AI & ML) Robust Machine Learning Program (ELLIS)
Anish Mukherjee is a Research Fellow at the Faculty of Economics, University of Italian Switzerland (USI) in Lugano, Switzerland, working under Professor Antonietta Mira. His role focuses on developing statistical methodologies for complex biomedical data analysis within an economics faculty context. He earned his PhD in Biostatistics from the University of Louisville under Jeremy Gaskins' supervision. His research integrates advanced statistical techniques with biomedical applications, specializing in Bayesian approaches for high-dimensional dependent data. Key methodological contributions include zero-inflated count data models, heterogeneity detection frameworks for longitudinal outcomes, and nonparametric Bayesian methods using stochastic differential equations. Applied work spans microbiome analysis related to neurological treatments, infectious disease transmission modeling, maternal health disparities, wastewater-based SARS-CoV-2 surveillance, and air pollution-COVID-19 outcome relationships.
Oke Gerke is a Professor in Clinical Biostatistics in Diagnostic Research at the Department of Clinical Research, University of Southern Denmark, and a Biostatistician at the Department of Nuclear Medicine, Odense University Hospital. He is affiliated with the Research Unit of Clinical Physiology and Nuclear Medicine in Odense and holds a DMSc from the Faculty of Health Sciences at SDU. MSc in Mathematics and Economics, University of Hamburg (1998) PhD in Statistics and Econometrics, University of Hamburg (2001) DMSc, Faculty of Health Sciences, University of Southern Denmark (2024) Lecturer Training Programme, University of Southern Denmark (2010) His research centers on the methodological foundations of diagnostic and prognostic trials in molecular imaging. He specializes in adaptive and sequential trial designs, Bland-Altman agreement analysis, ROC curve methodology, and network meta-analysis of diagnostic accuracy studies. His work bridges biostatistics, clinical epidemiology, and nuclear medicine, with applications in oncology, cardiology, and public health. He has contributed extensively to improving reporting standards in diagnostic research and statistical methodology in clinical trials. The recent articles highlight a strong trend toward methodological innovation in diagnostic research, with a focus on adaptive and seamless trial designs, real-time evaluation frameworks during outbreaks, and advanced statistical techniques for agreement and cutpoint analysis. His clinical work integrates nuclear imaging modalities like PET/CT in cancer and cardiovascular disease, supported by rigorous meta-analytic and biostatistical approaches. He is a member of the following scientific societies: International Biometric Society (IBS) International Society for Clinical Biostatistics (ISCB) Danish Society for Theoretical Statistics (DSTS) Oke Gerke has supervised 1 PhD as main supervisor and 22 as co-supervisor, with 10 completed master’s theses under his main supervision and 4 ongoing PhD projects as co-supervisor. He has been involved in research projects such as the Neurobiological effects of work-related adjustment disorder, contributing to both statistical design and analysis. While no specific grants are listed, his extensive publication record and collaborative research indicate active grant-supported work. He frequently participates in workshops, seminars, and conferences, delivering guest lectures on topics such as network meta-analysis and diagnostic test evaluation. He is actively involved in academic and clinical research teams at SDU and OUH, particularly within the Research Unit of Clinical Physiology and Nuclear Medicine. His collaborative network spans multiple disciplines, including cardiology, oncology, and psychiatric research, reflecting a multidisciplinary approach to clinical biostatistics.
