Zhou Fan is an Associate Professor in the Department of Statistics and Data Science at Yale University, specializing in mathematical statistics, probability theory, and computational algorithms with applications in statistical genetics and computational biology. Education: Ph.D. in Statistics, Stanford University, 2018 His research spans Random matrices and free probability , Statistical physics and inference , High-dimensional statistics and machine learning , and Applications in genetics and computational biology . He develops theoretical frameworks for complex data analysis, focusing on inferential problems in scientific contexts through advanced computational methods. Recent publications demonstrate leadership in Approximate Message Passing algorithms, empirical Bayes methods, and group orbit estimation, with significant contributions to high-dimensional statistics and biological applications. His work bridges statistical theory with practical computational solutions for modern data challenges. As Co-Director of Graduate Studies, Professor Fan provides academic leadership for the department's graduate program while teaching advanced courses in high-dimensional probability, statistical theory, and random matrix applications.
Professor Dankmar Böhning holds a Chair in Medical Statistics at the University of Southampton, affiliated with the Southampton Statistical Sciences Research Institute (S3RI) as Deputy Director. His expertise spans mathematical sciences and medicine, with a focus on meta-analysis, capture-recapture methods in health/social sciences, count data analysis, and mixture models. Education: Bielefeld and Berlin universities (Mathematics/Social Sciences), PhD (1981) and Habilitation (1992) from Free University Berlin. Career highlights include roles as Chair in Statistics at University of Reading (2005–2011) and visiting positions at institutions in Vienna, Graz, PennState, Bangkok, and Manila. Research interests include applying statistical methods to healthcare challenges, including epidemiological studies and clinical trials. He leads projects like CLASP Cancer and capture-recapture for contact-tracing efficacy. Active in teaching statistical inference and epidemiology courses at undergraduate and postgraduate levels. Key achievements: Bualuang ASEAN Chair Professor Prize (2022), editorial roles (e.g., Biometrical Journal), and leadership in national/international research collaborations. His recent work focuses on pandemic modeling (Covid-19) and improving diagnostic testing strategies.
Professor Simon Burgess is a Professor of Economics at the School of Economics, University of Bristol . He is a Fellow of the British Academy and a Research Fellow at the Institute for the Study of Labor (IZA) . His academic career includes directing major research centers such as the Centre for Market and Public Organisation (CMPO) and the Centre for Understanding Behaviour Change (CUBeC) , with a focus on education policy and social equity. Education: MA (Cambridge), DPhil (Oxford) Research Interests : Burgess specializes in the economics of education, examining teacher effectiveness, pupil motivation, school accountability, and ethnic segregation in schools. He also explores broader topics like unemployment, poverty, and public sector incentives. Recent Publications emphasize school performance metrics, classroom time allocation, and gender gaps in STEM education. Key themes include Education Policy , Labour Market Dynamics , and Social Equity , with methodological strengths in experimental economics and statistical modeling. Scientific Awards : Fellow of the British Academy Research Fellow, IZA Advisory Roles : Burgess chairs the Somerset Challenge (2016-2017) and serves on multiple advisory boards, while also contributing to public policy discussions on education reform.
Jaouad Mourtada is an Assistant Professor in the Department of Statistics at ENSAE Paris, a founding member of the Institut Polytechnique de Paris, and a permanent member of CREST (Center for Research in Economics and Statistics). Since September 2020 he has held this faculty position, after completing a post-doctoral fellowship at the University of Genoa and earning his PhD from École Polytechnique. Education PhD in Statistics, École Polytechnique (2016–2019) MSc in Mathematics, "Probability and Random Models", Université Pierre et Marie Curie (2016) MSc in Mathematics, "Fundamental Mathematics", Université Pierre et Marie Curie (2015) BSc in Mathematics, Université Pierre et Marie Curie & École Normale Supérieure (2013) Student at École Normale Supérieure (2012–2016) Research Interests Mourtada’s work lies at the intersection of statistics and learning theory, with a focus on understanding the fundamental complexity of prediction and estimation tasks. His interests span: High-dimensional statistics and minimax theory Statistical learning theory and generalization bounds Online learning, regret minimization, and expert aggregation Density estimation and robust statistics Random forests, kernel methods, and convex optimization Research Output Trends Across more than fifteen recent publications, Mourtada has systematically advanced the understanding of statistical and computational limits in learning. His contributions range from exact minimax analyses of linear least squares and novel robust regression guarantees to refined PAC-Bayesian bounds for aggregation and sharp asymptotics for ridge regression. A recurrent theme is the development of estimators that achieve optimal or near-optimal rates while remaining computationally tractable and adaptive to unknown parameters. Scientific Awards & Recognition While the provided materials do not list specific awards, his sustained publication record in top venues such as Annals of Statistics , Journal of Machine Learning Research , Journal of the European Mathematical Society , and leading ML conferences (NeurIPS, COLT, AISTATS) attests to significant peer recognition. Teaching & Mentoring Mourtada has extensive teaching experience at both undergraduate and graduate levels, covering probability, statistics, and machine learning. Courses delivered include: Statistical Learning Theory (M2 Data Science, École polytechnique & ENSAE) Probability Theory (ENSAE) Python for Probability, Statistics, and Machine Learning (École polytechnique) Optimization for Data Science (M2 Data Science, École polytechnique) Laboratories & Collaborations He is affiliated with CREST/ENSAE and has previously collaborated with the Laboratory for Computational and Statistical Learning at the University of Genoa, the Center for Applied Mathematics (CMAP) at École Polytechnique, and maintains ongoing research ties with international scholars in statistical learning and optimization.
Dr. Laura Kubatko is Professor of Statistics and Evolution, Ecology and Organismal Biology at The Ohio State University, with joint appointments in both departments. She holds affiliate positions at the Battelle Center for Mathematical Medicine and Translational Data Analytics Institute. Awarded AAAS Fellowship in 2019, she served as President of the Society of Systematic Biologists (2020-2022) and co-directed the Mathematical Biosciences Institute. Her research integrates statistical genetics and phylogenetics, focusing on coalescent theory methods for phylogenetic tree inference, hybridization detection, and algebraic statistics applications. She develops computational tools for analyzing genome-scale evolutionary data. Dr. Kubatko's work spans conservation biology applications, viral evolution analysis (including SARS-CoV-2), and cancer progression modeling. Publications demonstrate consistent focus on methodological innovation in phylogenetic inference under complex evolutionary scenarios. Leadership & Recognition: AAAS Fellow (2019) President, Society of Systematic Biologists (2020-2022) Associate Editor: Systematic Biology (2007-2020), Evolution (2008-2010) Section Editor: BMC Ecology and Evolution (2016-2022)
Kun Liang is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, part of the Faculty of Mathematics. His research focuses on large-scale inference, statistical genetics, high-dimensional statistics, and machine learning with applications in bioinformatics and genomics. Education: Ph.D. in Statistics, Iowa State University M.S. in Statistics, Iowa State University M.S. in Automation, Tsinghua University B.E. in Automation, Tsinghua University Research Interests: His work emphasizes statistical methodologies for genomic data analysis, including false discovery rate control, ChIP-seq analysis, and integration of auxiliary information in RNA sequencing. He develops computational tools for gene ontology analysis and biomarker discovery in autoimmune diseases like psoriatic arthritis. Publications: Recent work includes advancements in directional hypothesis testing, grouped false discovery rate control, and proteomic profiling of inflammatory arthritis. His research bridges statistical theory and biomedical applications, with contributions to malaria studies and epigenetic regulation analysis. Awards: No awards explicitly listed in the provided texts. Advising & Grants: Advising details not specified here; his grants likely focus on statistical genomics and bioinformatics, though explicit mentions are absent. Labs/Teams: Engaged in interdisciplinary collaborations at the University of Waterloo, particularly in bioinformatics and statistical genetics.
Termeh Shafie is a full-time Professor in the Department of Politics and Public Administration at the University of Konstanz, specializing in Computational Social Science and Data Science. With a strong statistical foundation, she develops advanced methodologies for analyzing multivariate social networks while bridging archaeological network reconstruction with modern data science techniques through projects like NEXUS 1492. Key Research Areas: Multigraph modeling, network entropy analysis, isotope geoprovenance, and hypergraph representations Projects: NEXUS 1492 archaeological network reconstruction Her 15 most recent publications demonstrate significant contributions to network methodology (random multigraph models, centrality index analysis), archaeological applications (Caribbean attack networks, Iroquoian settlement patterns), and data privacy frameworks. Articles span 2012–2025 with interdisciplinary focus on statistical sociology, archaeological theory, and computational modeling. Current teaching includes courses on social network analysis, data science, and statistical learning. Office hours available via ILIAS booking system. Contact details provided for both academic and administrative correspondence.
Dr. Quefeng Li is an Associate Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill (UNC-Chapel Hill), part of the Gillings School of Global Public Health. He earned his Ph.D. in Statistics from the University of Wisconsin-Madison (2013) and completed a postdoctoral fellowship at Princeton University (2013–2015). His research focuses on high-dimensional statistics and its applications in biomedical and neuroimaging data analysis, emphasizing robust methodologies and integrative data analysis techniques. Dr. Li's work has contributed to advancements in variable selection, data integration, and statistical methods for complex biomedical datasets. He has held editorial roles at journals like Biometrics and Biostatistics , and serves on multiple professional committees. His research has been supported by grants from the NIH, including R01AG073259 and R01CA282648, and he collaborates extensively with clinical researchers across disciplines. His research interests span high-dimensional statistics, neuroimaging analysis, and robust statistical methods. Notable contributions include developing the ARTdeConv algorithm for cell-type deconvolution and the glmmPen package for penalized generalized linear mixed models. He has advised numerous PhD students, many of whom now work in industry and academia. Awardees include the Gillings Research Excellence Award (2023), Junior Faculty Development Award (2018), and University Research Council Award (2016). His work bridges theoretical statistics with practical biomedical applications, addressing challenges in data heterogeneity and reproducibility.
Eliana Duarte is an Assistant Professor in Probability and Statistics at Universidade do Porto, where she conducts interdisciplinary research at the intersection of statistics, algebraic geometry, commutative algebra, and combinatorics. Her work focuses on algebraic and geometric methods in statistical modeling, particularly in discrete models, graphical models, and tensor product surfaces. Her research interests include Algebraic Statistics , Graphical Models , Discrete Statistical Models , Toric Varieties , Implicitization , and Polynomial Systems . She applies algebraic techniques to understand the structure of statistical models and their maximum likelihood estimators, with recent work on decomposable models, polytree learning, and rational linear precision in higher-dimensional polytopes. The trend in her recent publications (2016–2024) reflects a consistent focus on the algebraic foundations of statistical models, combining symbolic computation with geometric insight. Her work spans pure mathematical theory and applications in causal inference, microbiome modeling, and geometric design. Key themes include the use of syzygies, toric fiber products, and virtual resolutions in modeling and implicitization. Scientific Awards: No awards listed in the provided text. Advising and Grants: Dr. Duarte advises graduate students in statistics and algebraic methods, although specific advisee names are not listed. She is involved in multiple research projects related to algebraic statistics and probabilistic modeling. While no specific grants are mentioned, her sustained publication record suggests active research funding. Labs and Teams: No specific laboratory or research team name is provided in the text. However, her collaborative publications indicate active participation in interdisciplinary research networks, particularly in algebraic statistics and computational geometry.
Josh Speagle is an Assistant Professor jointly appointed in the Department of Astronomy & Astrophysics and the Department of Statistical Sciences at the University of Toronto. He maintains strong affiliations with the Dunlap Institute for Astronomy & Astrophysics, which forms part of Canada's leading concentration of astronomers at the University of Toronto. Dr. Speagle received his Ph.D. from Harvard University in 2020, establishing a foundation for his interdisciplinary career at the intersection of astronomy and statistics. His educational background bridges these two critical fields for modern data-intensive astronomy. His research focuses on astrostatistics and data science with multi-wavelength, large-area surveys, with specific interests in Galactic structure and dynamics; stars and stellar populations; dust and the interstellar medium; galaxy formation and evolution; scalable inference; and Monte Carlo sampling. Dr. Speagle combines astronomy, statistics, and computer science to analyze billions of stars and galaxies from wide-field imaging and spectroscopic surveys to better understand how galaxies like the Milky Way form, behave, and evolve over time. His recent publications demonstrate a strong emphasis on developing and applying advanced statistical methods to astronomical problems, particularly simulation-based inference, Bayesian methodology, 3D dust mapping, and stellar population analysis. His work frequently leverages major astronomical datasets from Gaia, DESI Legacy Imaging Surveys, and the James Webb Space Telescope. Banting-Dunlap Postdoctoral Fellowship Dr. Speagle collaborates extensively with researchers across the University of Toronto's three campuses and internationally. His work has significant implications for understanding galactic evolution and developing statistical frameworks for astronomical data analysis. He contributes to the vibrant research environment at the Dunlap Institute, which includes over 50 faculty, postdocs, students, and staff dedicated to innovative technology, groundbreaking research, and world-class training in astronomy. As a member of the Dunlap Institute community, Dr. Speagle participates in research spanning optical, infrared and radio instrumentation; Dark Energy studies; large-scale structure; the Cosmic Microwave Background; the interstellar medium; galaxy evolution; cosmic magnetism; and time-domain science.
Boris Beranger is a Senior Lecturer in Statistics and Data Science at the School of Mathematics and Statistics, UNSW Sydney . He is also a member of the UNSW Data Science Hub (uDASH) and previously served as an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) . His research spans theoretical and applied statistics, focusing on Extreme Value Theory (environmental, financial, and insurance applications) and Symbolic Data Analysis (complex/non-standard data structures). Education: PhD in Statistics (Université Pierre and Marie Curie & UNSW, 2016), MSc in Mathematics (Université Pierre and Marie Curie, 2011) Research Trends are evident in: High-dimensional extremal dependence modeling (ExtremalDep package) Spatial extremes and max-stable processes Symbolic/histogram/interval-valued data analysis Composite likelihood and aggregated data methods Tail density estimation via kernel methods Scientific Awards & Grants include: J.B. Douglas Award for Postgraduate Excellence (2014) Multiple ARC ACEMS Research Support Schemes Discovery Project DP220103269 ($405,000) for modeling real-world extremes Supervision covers PhD, Masters, and Honours students in areas like Symbolic Data Analysis, Spatial Extremes, and Statistical Computing. He also co-organized workshops and served as Vice-President (2025-26) of the Statistical Society of Australia's NSW Branch.
Patrick Forré is an Assistant Professor and Lab Manager of the AI4Science Lab at the Informatics Institute, Faculty of Science, University of Amsterdam. His work bridges theoretical machine learning and scientific applications, fostering interdisciplinary collaboration across informatics, mathematics, ecology, chemistry, physics, biology, and astrophysics. His research centers on mathematical foundations of machine learning including causal inference, graphical models, information theory, conditional independence structures, and geometric deep learning. He specializes in applying these techniques to scientific data problems, particularly in electro-catalysis and nitrogen fixation, where machine learning enhances molecular simulations and quantum chemical modeling. His theoretical work addresses non-linear structural causal models with cycles and latent confounders. The AI4Science Lab under his management focuses on detecting hidden patterns in scientific data through projects like electrode-electrolyte interface modeling, nitrogen-fixing coordination complexes analysis, and classical DFT neural approximations. Located in LAB42 Building at Amsterdam Science Park, the lab connects diverse scientific disciplines through machine learning innovation while organizing colloquia, workshops, and PhD defenses.
Xueyu Song is a Professor of Chemistry at Iowa State University, affiliated with the Ames Laboratory of the U.S. Department of Energy. Their research focuses on theoretical and computational tools for chemical reactions in chemical and biochemical systems. Ames Laboratory of the U.S. Department of Energy Department of Chemistry, Iowa State University Education: Postdoctoral Fellow, University of California, Berkeley (1995-1998) Ph.D., California Institute of Technology (1995) Research Interests: The group develops theories for electron transfer in solutions and inhomogeneous materials, solvent effects on chemical reactions, solvation dynamics in protein environments and ionic fluids, phase behaviors of metallic systems, nucleation kinetics, protein-protein interactions, and protein crystallization. Recent work integrates machine learning with supercooled liquids and extends the Debye-Hückel theory beyond dilute solutions. Sub-diffraction imaging using heterodyne and entangled photons is also studied. Recent Article Trends: Publications span 2019-2024, focusing on solvation dynamics in ionic fluids, nanodomain analysis via single-molecule tracking, machine learning for heterogeneities in supercooled liquids, extensions of Debye-Hückel theory, and phase behavior in metallic systems. Computational methods like density functional theory, molecular dynamics, and Bayesian inference are key tools. Contact: Email xsong@iastate.edu , Phone: 515-294-4383, Office: 303 Wilhelm, 2332 Pammel Dr, Ames, IA 50011-1025.
Matthew Wascher is an Assistant Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. He is affiliated with the College of Arts and Sciences and holds office locations at 2145 Adelbert Rd. and Sears 5th Floor. His research focuses on infectious disease modeling , interacting particle systems , phylogenetics , and mathematical biology , with applications to epidemiology, ecology, and evolutionary biology. His work spans theoretical and applied domains, including projections of pandemic impacts, freeze tolerance mechanisms in organisms, and statistical methods for disease transmission analysis. Notable contributions include frameworks for environmental pathogen surveillance and methods to correct biases in disease prevalence estimates. His research also addresses vaccination group dynamics and survival analysis in epidemics. Wascher’s articles highlight interdisciplinary approaches, combining mathematical modeling with biological systems to address challenges in public health, organismal physiology, and evolutionary genetics. Despite prolific output, no scientific awards or grants are explicitly mentioned in the provided materials. He currently advises no listed students but collaborates on projects involving university outbreaks, species tree inference, and integrative biochemical studies. His lab or team affiliations are not detailed here, though his work suggests involvement in collaborative networks focused on mathematical biology and infectious disease systems.
Mitchell O’Sullivan is a PhD student at Queensland University of Technology (QUT), affiliated with the School of Mathematical Sciences. His research focuses on novel sequential Monte Carlo methods for approximate Bayesian computation (ABC) and dimensionality reduction techniques. Prior to this, he worked as an analyst modeling payments data to detect financial crime before returning to QUT in 2019 to complete his honours in Mathematics. Education: Bachelor of Mathematics (Honours) – Queensland University of Technology (2019) Research interests include Bayesian statistics, likelihood-free inference, machine learning, and high-performance computing. He is particularly enthusiastic about advancing computational methods for complex implicit models and leveraging modern computer hardware. Advising and Grants: No advising or grants listed. Labs/Teams: Affiliated with the QUT Centre for Data Science.
