Dr. Kaibo Liu is the Grainger STAR Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison and serves as Associate Director of the UW-Madison IoT Systems Research Center. He earned his B.S. from the Hong Kong University of Science and Technology (2009), and M.S. and Ph.D. from Georgia Tech (2011/2013). His research focuses on system informatics, big data analytics, and data fusion for process modeling, monitoring, and decision-making. He has been funded by NSF, ONR, DOE, and industry partners. Notable awards include the 2024 Hromi Medal (ASQ), 2021 IISE Technical Innovation Award, and multiple early-career recognitions. Recent work emphasizes real-time cyber-physical security, reinforcement learning for data streams, and Bayesian methods for prognosis. He edits IEEE Transactions on Automation Science and Engineering and IISE Transactions on Data Science.
Naisyin Wang is a Professor of Statistics at the University of Michigan, where she has been since 2009. Previously, she served as a faculty member in Statistics and Toxicology at Texas A&M University from 1992. She holds a Ph.D. in Statistics from Cornell University (1992), an M.A. in Statistics from Ohio State University (1987), and a B.S. in Mathematics from National Tsing-Hua University, Taiwan (1986). Her research focuses on longitudinal and functional data analysis, measurement error models, semiparametric methods, and applications in biological and medical fields, particularly genomics and metabolomics. Key contributions include methodologies for handling missing data, mixed effects models, and clustering techniques. Education: Ph.D. in Statistics, Cornell University (1992) M.A. in Statistics, Ohio State University (1987) B.S. in Mathematics, National Tsing-Hua University (1986) Dr. Wang’s honors include the College of Science Distinguished Alumni Award (2012), the Distinguished Achievement Award in Research (2003), and fellowships from the AAAS, ASA, and IMS. She has held leadership roles, including Co-editor of Statistica Sinica (2011–2014) and President of the International Chinese Statistical Association (2010). Her teaching includes courses such as Applied Statistics (STATS 500), Linear Models (STATS 600), and Special Topics in Applied Statistics (STATS 700). She has advised numerous students and contributed to research on cancer genomics, dietary interventions, and statistical methodology.
Professor Luke Prendergast is the Deputy Dean of the School of Computing, Engineering & Mathematical Sciences (SCEMS) at La Trobe University (LTU) and holds a Professorship in the Department of Mathematics and Statistics. He previously served as Head of Department (2014–2020) and led LTU's Statistics Consulting Platform. His research focuses on robust statistics, meta-analysis, dimension reduction, and applied statistics, leading the DRAMA research group. Collaborations span fields like endocrinology, disability studies, and respiratory health. He actively contributes to research grants, including projects on Prader-Willi syndrome and exercise for disability populations. Professor Prendergast's recent work emphasizes statistical software development (e.g., the rquest package) and applications in biostatistics, such as metabolomics analysis and health intervention fidelity. His articles address topics like quantile-based hypothesis testing, geospatial accessibility for disability care, and motivational interviewing efficacy. Professional roles include NHMRC grant review panels, editorial boards for Nutrients and Respirology , and leadership in the Statistical Society of Australia (SSA Vic). His teaching includes courses in meta-analysis, linear models, and data-based critical thinking. Grants funded projects on exercise programs for cerebral palsy populations and community-university partnerships for disability inclusion. Luke's work bridges statistical theory with real-world health challenges, emphasizing robust methodologies and interdisciplinary collaboration.
Prof. Mathias Drton holds the Chair of Mathematical Statistics at the Technical University of Munich (TUM), within the Department of Mathematics and School of Computation, Information and Technology. His research focuses on graphical models, algebraic statistics, causal inference, and multivariate data analysis. He has authored numerous publications in top-tier journals and conferences, including work on conditional independence, sparse factor analysis, and causal discovery in linear models. Drton has supervised a large number of theses, mentoring students in areas like high-dimensional statistics, graphical models, and causal inference. He is actively involved in teaching advanced courses such as 'Graphical Models in Statistics' and 'Fundamentals of Mathematical Statistics.' His academic contributions span theoretical developments in statistical methodology and computational tools, including R packages like SEMID and symRC . Drton collaborates internationally, contributing to projects like the TUM-ICL Mathematical Sciences Hub and Exzellenzcluster MCQST. His work bridges algebraic methods with statistical challenges, addressing identifiability in latent variable models and robust graphical modeling under non-Gaussian assumptions. Recent research emphasizes causal structure learning under partial homoscedasticity, distribution-free independence tests, and multi-domain causal representation learning. Drton’s lab actively explores applications in genomics, epidemiology, and machine learning, leveraging both theoretical rigor and practical computational methods.
Zhe Zeng is an incoming Assistant Professor in the Department of Computer Science at the University of Virginia starting July 2025. Currently, she serves as a Faculty Fellow in the Computer Science Department at New York University. She earned her Ph.D. in Computer Science from UCLA in 2024 under Professor Guy Van den Broeck, and her B.S. in Mathematics from Zhejiang University in 2018. Research Focus: Dr. Zeng specializes in neurosymbolic AI and probabilistic machine learning, developing methods that integrate symbolic knowledge (logical constraints, graph structures) with probabilistic uncertainty. Her work spans three core areas: Reasoning: Probabilistic inference, tractable probabilistic models Learning: Constrained deep learning, graph ML, weakly supervised learning Trustworthiness: Explainability, uncertainty quantification, domain-knowledge integration Awards & Honors: Rising Star in EECS (2023) Amazon Doctoral Fellowship (2022) NEC Research Fellowship (2021) ICML Travel Award (2018) Outstanding Graduate, Zhejiang University (2018) Advising & Mentoring: Has supervised six students including PhD candidates and undergraduates at UCLA, Tsinghua, and CAS, with placements at Princeton and UT Austin. Academic Service: Regularly reviews for NeurIPS, ICML, ICLR, UAI; served as UAI 2023 discussant; active in WiML mentorship programs.
Sharat Ganapati is an Assistant Professor at the Edmund A. Walsh School of Foreign Service (Primary Appointment) and affiliated faculty in the Department of Economics at Georgetown University. He holds positions as a National Bureau of Economic Research (NBER) Faculty Research Fellow and a CESifo Research Affiliate. His expertise spans international trade, industrial organization, environmental economics, and spatial economics. He earned his Ph.D. in Economics from Yale University (2017), following degrees from the University of Chicago (B.S. Mathematics, B.A. Economics, 2009). His research focuses on the interplay of trade policies, firm behavior, and spatial economic dynamics. Notable contributions include studies on transshipment hubs, urban welfare, and remote work trends. His work has been published in top journals like the American Economic Journal , Journal of Political Economy , and Journal of Economic Perspectives . Ganapati has received prestigious awards and fellowships, including the Yale Dissertation Fellowship and IBM Thomas J. Watson Scholarship. He advises graduate students such as Cam Healy and Philipp Ludwig and has held editorial roles in journals including the Journal of International Economics and Review of Economics and Statistics . His grants include NSF funding for research on firm heterogeneity. Professional activities include seminars at institutions like Harvard, MIT, and the Federal Reserve.
Hal S. Stern is Provost and Executive Vice Chancellor at the University of California, Irvine (UCI), and a Distinguished Professor in the Department of Statistics. He previously served as founding Chair of the Department of Statistics, Dean of the Donald Bren School of Information and Computer Sciences, and Vice Provost for Academic Planning at UCI. Earlier, he held faculty positions at Iowa State University and Harvard University. B.S. in Mathematics, Massachusetts Institute of Technology M.S. and Ph.D. in Statistics, Stanford University Stern is a leading expert in Bayesian statistical methods, with significant collaborative work in life sciences and social sciences. His current research focuses on forensic statistics (e.g., footwear impression and bloodstain pattern analysis), psychiatric studies of early-life adversity's impact on brain development, and statistical applications in sports analytics. He co-directs the NIST-funded Center for Statistics and Applications in Forensic Evidence and leads the Conte Center's NIMH-funded research on mental health vulnerabilities. His notable contributions include the third edition of Bayesian Data Analysis , which expanded computational methods and Bayesian nonparametric modeling, featuring STAN software. Stern has secured major grants from NIST and NIMH for interdisciplinary projects. Fellow, American Association for the Advancement of Science Fellow, American Statistical Association Fellow, Institute for Mathematical Statistics He has mentored graduate programs as Vice Provost for Graduate Education and contributed to UCI's academic strategy as Vice Provost for Academic Planning. Stern's leadership extends to directing centers that bridge statistics with forensic science and mental health research.
Ye Zhisheng is the Dean’s Chair and Associate Professor in the Department of Industrial Systems Engineering & Management at the National University of Singapore (NUS). His research focuses on reliability engineering, inventory control, emergency response systems, and statistical modeling. He holds a PhD in Industrial and Systems Engineering from NUS, along with a BEng in Material Science and Engineering and a BEco in Economics from Tsinghua University. His work emphasizes practical applications in mission-critical systems, predictive maintenance, and data-driven decision-making. Current research initiatives include optimal maintenance policies for manufacturing systems, degradation analysis of bearings, and federated learning approaches for battery lifecycle prediction. He has pioneered methods for integrating physics-informed neural networks into prognostics and health management (PHM) systems. Key technical contributions span advanced statistical methodologies like sieve estimation for survival data, phase-type distributions modeling, and condition-based maintenance optimization. His interdisciplinary approach bridges operations research, mechanical engineering, and computer science to address complex reliability challenges. Recent projects include resilient consensus-based power grid management and contamination source identification frameworks. Notable collaborations involve developing intelligent cross-domain fault diagnosis systems using transformer networks and advancing the Internet of Federated Things (IoFT) for distributed data analytics. His work has been applied in aerospace, telecommunication infrastructure, and medical emergency response systems.
Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence, and Medicine at the University of Cambridge, leading the van der Schaar Lab. She holds dual affiliations with the Department of Applied Mathematics and Theoretical Physics (DAMTP) and the Centre for Mathematical Imaging in Healthcare. Her research focuses on healthcare AI, machine learning, and operations research. She has authored over 250 journal articles and 275 conference papers, with notable contributions to synthetic data for privacy, causal inference, and clinical decision-making. Her work has led to 35 U.S. patents, including foundational innovations in streaming video compression (MPEG-4 standards). Awards include the Oon Prize (2018), IEEE Fellow (2009), and recognition as the UK's most-cited female AI researcher (2019). Leadership roles include Director of the Cambridge Centre for AI in Medicine and Co-Director of the European Laboratory for Learning and Intelligent Systems. She has mentored global academic leaders and pioneered initiatives like the Inspiration Exchange for early-career researchers. Key projects include predictive models for hospital resource allocation during pandemics and AI tools for personalized medicine. Publications span machine learning theory, healthcare applications, and interdisciplinary fields like network science. Her lab's impact includes tools like AutoPrognosis (automated ML for clinical prediction) and SynthCity (synthetic healthcare data generation).
Fabio Sigrist is a Professor of Applied Statistics and Data Science at the Institute of Financial Services Zug (IFZ) , part of the Lucerne University of Applied Sciences and Arts . He also holds a Senior Scientist and Lecturer position at the Seminar for Statistics, ETH Zurich . His career spans academic research, industry consulting, and project leadership in finance and data science. PhD in Statistics (2013), ETH Zurich MSc in Mathematics with distinction (2008), ETH Zurich MEd in Mathematics Education (2008), ETH Zurich Sigrist’s research focuses on integrating Machine Learning with Spatial Statistics for applications in Financial Econometrics and Credit Risk . His work includes developing novel algorithms like GPBoost and KTBoost , advancing spatio-temporal modeling , and applying tree-based boosting to financial problems. Projects such as CreHos (credit risk in hospitality) and NISMO (interpretable real estate modeling) highlight his interdisciplinary approach. His publications address challenges in large-scale spatial data , loss given default modeling , and stock volatility prediction . He contributes to software development with tools like spate (R package) and varycoef (spatially varying coefficients).
Carlos Fernandez-Granda is an Associate Professor of Mathematics and Data Science at New York University, holding joint appointments at the Courant Institute of Mathematical Science and the Center for Data Science. He currently serves as the Interim Director of the Center for Data Science. His academic career spans over a decade at NYU, where he has taught probability and statistics to data-science students. Dr. Fernandez-Granda's research focuses on designing and analyzing data-science methodology, with current emphasis on machine learning applications in medicine, climate science, and scientific imaging. His work bridges theoretical foundations with practical applications across multiple domains. His notable contributions include: Development of the COBRA (COnfidence-Based chaRacterization of Anomalies) score for automatic assessment of impairment and disease severity Applications of machine learning to improve climate projections through the M2LInES project Research on magnetic resonance fingerprinting for quantitative tissue parameter estimation Development of AI systems for medical diagnostics including Alzheimer's detection and breast cancer diagnosis Dr. Fernandez-Granda is the author of the book "Probability and Statistics for Data Science," published by Cambridge University Press. The book serves as a comprehensive guide to the two pillars of data science, featuring real-world datasets and addressing fundamental challenges like overfitting, the curse of dimensionality, and causal inference. His research has been supported by grants from the National Science Foundation (Division of Mathematical Sciences, grants 1616340 and 2009752) and the Alzheimer's Association (grant AARG-NTF-21-848627). Dr. Fernandez-Granda is actively involved in several collaborative projects: M2LInES project: An international collaboration focused on improving climate projections using machine learning to capture unaccounted physical processes at the air-sea-ice interface Math and Data group: Exploring the intersection of mathematical theory and data science applications
Yannick BARAUD is a Full Professor in Mathematics at the University of Luxembourg, leading the group 'Developing Contemporary Mathematical Statistics' within the Department of Mathematics. He holds the ERA-Chair 'SanDAL' in Mathematical Statistics and Data Science, funded by the European Commission. His research focuses on robust estimation, model selection, hypothesis testing, and nonparametric methods. He serves as the Study Programme Director for the Master in Data Science and has held academic positions at the University of Nice Sophia Antipolis and CNRS. His career includes roles as a researcher at École Normale Supérieure (Paris) and a lecturer at the same institution. He earned his PhD from Université Paris-Sud and studied at École Normale Supérieure de Cachan. His work emphasizes rigorous statistical methodologies, including rho-estimation and robust Bayes-like approaches. Notable contributions include advancements in density estimation under shape constraints and robust regression techniques. He has published extensively on topics such as loss functions, empirical processes, and statistical inference, with applications in epidemiology and data science. His leadership in the SanDAL initiative underscores his commitment to bridging mathematical statistics and practical data science challenges. Collaborations and grants further highlight his role in advancing interdisciplinary research.
Nezir KÖSE is a Professor and currently serves as the Dean of the Faculty of Economics and Administrative Sciences at Beykent University. He has previously held academic positions at Istanbul Gelişim University and Gazi University, where he advanced from Research Assistant to full Professor. His academic career spans over three decades, with continuous contributions in teaching, research, and administrative leadership. Beykent University – Faculty of Economics and Administrative Sciences (2020–Present) Istanbul Gelişim University – Faculty of Economics, Administrative and Social Sciences (2017–2020) Gazi University – Faculty of Economics and Administrative Sciences (1990–2017) Education: Doctorate, Institute of Social Sciences, Gazi University (1992–1998) Degree, Faculty of Economics and Administrative Sciences, Gazi University (1990–1992) Licence, Faculty of Science, Gazi University (1985–1989) His primary research interests include Econometrics , Macroeconomics , Financial Economics , Time Series Analysis , and Energy and Environmental Economics . He has made significant contributions to the analysis of inflation, exchange rate volatility, foreign direct investment, oil price impacts, and financial stability, with a regional focus on Turkey and emerging markets. His recent publications (2023–2025) reflect a dynamic research agenda involving cryptocurrency markets , climate change economics , machine learning applications , and nonlinear macroeconomic modeling . His work frequently employs advanced econometric techniques such as panel data analysis, VAR/SVAR models, GARCH models, and time-varying parameter estimation. Scientific Awards: No awards mentioned in the provided text. Nezir KÖSE has supervised numerous graduate students, including PhD candidates who have completed theses on topics such as foreign direct investment, financial stability, oil price effects, and inflation uncertainty. He has also contributed to academic grants and collaborative research projects, particularly in energy and financial economics. He teaches core courses including Econometrics I & II , Time Series Analysis , and Nonparametric Statistics , demonstrating a strong commitment to pedagogy. He has authored several textbooks in econometrics and statistics, enhancing educational resources in Turkish academia. There is no indication of lab or team leadership, but his collaborative publications suggest active participation in research groups.
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.
Dan Kowal is an Associate Professor in the Department of Statistics and Data Science at Cornell University, joining in 2024. His research focuses on Bayesian models for large/dependent data, mixed data modeling, and interpretable uncertainty quantification. Key areas include public health, environmental justice, epidemiology, and economics. He holds a PhD from Cornell University (2017) and previously served as an Assistant Professor at Rice University. Awards include the Blackwell-Rosenbluth Award (2021), Army Research Office Young Investigator Award (2020), and Lindley Prize Honorable Mention (2024). Notable grants include NSF funding for adaptive dependent data models (2022–2025) and Army Research Office support for Bayesian prediction methods (2020–2022). His work addresses racial inequities in statistical modeling and has been published in top journals like JASA and Bayesian Analysis. He advises multiple PhD students and develops R packages (e.g., SeBR, countSTAR) for Bayesian regression and data synthesis. Teaching roles include Bayesian Statistics at both undergraduate and graduate levels.
