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Johan SegersView profile

Professor

• Extreme Value Theory
• Dependence Modelling via Copulas
• Statistical Learning
• +5 more

Johan Segers is a Full Professor at the Department of Mathematics, KU Leuven , and a Visiting Professor at the Institut de statistique, biostatistique et sciences actuarielles (UCLouvain). His research bridges extreme value theory , copulas , statistical learning , and optimal transport , with applications in finance, environmental risk analysis, and multivariate statistics. Research Trends : Focus on extremal dependence, vine copulas, and Monte Carlo methods. Recent Article Trends : Combines extreme value theory with graphical models and optimal transport for high-dimensional data analysis. Awards & Honors : Prix Adolphe Wetrems (2012–2013) from the Académie Royale des Sciences, des Lettres et des Beaux-arts de Belgique Fellow of the Institute for Mathematical Statistics (IMS) Elected Member of the International Statistical Institute (ISI) Editorial Roles : Associate Editor for Advances in Applied Probability , Bernoulli , Electronic Journal of Statistics , and others. He also contributes to software development, including the R-package spatialTailDep .

Young Ki ShinView profile

Professor

• Econometrics
• Causal Inference
• Quantile Regression
• +3 more

Young Ki Shin is a Professor of Economics at McMaster University, where he has established himself as a leading researcher in econometrics and economic theory. His work bridges theoretical developments with practical applications in statistical methods for economic analysis, with significant contributions to quantile regression, causal inference, and economic modeling. His educational background includes a Ph.D. in Economics from the University of Rochester (2002-2007) and a B.A. from Seoul National University (1996-2001). Professor Shin's research focuses on several key areas: causal inference and identification , where he develops methods to determine cause-effect relationships; modern computational algorithms and inference , creating efficient statistical methods for large datasets; and applications of statistical decision theory to economic problems. His work often addresses computational challenges in applying econometric methods to massive datasets. His recent publications demonstrate a strong focus on quantile regression methods, with several papers addressing computational challenges in applying these techniques to large datasets. He has made significant contributions to generalized method of moments (GMM) estimation and developed innovative approaches to monotone signaling equilibria in matching markets. His research spans from theoretical econometrics to applied economic questions, including analyses of government spending multipliers during economic crises. As an educator, Professor Shin has taught a comprehensive range of econometrics courses at both undergraduate and graduate levels, including Econometrics I, Econometrics II, and Applied Econometrics, reflecting his research expertise in both theoretical foundations and practical applications of econometric methods.

Iain JohnstoneView profile

Professor

• Statistical decision theory
• Wavelet methods
• Estimation theory
• +6 more

Iain Johnstone is the Marjorie Mhoon Fair Professor in Quantitative Science and Professor of Statistics and Biomedical Data Science at Stanford University. He holds appointments in both the Department of Statistics and the Department of Health Research and Policy (Biostatistics), where he conducts research at the intersection of theoretical statistics and biomedical applications. Professor Johnstone's research focuses on statistical decision theory and wavelet-like methods in estimation theory, with applications to inverse problems and signal processing. His work spans asymptotic theory, simulation methodology, volume tests of significance, hazard rate estimation, and maximum entropy methods. He has made foundational contributions to wavelet shrinkage and thresholding methods that have become standard tools in nonparametric function estimation. His publication record demonstrates consistent contributions to statistical methodology, particularly in wavelet analysis, sparse modeling, and high-dimensional data analysis. Over two decades, his research has evolved from theoretical wavelet thresholding to applications in principal component analysis and sparse regression methods, influencing both theoretical statistics and practical data analysis techniques. Professor Johnstone has received significant recognition for his contributions to statistical science: Distinguished Alumni Award from Cornell University's Department of Statistical Science Guy Medal in Silver from the Royal Statistical Society As a leading researcher in statistical methodology, Professor Johnstone has supervised numerous graduate students and postdoctoral researchers. His research has been supported by various grants that have enabled significant advances in statistical theory and its applications to biomedical and signal processing problems. Professor Johnstone maintains an active research group focused on developing new statistical methodologies with applications in biomedical data science and signal processing. His team continues to explore the theoretical boundaries of high-dimensional statistical inference while developing practical tools for data analysis.

Gah-Yi BanView profile

Associate Professor

• Big Data Analytics
• Machine Learning for Operations Management
• Data-driven Decision-making and Optimization
• +4 more

Dr. Gah-Yi Ban is an Associate Professor of Analytics & Operations at Imperial College Business School (tenured since 2022). She previously held faculty positions at the University of Maryland and London Business School, and served as an Amazon Visiting Academic (2021–2024). She earned her PhD in Industrial Engineering & Operations Research from UC Berkeley (2012), along with multiple advanced degrees from UC Berkeley and a first-class honors in Physics & Mathematics from the University of Sydney. Her research focuses on Big Data analytics , particularly in decision-making under uncertainty with applications to operations management. Notable contributions include work on personalized pricing algorithms, data-driven inventory optimization, and machine learning applications in finance and retail. Her work has been recognized through awards including the 2021 Best OM Paper in Operations Research and inclusion in Poets & Quants ' 40 Under 40 list. Dr. Ban has taught across diverse programs including MBA, Executive MBA, and PhD-level courses, receiving accolades such as the Best Teacher Award at London Business School. She currently serves as an associate editor for top journals like Management Science and Operations Research , and chairs the Supply Chain Special Interest Group of the M&SOM Society. Her professional engagements include membership in the World Economic Forum Expert Network and roles on editorial and grant review boards. She has delivered keynotes at major conferences and a TEDx talk on algorithmic ethics ('The Power and Perils of Algorithms').

Tailen HsingView profile

Professor

• Functional Data Analysis
• Spatial Data Analysis
• Extreme Value Theory
• +2 more

Tailen Hsing is the Michael B. Woodroofe Collegiate Professor of Statistics at the University of Michigan's Department of Statistics, part of the College of Literature, Science, and the Arts (LSA). His research focuses on functional data analysis, spatial statistics, and extreme value theory. He has held academic leadership roles, including serving as Chair of the Department of Statistics from 2010 to 2015. Education: Tailen Hsing earned his Ph.D. in Statistics from the University of North Carolina in 1984, following an M.S. in Statistics from the same institution in 1983, and a B.S. in Mathematics from National Taiwan University in 1978. His academic career includes prior appointments at Ohio State University, the National University of Singapore, and Texas A&M University. Research Interests: Hsing’s work centers on extreme value problems, functional data analysis, long-range dependence, and space-time models. He has made significant contributions to theoretical foundations of functional data, including a notable book published in 2015. His current research explores high-dimensional parameters in functional data and inference for locally stationary spatial processes. Publications: His recent work includes advancements in spectral density estimation for spatial processes, functional data segmentation, and multivariate spatial models. These contributions highlight his expertise in bridging theoretical and applied statistical methods. Editorial Roles: He serves as an Associate Editor of Statistical Science and the Editorial Board of SpringerBriefs in Probability and Mathematical Statistics, among other editorial positions. Previously, he co-edited the Annals of Statistics and contributed to editorial roles for leading journals like Bernoulli and Statistica Sinica . Advising and Grants: While specific grants and student advisees are not detailed here, his extensive publication record reflects collaborative work with students and colleagues. His research has been supported through various academic collaborations and institutional resources. Labs and Teams: Though no specific labs are mentioned, his collaborations with researchers such as Stoev and Yarger indicate active participation in interdisciplinary teams focused on spatial and functional data analysis.

Liza LevinaView profile

Professor

• High-Dimensional Statistical Inference
• Statistical Network Analysis
• Neuroimaging
• +3 more

Liza Levina is the Vijay Nair Collegiate Professor and Chair of the Department of Statistics at the University of Michigan. She is affiliated with the Michigan Institute for Data and AI in Society and the Center for the Study of Complex Systems. Her expertise lies in high-dimensional statistical inference and statistical network analysis, with applications in neuroscience and imaging. She earned her PhD in Statistics from UC Berkeley in 2002 and has been at the University of Michigan since then. Her research focuses on network analysis, statistical learning, and big data, particularly in neuroimaging. She has received notable awards, including the ASA Junior Noether Award, and is a Fellow of both the ASA and IMS. She was an invited speaker at the 2018 International Congress of Mathematicians and an IMS Medallion lecturer. Levina’s work bridges theoretical advancements and practical applications, such as analyzing brain connectivity networks and developing methods for community detection in complex systems. She has contributed to methodologies for network cross-validation, pseudo-likelihood approaches, and conformal prediction in network-assisted regression. Her affiliations include leadership roles in the Department of Statistics and collaborations across interdisciplinary institutes. Her research has been recognized with prestigious acknowledgments, reflecting her impactful contributions to statistical science and its applications.

Holger H. HoosView profile

Professor

• Algorithmics
• Bioinformatics
• Computational Intelligence
• +9 more

Holger H. Hoos is a Professor at the University of British Columbia's Computer Science Department, affiliated with the Institute for Computational Intelligence and Cognitive Systems (ICICS) and the Peter-Wall Institute for Advanced Studies. As of 2017, his primary affiliation shifted to Leiden University (Netherlands), though he remains connected to UBC. His research focuses on algorithmics, bioinformatics, computational intelligence, and computer music, with significant contributions to stochastic local search, empirical algorithmics, and SAT solving. Education: Ph.D. (Dr.rer.nat) in Computer Science from TU Darmstadt (1998), postdoctoral fellow at UBC. He has led the BETA Lab and SALIERI Project, exploring biomolecular structure prediction and music algorithms. Research interests span stochastic algorithms, RNA structure prediction, automated algorithm configuration, and interdisciplinary applications like flow cytometry analysis. His work bridges theoretical foundations with empirical methods, emphasizing practical problem-solving. Awards include the AAAI Fellowship, multiple best paper awards (GECCO 2020, IJCAI-JAIR 2010), and leadership roles in conferences like AAAI and LION. He has advised over 30 students, contributing to impactful projects in bioinformatics and AI. Labs/Teams: Founder of the BETA Lab (Bioinformatics, Theoretical and Empirical Algorithms), member of the Laboratory for Computational Intelligence (LCI), and collaborator on SALIERI (Computer Music).

Dana YangView profile

Assistant Professor

• Statistical Inference
• Machine Learning
• Computational Complexity
• +4 more

Dana Yang is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. She joined Cornell in Spring 2022 after completing a Simons-Berkeley fellowship focusing on computational complexity of statistical inference at UC Berkeley. Previously, she was a postdoctoral associate at Duke University’s Fuqua School of Business. Her education includes a B.S. in Mathematics from Tsinghua University and M.A./Ph.D. in Statistics from Yale University. Yang’s research interests span statistical inference, machine learning, and computational complexity. She focuses on problems involving planted structures (e.g., matching recovery), privacy-preserving algorithms, high-dimensional statistics, and graph theory. Her work bridges theoretical foundations with practical applications in data science. Her recent articles explore phase transitions in statistical recovery, private convex optimization, and algorithmic fairness. Notable contributions include studies on planted matching problems, community detection efficiency, and secure sequential learning protocols. No scientific awards were explicitly mentioned in the provided materials. Her advising history and grant involvement remain unspecified in the current data. Dana Yang is affiliated with Cornell’s Comstock Hall facility, though specific lab or team affiliations were not detailed.

Erin HartmanView profile

Associate Professor

• Causal Inference
• Survey Design and Analysis
• External Validity of Experiments
• +2 more

Erin Hartman is an Associate Professor of Political Science at the University of California, Berkeley, where she has served since July 2024. Previously, she was an Assistant Professor of Political Science at UC Berkeley from July 2021 to June 2024, and before that held positions at UCLA and the University of Texas at Austin. Her academic work bridges political science and statistics, focusing on methodological innovations that enhance causal inference in social science research. PhD in Political Science, 2013, University of California, Berkeley MA in Statistics, 2013, University of California, Berkeley BS in Economics and Political Science, 2007, California Institute of Technology Professor Hartman's research centers on causal inference and survey methodology, with particular emphasis on external validity of experiments, falsification testing, and survey weighting. Her work develops statistical methods that address fundamental challenges in generalizing experimental findings to broader populations and contexts. She is known for methodological innovations that improve the precision and validity of causal estimates in political science research. Her publication record reveals a consistent trajectory of methodological innovation in causal inference. Hartman's work spans theoretical developments in external validity frameworks, practical applications in survey weighting techniques, and novel approaches to experimental design. A key theme across her publications is the development of methods that address the tension between internal and external validity in experimental research, particularly through innovative weighting and calibration techniques that allow for more reliable generalization of findings. While no specific awards are listed in the available information, her publications in top journals across statistics and political science indicate significant recognition within her field. Her collaborative work with leading scholars in statistics and political science demonstrates her position at the intersection of these disciplines. Professor Hartman has supervised multiple research projects and collaborations, though specific student names are not provided in the available information. Her work has received funding from various sources supporting methodological research in political science and statistics. She has been actively involved in developing new methodological approaches that address contemporary challenges in survey research and experimental design.

• Structural Engineering
• Structural Reliability
• Earthquake Engineering
• +2 more

Ziqi Wang is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of California, Berkeley, specializing in structural reliability, earthquake engineering, and uncertainty quantification. His research develops computationally efficient methods that integrate domain-specific knowledge for analyzing infrastructure resilience under natural hazards. Education: Ph.D. in Civil Engineering, Southwest Jiaotong University, China (2015) B.S. in Civil Engineering, Southwest Jiaotong University, China (2010) Research Interests: Professor Wang's work centers on interpretable probabilistic analysis for structural reliability, emphasizing methods that leverage civil engineering domain knowledge. Key focus areas include computational reliability techniques for high-dimensional problems, statistical physics applications for regional-scale hazard impact analysis, and physics-informed machine learning for seismic response prediction. His research bridges civil engineering with computational science to address challenges in infrastructure resilience. Publication Trends: Recent publications (2024-2025) demonstrate a strong shift toward hybrid modeling approaches combining physics-based principles with data-driven techniques. A recurring theme is the development of interpretable surrogate models for high-dimensional reliability analysis, particularly for lifeline networks and structural systems under seismic events. His work increasingly integrates statistical physics concepts to model systemic infrastructure behavior during disasters. Scientific Awards: Society of Hellman Fellows Fund Award (2024) Advising and Teaching: Professor Wang mentors graduate students through Individual Research (CIVENG 299) and teaches specialized courses including Uncertainty Quantification for Engineering (CIVENG 293), Engineering Risk Analysis (CIVENG 193), and Structural System Reliability (CIVENG 229). His teaching emphasizes practical computational methods for real-world engineering challenges. Laboratories and Research Groups: While specific lab names aren't detailed in source materials, his research aligns with Berkeley's Pacific Earthquake Engineering Research Center (PEER) initiatives, focusing on collaborative infrastructure resilience projects involving structural dynamics and regional risk modeling.

• Econometrics
• High-dimensional Statistics
• Factor Models
• +3 more

Luca Margaritella is an Associate Senior Lecturer in the Department of Economics at Lund University, part of the Lund University School of Economics and Management (LUSEM). His research focuses on econometrics and high-dimensional statistics, with applications in financial economics and time series analysis. He is actively involved in academic activities, including organizing the FEM seminar series and contributing to international collaborations. His research interests include factor models, vector autoregressive processes, and high-dimensional statistical methods, with recent work addressing topics such as bank network connectedness and climatic attribution using Granger causality. Margaritella has published in top journals like the Oxford Bulletin of Economics and Statistics and the Journal of Business & Economic Statistics . He holds a doctoral degree and has supervised research projects. His work often intersects with applied econometrics, emphasizing methodological advancements for complex economic datasets.

Mathew EvansView profile

Professor

• Atmospheric Chemistry
• Climate Science
• Air Pollution Modeling
• +5 more

Professor Mathew Evans is a leading academic in atmospheric chemistry modeling at the University of York’s Department of Chemistry, affiliated with the Wolfson Atmospheric Chemistry Laboratories and the National Centre for Atmospheric Science. His work focuses on numerical modeling of atmospheric composition to understand air pollution and climate change, leveraging tools like the GEOS-Chem model and high-performance computing (HPC) at York’s Viking facility. Education & Career PhD in Atmospheric Sciences, University of Cambridge Postdoctoral Research: MIT (Earth, Atmospheric & Planetary Sciences) and Harvard University (Division of Engineering & Applied Sciences) NERC Fellowship at the University of Leeds Lecturer/Reader at Leeds before moving to York as a Professor Research Themes Halogen chemistry (Cl, Br, I) in the lower atmosphere Nitrogen oxides in remote atmospheres Atmospheric interactions with oceans and clouds Machine learning applications for chemical model acceleration Field campaign data integration (Bermuda, Cape Verde, China, Africa) Teaching & Leadership Departmental Theme Leader for Digital Chemistry Lecturer for the 3rd-year Atmospheric Chemistry module Supervises projects on spectral methods, ensemble approaches, and open-source modeling Collaborations Wolfson Atmospheric Chemistry Laboratories National Centre for Atmospheric Science (NCAS) NASA Goddard’s Global Modeling & Assimilation Office Master Chemical Mechanism development

• Micro-econometrics
• Causal Inference
• Nonparametric Estimation
• +3 more

Ying-Ying Lee is an Associate Professor of Economics at the University of California, Irvine, and an Associate Editor of the Journal of Econometrics . She holds a Ph.D. in Economics from the University of Wisconsin-Madison and completed a postdoctoral fellowship at Nuffield College, University of Oxford. Her research focuses on micro-econometrics with an emphasis on causal inference for continuous and multivalued treatments, leveraging nonparametric estimation and machine learning techniques. Her work addresses challenges such as sample selection bias, monotonicity assumptions, and high-dimensional data in causal inference frameworks. Key contributions include advancements in double debiased machine learning for continuous treatment effects, regression discontinuity designs, and quantile derivative estimation. She has applied these methods to policy evaluation, workforce programs, and structural models. Education: PhD in Economics (University of Wisconsin-Madison), Postdoc (Nuffield College, Oxford) Roles: Associate Editor of Journal of Econometrics, Principal Investigator of multiple research projects Research Themes: Continuous treatment effects, causal bounds, instrumental variables, policy analysis Her recent work emphasizes methodological innovations for handling complex treatment structures and selection mechanisms. Lee’s papers consistently bridge econometric theory with practical applications in labor economics and public policy. She actively contributes to open-source tools through GitHub repositories for her methodologies.

W. (Weining) WangView profile

Professor

• Econometrics
• Time Series Analysis
• High-Dimensional Time Series
• +4 more

Prof. W. (Weining) Wang is a Professor at the Faculty of Economics and Business, University of Groningen. His research focuses on econometrics, time series analysis, and high-dimensional data modeling. He has collaborated with notable scholars such as Jeffrey Wooldridge and Wei Biao Wu, producing influential work in statistical methodologies for dynamic systems, spatial data, and financial econometrics. His recent contributions include advancements in quantile regression models, change-point detection in time series, and generalized estimating equations for nonlinear models. Publications span prestigious journals like the Journal of Time Series Analysis , Annals of Statistics , and Journal of Business and Economic Statistics . Contact: weining.wang@rug.nl . Research interests emphasize methodological innovations in econometric theory, with applications to economics, finance, and spatial systems. Ongoing work includes dynamic network quantile models and high-dimensional time series inference.

Joseph G. IbrahimView profile

Professor

• Bayesian Inference
• Clinical Trials Design
• Genomics
• +4 more

Dr. Joseph G. Ibrahim is the Alumni Distinguished Professor of Biostatistics at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. He holds leadership roles as Director of Graduate Studies in Biostatistics, Director of the Laboratory for Innovative Clinical Trials, and Biostatistics Core Director at the Lineberger Comprehensive Cancer Center. His academic tenure includes teaching courses like BIOS 762 (Linear Models) and BIOS 779 (Bayesian Statistics). Education: PhD/MSc in Statistics (University of Minnesota, 1988) and BS in Mathematics (1983). Research Interests: Bayesian methodology, clinical trial design, missing data, medical imaging, and cancer genomics. His work bridges statistical theory and application, with over 360 publications in top journals and two influential books on Bayesian survival analysis and Monte Carlo methods. Key Contributions: Developed statistical methods for joint modeling of longitudinal/survival data, power prior frameworks, and Bayesian approaches for multi-regional trials. His team's innovations include the LEAP prior for historical data integration and the glmmPen R package for high-dimensional models. Awards: Elected fellowships from ASA, IMS, RSS, and ISBA; Janssen Chair in Survival Analysis (2005); and UNC's Alumni Distinguished Professorship (2006). Service: Editor of JASA (Applications/Cases, 2013–2015), associate editor for Biometrics, Lifetime Data Analysis, and Bayesian Analysis. Served on NIH study sections (BMRD, EPIC) and ASA's Bayesian Section leadership. Labs/Teams: Leads the Center for Innovative Clinical Trials (Gillings Innovation Lab) and directs the Cancer Genomics Training Grant. Collaborates on NIH-funded breast cancer SPORE projects and epigenomic analysis tools like Epigrahmm.