Heather Battey is a Professor in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. Her work bridges foundational statistical theory with practical scientific applications, focusing on parametrization effects, sparsity, and high-dimensional inference. Education PhD, University of Cambridge (2008-2011) Research Interests Battey's research examines how model structure and parametrization influence inferential procedures, particularly in high-dimensional settings. She investigates the equivalence between sparsity and reparametrization, and challenges traditional Fisherian statistical abstractions through modern practices. Her publications reveal a pattern of innovation in high-dimensional regression, covariance matrix analysis, and statistical methodology for complex data. Collaborations span disciplines including machine learning, economics, and biomedical research. Scientific Awards Fellow of the Institute of Mathematical Statistics (2023) EPSRC Early Career Research Fellowship (2020-2026) EPSRC Postdoctoral Research Fellowship (2017-2020) Advising and Grants Battey supervises PhD students Charlotte Edgar, Jakub Rybak, and Rebecca Lewis, with informal guidance to Henrique Hoeltgebaum. Over 15 pre-doctoral researchers have been mentored in topics ranging from support vector machines to spatial point processes. Current funding includes an EPSRC grant for theoretical foundations of inference with nuisance parameters and prior support for covariance matrix inference.
Subhashis Ghoshal is a Goodnight Distinguished Professor in the Department of Statistics at North Carolina State University (NCSU). He holds a Ph.D. in Statistics from the Indian Statistical Institute (1995). His research focuses on Bayesian nonparametrics, high-dimensional models, asymptotic theory, and functional data analysis. He has authored influential books like *Fundamentals of Nonparametric Bayesian Inference* (2017) and contributed to methodologies in image processing and statistical inference. Key awards include the Goodnight Distinguished Professorship (2021), Dr. Cavell Brownie Mentoring Award (2014-15), and the De Groot Prize (2019). He has held editorial roles in journals like *Statistical Science* and *Annals of Statistics*. His work bridges theory and applications, addressing challenges in modern statistical problems such as uncertainty quantification and causal inference. He advises on graduate programs and actively contributes to academic leadership at NCSU.
Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.
Dr. Katerina Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program at the University of Minnesota's Department of Psychology. She is affiliated with the Minnesota Population Center and serves as Co-Chair of the Structural Equation Modeling Special Interest Group (SEM SIG) for the American Educational Research Association. Her research focuses on advanced data mining and modeling techniques for complex longitudinal data, particularly applied to developmental processes in economically disadvantaged immigrant children. She holds a PhD in Quantitative Psychology from Arizona State University, an MA from UC Davis, and a BA from UC Santa Barbara. Education: PhD: Quantitative Psychology, Arizona State University MA: Quantitative Psychology, University of California, Davis BA: Psychology (minor in Education), University of California, Santa Barbara Research Interests: Dr. Marcoulides develops and applies statistical methods such as structural equation modeling (SEM), Bayesian synthesis, and data fusion to study developmental and educational processes. Her work emphasizes longitudinal data analysis, item response theory, and multilevel modeling. Recent projects include NIH-funded research on parenting, marginalization, and well-being during the pandemic. Awards: APS Rising Star Award (2021) NIH Grant Award Teaching & Collaboration: She teaches courses on SEM, multilevel modeling, and data analysis at the University of Minnesota. Previously at the University of Florida, she contributed to workshops on educational data mining and served as an APA Advanced Training Institute presenter. Her interdisciplinary collaborations span population studies, health inequities, and workforce research. Labs & Groups: She leads the Data Analytics and Visualization Lab and actively participates in the Minnesota Population Center, integrating computational and statistical innovations with real-world applications.
Jessica Lin is an Associate Professor in the Department of Computer Science at George Mason University, with a focus on data mining and time series analysis. She has published extensively on topics including motif discovery, anomaly detection, clustering, and symbolic representation of time series data. Ph.D., M.S., and B.S. in Computer Science from UC Riverside (2005, 2002, 1999) Her research spans efficient algorithms for mining massive time series datasets, extending to multimedia data like images and texts. She has developed tools such as GrammarViz and SAX for pattern visualization and symbolic analysis. Recent publications highlight advancements in variable-length motif discovery, interpretable classification frameworks, and anomaly detection. Her work appears in top conferences like AAAI, ICDM, and SDM, as well as journals including Knowledge and Information Systems and Data Mining and Knowledge Discovery . Dr. Lin has advised numerous Ph.D. students, many of whom have taken academic or industry positions. She has served on editorial boards and program committees for conferences such as KDD, ICDM, and ECML-PKDD.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IITK), where he leads the INSIGHT: Intelligent Scientific and Visual Computing of Big Data Research Group. He joined IIT Kanpur in October 2022 after working as a Scientist II at Los Alamos National Laboratory (LANL) from July 2019 to August 2022, and previously as a Postdoctoral Research Associate at LANL from June 2018 to July 2019. His educational background includes a Ph.D. and M.S. in Computer Science and Engineering from The Ohio State University (2011-2018), where he was part of the GRAVITY research group, and a B.Tech. in Electronics and Communication Engineering from West Bengal University of Technology, India (2005-2009). Research Interests: Dr. Dutta's research focuses on the intersection of machine learning, visual computing, big data, and high-performance computing. His primary research areas include Machine Learning for Visual Computing and Image Analysis, Big Data Visualization and Analytics, Data Science and HPC, Machine Learning for Scientific Computing, and Explainability and Interpretability of AI Models. His work addresses various big data characteristics including the 5 Vs: Volume, Velocity, Variety, Veracity, and Value. He develops techniques that make complex machine learning models more interpretable and explainable, enabling their effective adoption in real-life applications across scientific domains, social media, IoT, healthcare, and industry applications. Dr. Dutta's research group has secured multiple funded projects including: DAVi: An Intelligent Data Analytics and Visualization Framework (funded by ISRO), Intelligent Visual Computing of Extreme-scale Data for Accelerating Scientific Discovery (IIT Kanpur Initiation Grant), Enabling Interactive Big Data Analytics and Visualization at Exascale (SERB), Development of AI-Enabled National Portal for Efficient Search of Missing People (C3iHub), and Proactive and Generalized Deepfake Defense Mechanisms (C3iHub). Best Reviewer, Honorary Mention Award for IEEE Transactions on Visualization & Computer Graphics (TVCG), 2021 Best Paper Award at ISAV 2021, co-located with Supercomputing (SC) LAAP Award at Los Alamos National Laboratory, 2021 Best Paper Award at TopoInVis 2019 Best Paper Award at ISAV 2018, co-located with Supercomputing (SC) Best Poster Award in 12th Annual CSE Student Poster Exhibition, The Ohio State University, 2018 Best Poster Award in 11th Annual CSE Student Poster Exhibition, The Ohio State University, 2017 Best Paper Honorable Mention Award at IEEE Visualization Conference (IEEE VIS) 2016 Dr. Dutta actively mentors a large group of students including Ph.D., M.Tech., and B.Tech. students. His current Ph.D. students include Shanu Saklani, Sankhadeep Bhowmick, Ananya Chaturvedi, Arpita Santra, Anubhav Dixit (co-supervised), and Robin Shah. He has supervised numerous M.Tech. students with thesis topics ranging from uncertainty-aware neural networks to deepfake detection. Dr. Dutta currently teaches courses including CS360 - Introduction to Computer Graphics and CS661 - Big Data Visual Analytics. The INSIGHT research group collaborates internationally with researchers from Meta, Oak Ridge National Laboratory, and National Taiwan Normal University. The group's work focuses on building machine learning and data science-based solutions to analyze large-scale multifaceted data in a scalable way, enabling interactive and interpretable analytics of complex data from scientific simulations, social media, IoT, healthcare, and other application domains.
Richard J. Cook is a University Professor and Mathematics Faculty Research Chair in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds cross-appointments at the School of Public Health and Health Systems at the University of Waterloo and the Faculty of Health Sciences at McMaster University. Previously, he held a Tier I Canada Research Chair in Statistical Methods for Health Research from 2005 to 2019. His educational background includes: BSc in Statistics from McMaster University MMath in Mathematics from University of Waterloo PhD in Statistics from University of Waterloo Professor Cook's research focuses on developing and applying statistical methods for public health research. His primary areas of interest include the analysis of life history data, longitudinal data analysis, methods for incomplete data, clinical trial design, and multivariate analysis. His work provides critical methodological frameworks for understanding disease progression and evaluating interventions in complex health settings. He has made significant contributions to the development of multistate models for disease processes and methods for handling interval-censored data. His extensive publication record demonstrates consistent focus on methodological innovations addressing real-world health research challenges. Recent work emphasizes estimand specification in clinical trials, transportability of research findings, and causal inference methods. His research bridges theoretical statistics with practical applications in autoimmune diseases, transfusion medicine, and public health. Professor Cook has received significant professional recognition: Tier I Canada Research Chair in Statistical Methods for Health Research (2005-2019) Mathematics Faculty Research Chair at University of Waterloo His students have earned prestigious awards including multiple Pierre-Robillard Awards, ISCB Student Conference Awards, and ENAR Distinguished Student Paper Awards, with notable achievements like Dr. Shu (Joy) Jiang being named in the Forbes Top 30 Under 30 North America (2023) for Healthcare. Professor Cook has advised numerous graduate students throughout his career, with many going on to successful academic and industry positions. His research has been supported by various grants, and he collaborates extensively with researchers in rheumatology, transfusion medicine, and public health through affiliations with the Centre for Prognosis Studies in Rheumatic Diseases, the International Psoriasis and Arthritis Research Team, and the McMaster Centre for Transfusion Research. He leads a vibrant research team that includes research associates, post-doctoral fellows, and graduate students working on cutting-edge statistical methodology. His research group maintains strong connections with multiple institutions and research centers focused on health outcomes and disease progression.
Eamonn Keogh is a Professor in the Computer Science and Engineering Department at the University of California, Riverside. His pioneering work centers on the Matrix Profile, a transformative approach to time series data mining enabling efficient solutions for motif discovery, anomaly detection, and similarity search. His algorithms (STAMP, STOMP, SCRIMP, DAMP, SCAMP) offer exact, parameter-free, and scalable solutions across domains like seismology, bioinformatics, and industrial IoT. Research areas include: Development of ultra-fast algorithms for time series joins and motif discovery at unprecedented scales (breaking the 100 million barrier) GPU acceleration for time series mining Domain-agnostic methods for semantic segmentation and anomaly detection Novel primitives like Time Series Chains, Snippets, and Consensus Motifs His work is highly cited and recognized by industry and academia, with applications ranging from NASA's Cassini mission to detecting BGP anomalies in computer networks.
Professor Jiti Gao is a Donald Cochrane Chair in Econometrics & Business Statistics at Monash University's Faculty of Business and Economics. He leads the Department of Econometrics and Business Statistics, specializing in non- and semi-parametric econometrics, time-series analysis, and panel data methodologies. His research focuses on developing statistical models for climate change, energy demand, and financial forecasting. Affiliations: Monash University, Impact Labs Grants: Multiple ARC Discovery Projects (e.g., 2020–2025 on climate-energy time series, 2017–2020 on econometric model building) Collaborations: CSIRO, Yale University, and international partners from China, Norway, and Singapore Research interests include climate econometrics, financial time series, and policy evaluation. Over 136 publications span econometric theory and applications, with recent work on nonlinear trending models and quantile regression. His grants emphasize methodological advancements in time series and panel data analysis. Awards: Not explicitly mentioned, but recognition includes Australian Professorial Fellow status and international research leadership roles. Advising/Grants: Primary Investigator on multiple ARC-funded projects, focusing on climate modeling and financial econometrics Labs/Teams: Part of Monash's Impact Labs and collaborates with global institutions on climate and econometric initiatives
Alfred O. Hero, III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is currently on leave from the University of Michigan as a Program Director in the CISE Directorate at the National Science Foundation. His primary appointment is in the Department of Electrical Engineering and Computer Science (EECS), with secondary appointments in the Department of Biomedical Engineering and the Department of Statistics. He is also affiliated with the UM Center for Computational Medicine and Bioinformatics (CCMB), the UM Graduate Program in Applied and Interdisciplinary Mathematics (AIM), the UM Applied Physics Program, and the Michigan Institute for Data Science (MIDAS), which he co-founded from 2015-2018. Hero's research focuses on data science, developing theory and algorithms for multimodality data collection, fusion, analysis and visualization that use statistical machine learning and distributed optimization. His work has applications in wearable technologies for personalized health and predictive medicine, spatio-temporal networks in biology, climate, and social discourse, anomaly detection, and data analysis for international security. His recent research interests include high dimensional spatio-temporal data analysis, multimodal data integration, statistical signal processing, and machine learning, with particular emphasis on predictive mathematical models for biological and physical sciences, social networks, network security and forensics, and personalized health and disease. His recent publications demonstrate a strong focus on high-dimensional statistical methods, contrastive learning, neural network optimization, change detection in temporal graphs, and applications in microbiome analysis and epidemic modeling. The research spans theoretical foundations in information theory and statistical learning while addressing practical applications across multiple domains. Scientific Awards: IEEE Signal Processing Society Best Paper Award (1998) Best Original Paper Award from Journal of Flow Cytometry (2008) Best Magazine Paper Award from IEEE Signal Processing Society (2010) SPIE Best Student Paper Award (2011) IEEE ICASSP Best Student Paper Award (2011) IEEE Signal Processing Society Technical Achievement Award (2014) IEEE Signal Processing Society Society Award (2015) IEEE Fourier Award (2020) University of Michigan Distinguished Faculty Achievement Award (2011) Hero has advised over 60 PhD students and 30 postdocs in areas including modeling, computation, and inference for large scale time varying data in the biosciences. He has received significant research funding from the Department of Energy, Army Research Office, Air Force Office of Scientific Research, and National Science Foundation. He has held leadership positions including President of the IEEE Signal Processing Society (2006-2007), Director of Division IX (Signals and Applications) on the IEEE Board of Directors (2009-2011), and Chair of the Committee on Applied and Theoretical Statistics of the US National Academies (2018-2020).
Heping Zhang is the Susan Dwight Bliss Professor of Biostatistics at the Yale School of Public Health , with secondary appointments in the Child Study Center , Department of Statistics and Data Science , and Department of Obstetrics, Gynecology, and Reproductive Sciences . He directs the Collaborative Center for Statistics in Science (C²S²) and leads the Reproductive Medicine Network data coordinating center. Education: PhD in Statistics, Stanford University (1991) Postdoctoral Fellow, Mathematical Science Research Institute (1991) Research Focus : Zhang specializes in biostatistical methodology for genomic data analysis , clinical trials , and reproductive medicine . His work bridges genetics , mental health , and maternal-child health through innovative statistical approaches. Awards : 2023 Web of Science Highly Cited Researcher 2023 International Chinese Statistical Association Distinguished Achievement Award 2022 Institute of Mathematical Statistics Neyman Award and Lecture 2011 Royan Institute International Research Award 2011 Institute of Mathematical Statistics Medallion Award 2008 Harvard School of Public Health Myrto Lefokopoulou Distinguished Lecturer Professional Roles : He served as President of the International Chinese Statistical Association (2019) and Former Editor of the Journal of the American Statistical Association - Applications and Case Studies . His lab develops open-source software tools like ABESS , STREE , and modSaRa for genomic and clinical data analysis.
Lee Yong Lim is a Professor of Pharmaceutics at The University of Western Australia (UWA), leading the Laboratory for Drug Delivery. She specializes in innovative drug delivery systems, particularly for pediatric populations and marine aquaculture. Her work focuses on overcoming medication challenges for children through taste-masked formulations and stable drug-loaded scaffolds for ear diseases. She has secured over $4.5 million in research grants from diverse funding bodies. Education: BSc (Pharmacy) Hons, National University of Singapore PhD, University of Manchester Research: Her team developed a patented chocolate-based formulation for bitter drugs and conducts pediatric trials. Collaborations include WA hospitals for safe compounded medicines and international projects in nanotechnology. Key research areas include pediatric medicinal products, veterinary drug delivery, and sustainable drug formulations aligned with UN SDGs. Grants & Funding: Major supporters include NHMRC, ARC, Telethon, and industry partnerships. Current active grants focus on pre-procedural chewables for children and perioperative analytics. Labs/Teams: Heads the Laboratory for Drug Delivery and collaborates with the Centre for Optimisation of Medicines and Institute for Paediatric Perioperative Excellence.
Huiyan Sang is a Professor and Director of the Undergraduate Program in the Department of Statistics at Texas A&M University (College of Arts & Sciences). She earned her Ph.D. in Statistics from Duke University and a B.Sc. in Mathematics and Applied Mathematics from Peking University. Her research focuses on spatial statistics, Bayesian nonparametric methods, machine learning, computational statistics, and applications in environmental sciences, geosciences, urban planning, and biomedical research. Her interdisciplinary work integrates statistical methodologies with real-world challenges, such as analyzing extreme environmental events, optimizing urban infrastructure, and modeling complex systems like human mobility during pandemics. She has contributed to advancing spatio-temporal modeling, Gaussian processes, and Bayesian hierarchical frameworks for large datasets. Recent publications highlight innovations in nonparametric regression, spatial functional data analysis, and stochastic frontier analysis, often leveraging computational efficiency and scalability. Her work addresses critical societal issues, including the impact of community design on public health and environmental monitoring through remote-sensing data. No scientific awards are explicitly listed in the provided texts. She advises no students or grants in the current dataset but collaborates widely on interdisciplinary projects. Her research lab focuses on developing cutting-edge statistical tools with applications in engineering, public health, and environmental science.
Claire Donnat is an Assistant Professor in the Department of Statistics at the University of Chicago, specializing in statistical and machine learning methods for graph-structured and high-dimensional data. Her work bridges theoretical innovation with applications in biomedical research, environmental science, and public health. Education: B.S. and M.S. in Applied Mathematics from Ecole Polytechnique; Ph.D. in Statistics from Stanford University (2020). Her research focuses on three methodological directions: (1) statistical foundations for graph neural networks (GNNs), (2) structured estimation with graph constraints, and (3) multimodal data integration with uncertainty quantification. Key applications include thermotolerance in photosynthetic microbes, family network analysis for child welfare, and spatial transcriptomics. The 15 most recent publications highlight her work in GNNs, CCA, tensor modeling, and epidemic analysis, with keywords spanning statistics, machine learning, and network science. Her methodological contributions address challenges in sparsity, graph topology, and heterogeneous data fusion. Scientific Awards: Facebook Research Award (2021), C3.AI COVID Grand Challenge winner (2020), Lumiata hackathon winner (2020), Stanford Centennial Award (2019), and others. Claire's research group actively recruits postdocs and students for projects involving graph-based modeling, data integration, and biomedical applications. She also provides research consulting in statistical methodology and graph modeling for life sciences.
Vladimir Spokoiny is a Professor at the Departments of Mathematics and Economics of the Humboldt University of Berlin and Head of the Research Group "Stochastic Algorithms and Nonparametric Statistics" at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. His research spans multiple areas of statistics, machine learning, and financial mathematics, with significant contributions to nonparametric statistics, high-dimensional data analysis, and statistical methods in finance. Spokoiny received his M.Sc. in applied mathematics from the Moscow Institute of Railway Engineering in 1981 and his Ph.D. in mathematics from Lomonosov Moscow State University in 1988. He completed his Habilitation at Humboldt University in 1996. His academic career includes positions at the All-Union Institute of Railway Transport in Moscow, the Institute for Information Transmission Problems in Moscow, and the Institute for Applied Analysis and Statistics in Berlin before joining the Weierstrass Institute and Humboldt University where he has been a professor since 2002. Spokoiny's research focuses on adaptive nonparametric smoothing and hypothesis testing, high dimensional data analysis, statistical methods in finance, image analysis with applications to medicine, classification, and nonlinear time series. His work often addresses the challenges of nonstationarity in time series data and develops innovative methods for volatility estimation and risk management. He has made significant contributions to the development of adaptive weights smoothing procedures, which have applications in image processing, community detection, and manifold learning. His recent work has expanded into high-dimensional statistics, Bayesian inference, and optimization methods for machine learning, with publications demonstrating novel approaches to Gaussian approximation, Laplace methods, and statistical inference in non-Euclidean spaces. Spokoiny has supervised numerous PhD students including Oliver Reiss, Danilo Mercurio, Ying Chen, Elmar Diederichs, and Mstislav Elagin, whose research has focused on mathematical finance, time series analysis, and statistical methods. He serves as an Associate Editor for The Annals of Statistics (since 2004) and Statistics and Decisions (since 2002), and has previously served on the editorial board of the Journal of Statistical Planning and Inference. His professional activities include reviewing for major statistical journals including Annals of Statistics, Bernoulli, Econometrica, and Journal of American Statistical Association, as well as reviewing grant proposals for the National Science Foundation (USA), German Research Foundation, and Netherlands Organisation for Scientific Research. Spokoiny is a member of several professional societies including the International Statistical Institute, American Statistical Association, Institute of Mathematical Statistics, and Bernoulli Society. He is fluent in Russian (mother tongue), English, and German, and has good knowledge of French. His research group at WIAS focuses on developing novel statistical methodologies with applications across various scientific domains, particularly emphasizing adaptivity and robustness in complex data environments. The group's work has significant implications for financial risk management, medical imaging, and machine learning applications, with recent publications addressing fundamental questions in high-dimensional statistics and nonparametric inference.
