Flora Salim is a Professor in the School of Computing Technologies at RMIT University. She serves as co-Deputy Director of the RMIT Centre for Information Discovery and Data Analytics (CIDDA) and an Associate Investigator of the ARC Centre of Excellence in Automated Decision Making and Society. Her research focuses on human behavior modeling, machine learning with time-series and spatio-temporal data, and edge AI applications in IoT and wearables. Flora has secured over $10M in research funding from ARC, industry partners, and government bodies. Notable awards include the 2021 PACM IMWUT Distinguished Paper Award, 2019 Humboldt-Bayer Fellowship, and RMIT's 2018 Research Impact Award. She leads the CRUISE research group and has held visiting professorships at the University of Kassel and University of Cambridge. Editorial roles: Associate Editor of PACM on IMWUT, Area Editor of Pervasive and Mobile Computing Steering Committee member of ACM UbiComp Her work bridges ubiquitous computing and machine learning, with applications in urban analytics, mobility, and health monitoring. Recent projects include self-supervised learning for multimodal data and forecasting with heterogeneous time-series. Supervision areas: Deep learning for sensor data, explainable AI, and wearable-based emotion sensing Teaching programs: Master of Artificial Intelligence and Master of Data Science
Indrabati Bhattacharya is an Assistant Professor in the Department of Statistics at Florida State University. Their research focuses on advanced statistical methodologies including dynamic treatment regimes, machine learning applications in healthcare, and Bayesian asymptotics. Bhattacharya’s work emphasizes nonparametric Bayesian approaches for addressing partial compliance in sequential decision-making frameworks and developing robust quantile regression techniques. Key research areas include: Quantile Regression and Shape-Restricted Inference Bayesian Nonparametric Methods for Multivariate Analysis Optimization of Dynamic Treatment Regimes in Clinical Trials Development of Marginal Structural Models for Sequential Treatment Decisions Recent contributions highlight Bayesian Q-learning algorithms for policy optimization under partial compliance scenarios, as well as innovative Gibbs posterior frameworks for multivariate quantile inference. Bhattacharya has also applied Bayesian techniques to sports analytics, notably exploring the Duckworth-Lewis method in cricket. Current research trends emphasize integrating machine learning with Bayesian statistical theory to address complex real-world problems in healthcare and decision science. No academic awards or grants are explicitly listed in available materials.
Associate Professor Pierre Lafaye de Micheaux is a statistician based at the School of Mathematics and Statistics, University of New South Wales , where he has worked since 2020. He previously held academic roles at Université Paul Valéry (2020, Associate Professor), ENSAI (2015–2017, Professor), Université de Montréal (2011–2016, Associate Professor), and Grenoble Alps University (2003–present, Assistant Professor). His research spans theoretical and applied statistics , focusing on complex random vectors , neuroimaging genetics , and data science for IoT . Education: PhD in Statistics (2003, Université de Montréal & Montpellier) MSc in Biostatistics (1998, Montpellier) BSc in Mathematics and Physics (1996, Montpellier) MSc in Cognitive Neuroscience (2007, Grenoble Institute of Technology) Research interests include: Dependence Measures : Leveraging complex analysis for big data dependence testing under 3V's (Volume, Variety, Velocity). Neuroimaging Genetics : Developing statistical tools for fMRI/EEG/DTI phenotyping of genetic variation with institutions like CHeBA and INSERM. IoT Data Science : Creating Raspberry Pi-based statistical computing tools for real-time sensor data streams. Complex-Valued Inference : Building a unified framework for complex random vectors in neuroimaging and nuclear engineering. Recent publications demonstrate expertise in circular data analysis , nonparametric testing , and central limit theorem counterexamples , with applications in medical imaging and finance. He has supervised numerous PhD, MSc, and honors students on topics ranging from deep learning to stochastic processes. Scientific achievements include: Université de Montréal Provost Honor List (2003) Editor of the Journal of Statistical Software (2017–present) Co-leader of three research groups: Dependence Measures , Neuroimaging Genetics , and Data Science & IoT He has secured grants from UNSW Research Infrastructure Scheme and NSERC , with industry collaborations including BNP Paribas (credit risk) and Olea Medical (stroke treatment analytics). Current teaching includes Statistical Inference (ZZSC5905) and Data Science (DATA3001) at UNSW.
Stanislav Anatolyev serves as Full Professor of Economics at the New Economic School (NES) since 2009 and holds an Associate Professor position at CERGE-EI in Prague. Affiliated with NES since 2000, he teaches advanced econometrics courses including Econometrics 3, Applied Time Series Econometrics, and Selected Chapters in Econometrics. Education PhD in Economics, University of Wisconsin-Madison (2000) MSc in Economics, New Economic School (1995) Specialist Diploma in Applied Mathematics, Moscow Institute of Physics and Technology (1992) Research Focus : Professor Anatolyev's work centers on econometric theory with expertise in method of moments, time series modeling, and high-dimensional data analysis. His contributions span theoretical developments in factor models, volatility estimation, and instrumental variables methods, alongside practical applications in financial econometrics and portfolio optimization. He maintains active research collaborations across international institutions. Publication Trends : Recent work demonstrates increasing emphasis on ultra-high-dimensional econometrics, with significant contributions to copula-based portfolio allocation, many-instrument regressions, and financial market belief updating mechanisms. His publications bridge theoretical rigor with empirical applications, frequently appearing in top econometrics journals including Journal of Econometrics and Econometric Theory. Awards Econometric Theory Multa Scripsit Award (2022) for exceptional scholarly output Academic Leadership : As founding Editor-in-Chief of the Russian-language journal Quantile since 2006, he has fostered econometric research dissemination in Eastern Europe. His co-authored textbook Methods for Estimation and Inference in Modern Econometrics serves as a key reference in graduate econometrics education. Professional Activities : Regularly presents at international conferences and serves as referee for leading econometrics journals, maintaining active engagement with the global econometrics community through seminar presentations and collaborative research projects.
Shengxian Ding is a Postdoctoral Associate at the Yale School of Public Health , focusing on Biostatistics, Neuroscience, and Public Health . Her research develops advanced statistical models for biomedical data analysis. Her work includes 2025: Subgroup Mediation Analysis , 2024: Shape Mediation in Alzheimer’s Disease , and 2023: Tumor Growth Quantification via MRI , reflecting expertise in Regression Models, Neuroimaging, and Computational Biology . Contact: naomi.ding@yale.edu
Frank Röttger is an Assistant Professor at Eindhoven University of Technology, specializing in Mathematical Statistics. His primary research focuses on graphical models, multivariate extremes, and statistical inference in high-dimensional settings. Research Interests : Extreme value theory, probabilistic graphical models, causal inference in extremes, and data-driven risk modeling. Awards : NWO Prize (Scientific) - 2024 Organized Activities : Eurandom Workshop on Graph Laplacians, Multivariate Extremes, and Algebraic Statistics (2024) Causality in Extremes Workshop (2024) Courses Taught : Dependence Modeling Foundations of Statistics Mathematical Statistics Contact : Email: f.rottger@tue.nl
Jennifer Chan is a Professor in the Statistics Department at the University of Sydney's Faculty of Science. She earned her PhD from the University of New South Wales in 1997 and previously lectured at the University of Hong Kong before joining her current institution in 2006. Her research integrates statistical and machine learning models with applications in finance and insurance, including volatility modeling, Bayesian methods, and neural network applications. Her interdisciplinary research focuses on: Generalized linear mixed models and multivariate volatility measures Machine learning techniques for financial risk assessment Bayesian robustness and portfolio optimization Time-series analysis of cryptocurrencies and equity markets Recent publications demonstrate strong focus on Bayesian models in finance (42% of last 15 papers), machine learning applications (33%), and actuarial science (25%), with emerging emphasis on neural networks for financial forecasting. Awards & Honors: Second prize, Natural Science Award of China's Ministry of Education (2008) National Drug Strategy Research Scholarship (1994-1996) She supervises doctoral candidates working on machine learning applications in finance and insurance. Her international collaborations include institutions in Israel, Japan, Malaysia, and the United States.
Professor Ralf Kellner holds the Chair of Financial Data Analytics at the University of Passau, Faculty of Economics. His work integrates economics, data science, and statistics, focusing on empirical and application-oriented research to explore how statistical learning and AI can uncover insights in data-driven decision-making processes that generate economic value. He also teaches courses such as Deep Learning and Text Analysis in Finance, Financial Data Analytics and Machine Learning, and Scientific Computing with Python. His research examines the intersection of financial markets, statistical learning, and artificial intelligence, with specific interests in modeling adverse financial developments, systemic risks, and analyzing text data via domain-specific language models. Publications highlight collaborations with researchers like D. Rösch and N. Gatzert. Recent publications include work on hybrid service agents, quantile neural networks, default resolution time analysis, Bayesian sovereign bond risk models, and international diversification studies. His methodological approaches span extreme value theory, quantile regression, and multivariate statistical techniques applied to financial and insurance contexts. Contact: ralf.kellner@uni-passau.de
Mathias Beiglböck is a full Professor at the Department of Mathematics within the Faculty of Mathematics at the University of Vienna. His research spans multiple areas of mathematical analysis with a strong focus on probability theory and its applications to finance and other fields. With over 60 publications spanning from 2009 to 2024, Beiglböck has established himself as a leading researcher in his field. Beiglböck's primary research interests center around optimal transport theory, martingale theory, and their applications to mathematical finance. His work explores the deep connections between probability theory and financial mathematics, particularly in areas such as option pricing, risk management, and stochastic processes. His research also extends to epidemiological modeling, as evidenced by his contributions to SARS-CoV-2 research during the pandemic. Analysis of his recent publications (2022-2024) reveals a strong focus on advancing the theoretical foundations of optimal transport and martingale theory while finding novel applications in finance and data science. His work often bridges pure mathematical theory with practical applications, particularly in financial modeling and risk assessment. The high citation counts across his publications (some exceeding 100 citations) indicate significant impact in his field. Beiglböck has collaborated extensively with researchers across Europe, particularly with scholars from France, Austria, and the UK. His work on the COVID-19 pandemic demonstrates his ability to apply mathematical expertise to pressing real-world problems, contributing to public health policy through rigorous quantitative analysis.
Archer Yang is an Associate Professor in the Department of Mathematics and Statistics at McGill University, with additional affiliations as an Associate Academic Member of Mila - Quebec AI Institute, Associate Member of the School of Computer Science, and Member of the Quantitative Life Science Program. His academic journey began with a PhD from the University of Minnesota under the supervision of Hui Zou, establishing his foundation in statistical methodology and machine learning. Dr. Yang's research spans three interconnected themes: statistical machine learning, applications in drug discovery, and computational genomics and healthcare. In statistical machine learning, he focuses on developing dimensionality reduction, probabilistic models, and causality-inspired methods to address complex high-dimensional data challenges. His work in drug discovery involves creating machine learning models to accelerate drug candidate identification and enhance understanding of drug efficacy and safety. In computational genomics and healthcare, he develops techniques to analyze genomic data, identify biomarkers, and explore the genetic basis of diseases, with the goal of improving precision medicine and predicting patient outcomes. His overarching objective is to bridge advanced data-driven methodologies with impactful applications in pharmacology, genomics, and healthcare. His recent publications reveal a strong trend toward applying machine learning to healthcare challenges, particularly in congenital heart disease analysis, mortality prediction, and drug discovery. His work demonstrates expertise in developing interpretable models that can handle complex, high-dimensional biomedical data while maintaining statistical rigor. The integration of causal inference methods with machine learning appears to be a growing focus in his research trajectory. ICML Spotlight Paper (top 2.6%, 313/12,107) Dr. Yang actively supervises a large research group including multiple postdoctoral fellows, PhD students, and Master's students. His lab has developed several notable software tools, including ml-mr for machine learning in Mendelian randomization. His supervision extends across statistics, computer science, and biomedical applications, reflecting the interdisciplinary nature of his work. He appears to maintain strong collaborative relationships with researchers in healthcare and genomics fields. His laboratory, the Archer Yang Lab, maintains active GitHub repositories focused on machine learning applications in healthcare and drug discovery, with particular emphasis on interpretable models and statistical methodology development. The lab appears to work at the intersection of theoretical statistics and practical biomedical applications, with projects spanning from algorithm development to clinical implementation.
Yongcheng Qi is a Professor at the Department of Mathematics and Statistics, Swenson College of Science and Engineering, University of Minnesota - Duluth. His research focuses on probability theory, extreme-value statistics, and random matrix theory. PhD, University of Georgia (2001) PhD, Peking University (1992) B.S., Peking University (1987) Qi’s work explores applied probability, bootstrap methods, copulas, and high-dimensional statistics. Recent publications analyze spectral radii of random matrices, tail dependence functions, and empirical likelihood techniques. His research has been supported by NSF and NSA grants, including projects on extreme value theory, high-dimensional normal distributions, and asymptotic distributions for branching processes. Qi is affiliated with the American Statistical Association and the Institute of Mathematical Statistics.
Helle Sørensen is a Professor at the Department of Mathematical Sciences, University of Copenhagen. Her work bridges theoretical and applied statistics with interdisciplinary applications in biological and environmental sciences. Education : BSc (1993), MSc (1997), PhD (2000) in Statistics from University of Copenhagen. Employment : Professor (2018–present), Head of Data Science Lab (2018–2021), Professor MSO and head of Laboratory for Applied Statistics (2013–2018), Associate/Assistant Professor across multiple departments (2000–2013). Research Interests focus on: Functional data analysis Statistical inference for dependent data and stochastic processes Applications in biology, agriculture, and food science Her recent publications highlight statistical methodologies applied to: Enzymatic degradation of plant material Multivariate analysis in metabolic studies Random forest efficiency in metric spaces Quantile regression for longitudinal data Child food texture preferences and insect acceptance Teaching includes courses in basic probability, statistical theory, and applied statistics for bio/life sciences students. She supervises BSc, MSc, and PhD students in Statistics with co-supervision roles in interdisciplinary fields.
Harry Joe is a Professor in the Department of Statistics at the University of British Columbia (Vancouver Campus). His primary research focuses on dependence modeling, copula theory, multivariate analysis, and applications in biostatistics, finance, and psychometrics. He has advised students including Xiaoting Li, Xinyao Fan, and Pavel Krupskiy. Research Interests: - Advanced copula constructions (e.g., vine copulas) - Extreme value theory and tail dependence - Applications in financial risk, biomedical research, and educational measurement - Multivariate time series analysis and non-Gaussian models Publications highlight contributions to copula-based classification methods (2024), factor copula models (2015), and dynamic dependence modeling (2020). His work bridges theoretical developments with practical applications across disciplines. Teaching and advising emphasize methodological innovation. Current research explores high-dimensional dependence structures and computational methods for complex data. No lab/team affiliations explicitly noted in provided materials.
Yi Feng is an Assistant Professor in Quantitative Psychology at the University of California, Los Angeles (UCLA). With expertise in advanced statistical methodologies, Feng contributes to both theoretical and applied research in psychology and education, focusing on structural equation modeling (SEM), latent growth models, and causal graphical frameworks. They teach graduate-level SEM courses (Psych M257) and undergraduate statistics (Psych 100A). Research Focus: Causal inference, variability modeling, and power analysis with interdisciplinary applications in education, developmental psychology, and healthcare. Key Collaborations: Regularly works with researchers like Gregory R. Hancock and Jeffrey R. Harring on methodological innovations. Feng’s methodological work appears in high-impact journals such as Psychological Methods (Impact Factor: 14.738) and JAMA Internal Medicine (Impact Factor: 44.41). Their applied research spans topics like socioeconomic disparities in science achievement, cognitive trajectories post-surgery, and mental health service delivery. Feng also explores synthetic data applications for longitudinal educational research.
Ivan Jeliazkov is an Associate Professor of Economics and Statistics at the Department of Economics, University of California, Irvine. His research focuses on Bayesian econometrics and simulation-based inference, emphasizing methodologies like Markov chain Monte Carlo and econometric modeling. He holds a Ph.D. in Economics from Washington University in St. Louis and a BA in Economics and Business Administration from Coe College. His key research areas include Bayesian Econometrics, advanced simulation techniques, and causal inference applications. Notable work addresses heteroskedasticity in causal studies, quantile analysis of rental markets, and simultaneous equation models for discrete data. Recent advising includes 2024 Ph.D. graduates Robert MacDonald, Parush Arora, and Jieyu Gao. His articles span topics like dynamic factor models, regression discontinuity designs, and model comparison techniques. He has contributed to interdisciplinary research in marketing, finance, and historical economic analysis. His methodological innovations emphasize practical applications of Bayesian methods to address econometric challenges such as uncertainty quantification and model specification. Current work continues advancing computational tools for complex econometric problems.
