جست‌وجوی استاد و پژوهشگر

Vadim Sokolovمشاهده پروفایل

دانشیار

• Systems Engineering
• Operations Research
• Transportation Systems Analysis
• +۶ مورد دیگر

Vadim Sokolov is an Associate Professor in the Department of Systems Engineering and Operations Research at George Mason University (GMU), with affiliate status in the College of Engineering and Computing (CEIE). His research focuses on developing robust methodologies for large-scale systems analysis, combining agent-based modeling, Bayesian statistical techniques, and computational experimentation. He co-developed Polaris, an open-source mobility simulator widely used in transportation network analysis by government entities. Previously, Sokolov served as a Principal Computational Scientist at Argonne National Laboratory, a Fellow at the University of Chicago's Computation Institute, and a Lecturer at the University of Chicago. He holds a Ph.D. in Computational Mathematics from Northern Illinois University. His research interests span agent-based modeling, Bayesian analysis, transportation systems, and computational software development. Notable contributions include work on stochastic computer models, deep learning applications in traffic flow prediction, and quantum Bayesian computation. He has published in top-tier journals such as the Annals of Applied Statistics and Transportation Research Part C. Though no awards are explicitly listed, his extensive publications and industry collaborations highlight his impactful work in computational systems and transportation analysis.

Kent Staleyمشاهده پروفایل

استاد

• Philosophy of Science
• Philosophy of Physics
• Philosophy of Statistics
• +۳ مورد دیگر

Kent Staley is a Professor of Philosophy at Saint Louis University, affiliated with the College of Arts and Sciences. His expertise lies in the philosophy of science, physics, and statistics, with a focus on issues of scientific methodology, measurement theory, and error statistics. Staley holds a Ph.D. in Philosophy from Johns Hopkins University (1998) and a B.A. in Physics from Grinnell College (1986). His research explores foundational questions in scientific inquiry, including the justification of measurement results, the role of inductive risk in decision-making, and the epistemological foundations of statistical practices in high-energy physics. His notable works include An Introduction to the Philosophy of Science (Cambridge, 2014) and studies on the top quark discovery process. Recent publications address topics such as statistical significance in scientific discourse, the IPCC's confidence measures, and pragmatic justifications for frequentist methods. Staley's work bridges philosophical analysis with case studies from physics and climate science, emphasizing the practical dimensions of securing reliable scientific evidence. He has contributed to debates on experimental methodology, model criticism, and the historical evolution of scientific standards in particle physics.

Anna Scampicchioمشاهده پروفایل

پژوهشگر

• Intelligent Control Systems
• Model Predictive Control
• Machine Learning
• +۳ مورد دیگر

Dr. Anna Scampicchio is a Researcher at ETH Zürich, affiliated with the Professorship for Intelligent Control Systems. Her work focuses on advancing control theory and machine learning methodologies, particularly in data-driven control systems, model predictive control, and Bayesian learning techniques. She has contributed to the development of algorithms for system identification, optimal control, and robust control strategies. Her research integrates theoretical analysis with practical applications in robotics and dynamical systems. Key publications include studies on kernel methods, randomized signatures for learning dynamics, and Bayesian multi-task learning approaches. Dr. Scampicchio holds a Ph.D. (implied by her title) and has published extensively in top-tier journals and conferences, addressing challenges in intelligent control systems and machine learning integration.

• Statistical Inference
• Confidence Intervals
• Model Selection
• +۵ مورد دیگر

Dr. Paul Kabaila is an Associate Professor of Statistics at La Trobe University's Mathematics & Statistics school. He holds a PhD from the University of Newcastle and prior degrees from the University of New South Wales. His research focuses on statistical inference methodologies, particularly confidence intervals affected by preliminary model selection, frequentist methods utilizing uncertain prior information, and model-averaged confidence intervals. He has supervised numerous PhD students across diverse fields including biostatistics, bioinformatics, and business analytics. Dr. Kabaila has held visiting appointments at prestigious institutions including Oxford University and the Australian National University. He secured an ARC Discovery Grant (2002–2005) advancing statistical analysis of count data, with applications in epidemiology and finance. He led La Trobe University's first Statistics Program accreditation by the Statistical Society of Australia. His recent work emphasizes high-dimensional data challenges in statistical/machine learning and post-shrinkage strategies. He received the 2020 Distinguished Author Award from the Journal of Time Series Analysis for sustained contributions. His teaching spans all levels of statistical education, including advanced subjects like Statistical Inference and Theory of Statistics .

• Statistical Machine Learning
• Bayesian Statistical Inference
• Statistical Ecology
• +۷ مورد دیگر

Jabed Tomal is an Associate Professor in Statistics and Data Science at Thompson Rivers University (TRU), Canada. Previously, he held positions as an Assistant Professor at TRU (2018–2023) and the University of Toronto Scarborough (2014–2018), and a Postdoctoral Fellow at the University of British Columbia (2014). He earned a Ph.D. in Statistics (2013) from UBC, specializing in statistical machine learning, and dual M.Sc. degrees in Statistics (University of Windsor, 2007) and Biostatistics (University of Dhaka, Bangladesh). His research focuses on ensemble methods, Bayesian inference, and statistical ecology, with applications in drug discovery, protein homology, and environmental modeling. Key research interests include developing ensemble models for high-dimensional data, Bayesian methods for breakpoints detection in housing markets and ecological systems, and statistical approaches in healthcare. Notable contributions include work on QSAR studies, Bayesian hierarchical modeling of pandemic impacts, and ecological threshold detection. Tomal has secured grants such as the NSERC Discovery Grant ($102,500) and TRU internal funds for projects in big data and environmental thresholds. He has advised numerous graduate and undergraduate students on topics ranging from machine learning in healthcare to single-cell RNA sequencing analysis. His teaching spans courses like Bayesian Machine Learning, Multivariate Statistics, and Theoretical Machine Learning at the graduate level, alongside foundational statistics and calculus courses at TRU and the University of Dhaka. Administrative roles include Chair of the Award and Scholarship Committee for TRU’s Master of Data Science program and membership in Senate Research Committee. Education: Ph.D., Statistics (2013), UBC Vancouver M.Sc., Statistics (2007), University of Windsor M.Sc., Biostatistics (2005), University of Dhaka Awards: NSERC Discovery Grant (2021–2026) Research Training Recognition Fund (TRU,多次) SSC 2013 Talk Honourable Mention Labs/Teams: Active in interdisciplinary projects at TRU’s Department of Mathematics and Statistics, focusing on data science applications in ecology, healthcare, and genetics.

Joe Blitzsteinمشاهده پروفایل

استاد

• Probability theory
• Statistical inference
• Bayesian methods
• +۲ مورد دیگر

Joe Blitzstein is Professor of Statistics at Harvard University, specializing in probability theory and statistical methodologies. His work bridges Bayesian and frequentist approaches while exploring foundational statistical paradoxes. Research domains include: Probability distributions and stochastic processes Bayesian-frequentist reconciliation frameworks Educational innovations in statistics pedagogy

Olivia Feldmanمشاهده پروفایل

دانشیار

• Mathematical Biology
• Infectious Disease Dynamics
• Computational and Applied Mathematics
• +۴ مورد دیگر

Olivia Feldman is an Associate Professor in the Department of Mathematics at the University of Tennessee. Her research focuses on the intersection of mathematics and biology, particularly in developing mathematical models to understand infectious disease dynamics. Key areas include vector-borne diseases, drug resistance, and spatial transmission patterns. She holds a Ph.D. from the University of Florida and has been supported by grants such as the NSF CAREER award for designing optimal sampling strategies in epidemiological models. Her work integrates pharmacokinetics/pharmacodynamics with population-level disease transmission, explores within-vector parasite diversity, and examines spatial heterogeneity's role in disease spread. Recent grants include DMS-1816075 (collaborative research on vector-borne diseases and drug resistance) and DMS-2045843 (CAREER grant on sampling strategies). Education: Ph.D., University of Florida Grants: NSF CAREER (2021-2026), Collaborative Research on Drug Resistance (2018) Publications span topics like malaria dynamics, pandemic modeling, and optimal sampling methods. She emphasizes bridging mathematical models with real-world data through methodologies addressing model identifiability challenges.

Nicola Paolettiمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Multi-Agent Systems
• +۳ مورد دیگر

Nicola Paoletti is a Researcher affiliated with the Centre for Intelligent Systems. His work focuses on artificial intelligence, machine learning, and automated negotiation strategies, contributing to the UN Sustainable Development Goals. He has published extensively on topics such as deep reinforcement learning for negotiation, multi-agent systems, and Bayesian methods. Research Interests: - Development of negotiation algorithms using reinforcement learning - Automated e-market negotiation models - Bayesian vs. frequentist approaches in predictive monitoring - User preference uncertainty in multi-issue scenarios Recent publications highlight advancements in strategy templates for bilateral negotiations and neural predictive monitoring techniques. His work is referenced in patents and widely shared on platforms like Mendeley, demonstrating impactful contributions to AI-driven decision-making systems. Advising and Grants: - Supervised 1 student (specific details not disclosed) - Active in collaborative research networks focusing on global applications of AI Labs/Teams: - Member of the Centre for Intelligent Systems, driving interdisciplinary research in AI and multi-agent systems.

Richard Paapمشاهده پروفایل

استاد

• Econometrics
• Bayesian Econometrics
• Time Series Analysis
• +۷ مورد دیگر

Richard Paap is a Full Professor of Econometrics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He earned his PhD from the same institution in 1997 and currently serves as Academic Director of the Master Econometrics program. He is actively involved in research, teaching, and academic management. His research focuses on econometric modeling in marketing and macroeconomics, employing both Bayesian and frequentist methodologies. Key areas include time series analysis, panel data, regime switching, and market response models. He has made significant contributions through publications in leading journals such as Journal of Applied Econometrics , Journal of Business & Economic Statistics , and Journal of Marketing Research . His recent publications reveal a strong trend in applying advanced econometric techniques to real-world problems—forecasting in panel regressions, modeling professional forecasters' behavior, estimating expert loss functions, and analyzing inflation dynamics with regime switches. He also contributes to marketing with studies on store price image and brand choice modeling, and to macroeconomics with work on recessions, monetary policy, and growth differences. Richard Paap co-authored two influential books: Quantitative Models in Marketing Research and Periodic Time Series Models , both widely used in academic and applied settings. He teaches courses such as Bayesian Econometrics, Marketing Models, and Case Studies in Applied Econometrics, and supervises bachelor’s and master’s theses in econometrics and management science. He chairs the Program Committee for Econometrics & Operations Research, indicating leadership in academic program development. Although no formal awards are listed, his extensive publication record and book authorship reflect high scholarly impact. He advises students and collaborates widely, particularly with P.H. Franses, D. Fok, and D.J.C. van Dijk. While no specific lab or research center is named, his work is centered within the Econometric Institute, a leading research group in quantitative economics. His future work likely continues to bridge econometric theory with applications in marketing and macroeconomic policy.

Regina Nuzzoمشاهده پروفایل

استاد

• Statistics Education
• Statistical Communication
• Research Methodology
• +۳ مورد دیگر

Dr. Regina Nuzzo is a Professor of Mathematics at Gallaudet University, affiliated with the School of Science, Technology, Accessibility, Mathematics, and Public Health. She holds a Ph.D. in Statistics from Stanford University (2003) and a master's in Science Writing from UC Santa Cruz (2004). Her work bridges statistical research, education, and public communication, emphasizing reproducibility and clear data interpretation. Dr. Nuzzo's research focuses on statistical methodology, particularly in addressing issues like p-value misuse, communication of risk and relative risk, and improving statistical literacy. She has authored influential articles in Nature , Science , and New York Times , including her landmark 2014 piece on p-values that earned an ASA award. She co-authored the seminal 2016 ASA P-Values Statement, reshaping statistical practice. Education: Ph.D. in Statistics (Stanford), B.S. in Industrial Engineering (USF) Teaching: Courses span applied statistics, probability, and research methods for social sciences Awards: ASA Excellence in Statistical Reporting Award (2014) Her advisory roles include mentoring doctoral students in linguistics and psychology, and she actively contributes to workshops on statistical communication for journalists and researchers. She promotes accessible quantitative education for diverse audiences, including deaf and hard-of-hearing learners.

Herbert Hoijtinkمشاهده پروفایل

استاد

• Bayesian Statistics
• Multivariate Statistics
• Structural Equation Modeling
• +۳ مورد دیگر

Herbert Hoijtink is a Professor of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences. He leads the Hoijtink Methodology and Statistics research group and specializes in Bayesian statistical methods, particularly the evaluation of informative hypotheses. His work focuses on improving empirical research through prior knowledge integration, with applications in psychology, education, and biomedical sciences. Research interests include Bayesian model comparison, multivariate statistics, and structural equation modeling. He has developed the bain software package for Bayesian evaluation of inequality-constrained hypotheses. Key contributions include advancing sample size determination for multilevel models and promoting open science practices through the Open Empirical Cycle . Hoijtink has secured significant grants, including NWO VICI and Gravity grants (€27.6 million), and holds fellowships like the NIAS-KNAW residency (2019). He co-authored influential papers on Bayesian replication studies and contributed to the Redefine Statistical Significance debate. His work bridges methodological innovation with practical applications in clinical, developmental, and educational research. Key Projects: Bayesian sample size calculation for longitudinal data, NWO-funded studies on child development Labs/Teams: Methodology and Statistics group at Utrecht University Awards: Psychometric Society Award (2016), VICI Grant (2005)

Zhengyang Zhouمشاهده پروفایل

دانشیار

• Aging
• Alzheimer's disease
• Biostatistics
• +۵ مورد دیگر

Dr. Zhengyang Zhou is an Associate Professor in the College of Public Health at the University of North Texas Health Science Center, Department of Population and Community Health. His research focuses on statistical methodology development and collaborative interdisciplinary studies in aging/Alzheimer’s disease, health behaviors/substance use, and statistical genetics. He holds a PhD in Biostatistics from Southern Methodist University and has been actively funded since 2017 with 16 sponsored projects including NIH grants. His work includes 56 peer-reviewed publications with an h-index of 14 and 523 citations. Education: BS in Mathematics, Beijing Institute of Technology MS in Statistics, Southern Methodist University PhD in Biostatistics, Southern Methodist University Research Interests: Statistical method development for real-world challenges Evaluation of existing statistical methods Large-scale interdisciplinary collaborations in Alzheimer’s disease, substance use, and genetic epidemiology Awards: 2021 Junior Investigator Award, Research Society on Alcoholism Grants & Projects: PI/Co-I on 16 projects including NIH/NIA grants (e.g., Health & Aging Brain Study-HD) Focus areas: computational tools, genomic screens, and population health disparities Labs/Teams: Leads a research group studying Alzheimer’s disparities in Mexican/African American populations using advanced ML/AI and statistical techniques.

Jean-Michel Etienneمشاهده پروفایل

دانشیار

• Labor Economics
• Innovation Economics
• Health Economics
• +۳ مورد دیگر

Jean-Michel Etienne is an Associate Professor at the University Paris-Saclay's Department of Economics. He teaches microeconomics, industrial economics, and econometrics across bachelor's and master's programs. His administrative roles include pedagogical coordination for the L2 Economics & Management program, accelerated L1/L2 programs, and the Master II Innovation, Valorisation de la Recherche (IVR). He also co-leads the Master II Innovation, Marché et Science des Données (IMSD). His research focuses on applied microeconometrics with two core axes: frequentist approaches (labor market inequalities, health disparities, fish market pricing, hedonic housing prices, innovation-inclusive growth links, econometric model development, and nighttime light pollution) and Bayesian methodologies (R&D determinants, innovation-productivity links, CO2-economy relationships, economic growth drivers, and healthcare risks). Key research contributions include studies on neonatal ICU efficiency, OECD growth models, carbon emissions linkages, and digital media business models. Teaching responsibilities span microeconomics, econometrics, time series analysis, and industrial economics across multiple institutions including Université Paris-Saclay and Institut Mines-Télécom.

• Bayesian Statistics
• Machine Learning
• Statistical Modeling
• +۴ مورد دیگر

Natalia Bochkina is a Professor in Statistics at the School of Mathematics, University of Edinburgh. She holds a PhD in Statistics from the University of Bristol. Her research focuses on Bayesian inference, mis-specified statistical models, and non-regular models, with applications to machine learning and interdisciplinary collaborations. She explores the theoretical foundations of statistical methods, comparing Bayesian and frequentist approaches in scenarios like model mis-specification or non-normal distributions. Education: PhD in Statistics, University of Bristol. Her work bridges theory and practice, emphasizing collaboration with domain experts such as biologists and clinicians to ensure statistical models are applicable and relevant. She is particularly interested in Bayesian approaches to ranking problems, exemplified by her work on tennis ranking algorithms. Bochkina advocates for statisticians to engage directly with data producers to validate assumptions and improve practical utility. She has no listed scientific awards but has a notable early achievement: finishing second in the Soviet Union Chemistry Olympiad during her school years. Teaching is a priority for her, with experience in tutorials and lectures across disciplines, including adapting explanations for non-mathematics students (e.g., biology students struggling with differentiation concepts). Natalia highlights evolving university experiences, noting increased internship opportunities and the value of oral exams in fostering conceptual integration. Her research and teaching reflect a commitment to rigorous statistical theory coupled with real-world applicability.

Torkel Erhardssonمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Processes
• Statistical Inference
• +۷ مورد دیگر

Torkel Erhardsson is an Associate Professor (Docent) in the Department of Mathematics at Linköping University, Sweden. He is affiliated with the Division of Applied Mathematics (TIMA), where he conducts research in probability theory, stochastic processes, and statistical inference. His research interests include: Bounds for distances between probability distributions using Stein's method and couplings Applications to normal and compound Poisson approximations Bayesian nonparametric inference and asymptotics of posterior distributions Higher-dimensional autoregressive processes with random coefficients Reciprocal chains and reciprocal random fields on undirected graphs His recent publications (2008–2023) in journals such as Journal of Applied Probability , Advances in Applied Probability , and IEEE Transactions on Automatic Control reflect a strong focus on theoretical probability and its applications, particularly in approximation methods and stochastic modeling. The work shows increasing emphasis on graphical models and reciprocal processes in recent years. There are no listed scientific awards or honors in the provided materials. Torkel Erhardsson actively contributes to the academic life at Linköping University, participating in seminars in statistics and mathematical statistics. There is no public information indicating student advising, grant funding, or leadership of specific research labs or teams.