جست‌وجوی استاد و پژوهشگر

Hillel J. Bavliمشاهده پروفایل

دانشیار

• Evidence Law
• Torts
• Law and Statistics
• +۵ مورد دیگر

Hillel J. Bavli is a full-time Associate Professor of Law at the Dedman School of Law at Southern Methodist University. His work bridges legal doctrine with quantitative analysis, focusing on evidence law, torts, and the application of statistics to legal problems. Education B.A., Economics – Boston University J.D. – Fordham University School of Law LL.M. – Harvard Law School A.M., Statistics – Harvard University Ph.D., Statistics in Law and Government – Harvard University Professor Bavli’s research interests center on character evidence , statistical applications in legal contexts , and causation doctrines . He explores how implicit biases influence legal outcomes and advocates for evidence-based reforms in judicial procedures. His scientific awards include a Fulbright Fellowship, a fellowship at the Harvard Institute for Quantitative Social Science, and a visiting fellowship at Yale Law School’s Center for Private Law. He also received multiple teaching awards at Harvard University. Key publication trends reveal expertise in: Character evidence reform (e.g., Trial By Character , 2025) Statistical reliability in class actions (e.g., The Admissibility of Sampling Evidence , 2018) Causation frameworks in civil rights (e.g., Causation in Civil Rights Legislation , 2021) Implicit bias in legal decision-making (e.g., Character Evidence as a Conduit for Implicit Bias , 2023) He has collaborated with Reagan Mozer and John K. Felter on empirical studies and served on committees such as the American Statistical Association Advisory Committee on Forensic Science and the AALS Section on Evidence Executive Committee.

Joanna Tyrchaمشاهده پروفایل

استاد

• Statistical Inference
• Econometrics
• Neuroscience
• +۶ مورد دیگر

Joanna Tyrcha is a Professor in Mathematical Statistics at the Department of Mathematics, Stockholm University, and currently serves as the Head of the Department. Her academic career spans institutions in Poland, Canada, and Sweden, with roles including Lecturer, Director of Undergraduate Studies, and Head of the Division of Mathematical Statistics. M.S., Mathematical Statistics, Warsaw School of Economics, 1980 Ph.D., Mathematics, Jagiellonian University, 1989 Associate Professor, Mathematical Statistics, Stockholm University, 2003 Professor, Mathematical Statistics, Stockholm University, 2011 Her research integrates statistical inference , econometrics , and neuroscience , focusing on cell cycle models , neural connectivity , biological information processing , causality , and ion channel modeling . Recent publications highlight applications of Ising models to neural data, nonstationary dynamics , and transfer entropy in non-Gaussian distributions. Key collaborations include work with J. Hertz and Y. Roudi on network inference, and with C. Nellåker on mixture models for genomic data. Joanna has taught courses such as Probability Theory and Econometrics . She leads the Mathematical Statistics research group at Stockholm University, which focuses on biostochastics , biostatistics , discrete random structures , and finance/insurance applications.

Dale J. Poirierمشاهده پروفایل

استاد

• Bayesian Econometrics
• Statistical Modeling
• Econometric Theory
• +۳ مورد دیگر

Dale J. Poirier is a Professor of Economics at the University of California-Irvine, where he is affiliated with the Department of Economics in the School of Social Sciences. His office is located in Social Science Plaza, Building A, Room 4175. He teaches courses including Econometrics I (123A), Statistics and Econometrics I (220A), Statistics and Econometrics II (220B), and the Econometrics Colloquium (229). Dale Poirier's research focuses on Bayesian econometrics and statistical modeling. His work spans theoretical developments in Bayesian inference, econometric methodology, and applications across various domains including health economics, political science, and time series analysis. He has made significant contributions to understanding the growth and application of Bayesian methods in economics and statistics since 1970, as documented in his influential 2006 paper 'The Growth of Bayesian Methods in Statistics and Economics Since 1970.' Analysis of Poirier's recent publications reveals a strong emphasis on Bayesian methods in econometrics, with applications ranging from partial identification in econometric models to analyzing violence dynamics in political conflicts. His work demonstrates both theoretical rigor and practical relevance, addressing challenges in model specification, identification, and statistical inference across diverse fields including health outcomes, political violence, and financial modeling. Dale Poirier has contributed significantly to econometrics education through his teaching and publications. His textbook 'Intermediate Statistics and Econometrics: A Comparative Approach' has been widely used, and he provides detailed course materials including syllabi and corrections for his courses. He has supervised numerous graduate students in economics and statistics, contributing to the development of the next generation of econometricians.

Abdul-Lateef Haji-Aliمشاهده پروفایل

دانشیار

• Monte Carlo Methods
• Stochastic Differential Equations
• Importance Sampling
• +۳ مورد دیگر

Abdul-Lateef Haji-Ali serves as Associate Professor in the Actuarial Mathematics & Statistics department at Heriot-Watt University's School of Mathematical and Computer Sciences, Edinburgh. His research focuses on computational methods for stochastic systems with applications in finance and engineering, maintaining active supervision of PhD students. His primary expertise spans Monte Carlo methodologies, particularly multilevel techniques and importance sampling for rare events in stochastic differential equations. Current work integrates machine learning with traditional Monte Carlo approaches, emphasizing McKean-Vlasov systems and partial differential equations under uncertainty. Recent publications (2024-2025) reveal concentrated advancement in rare-event simulation for complex stochastic models, with growing interdisciplinary connections to generative AI through diffusion models. Key innovations include antithetic schemes for non-commutative noise systems and double-loop importance sampling frameworks. His scientific recognition includes: Leslie Fox Prize (2nd place, June 2019) Dr. Haji-Ali actively recruits PhD candidates for projects on Adaptive Monte Carlo Methods for Stochastic Differential Equations. His collaborative network spans international institutions, with recent co-authorship including Tempone, Pereyra, and Zygalakis, indicating robust research funding through sustained high-impact publication output.

• Blockchain
• Cryptocurrency
• Quantum Computing
• +۳ مورد دیگر

Dr. Mindaugas Juodis is a Researcher at the Blockchain and Quantum Technologies Group within Vilnius University's Institute of Data Science and Digital Technologies. His work centers on advanced statistical modeling of blockchain systems and decentralized networks. His research spans blockchain, cryptocurrency, quantum computing, and probability theory. Key investigations include Bitcoin price regime shifts using Bayesian methods, Ethereum transactional decentralization metrics, and wealth distribution analysis in blockchain networks. Earlier theoretical work focused on self-normalized sums, central limit theorems, and functional limit theorems for dependent processes. Publications reveal a clear trajectory from theoretical statistics (2004-2007) to applied blockchain analytics (2024-2025). Recent work bridges mathematical rigor with cryptocurrency applications, appearing in Mathematics, ICT Express, and IEEE conferences. His research demonstrates expertise in translating complex statistical frameworks to real-world decentralization problems. As part of the Blockchain and Quantum Technologies Group, he contributes to cutting-edge research in blockchain analytics and quantum computing intersections, focusing on empirical validation of decentralization metrics and network properties.

Marc Langeمشاهده پروفایل

استاد

• Philosophy of Science
• Metaphysics
• Epistemology
• +۳ مورد دیگر

Marc Lange is a Professor at the Department of Philosophy, Rutgers University. His research focuses on the philosophy of science, metaphysics, epistemology, and the philosophy of physics, biology, and mathematics. Research Interests: Marc explores the nature of laws of nature, non-causal scientific explanations, and explanatory proofs in mathematics. His work bridges abstract philosophical theories with practical scientific applications, emphasizing the interplay between metaphysics and empirical science. Selected Publications: Notable works include Laws and Lawmakers: Science, Metaphysics, and the Laws of Nature (2009) and Because Without Cause: Non-Causal Explanations in Science and Mathematics (2016). His recent papers address topics such as counterfactual invariance and the role of mathematical explanation in scientific practice. Scientific Awards: Winner of the 2014 Dialectica Prize Competition Shared top prize in the Analysis Annual Essay Competition (1993) Advising and Collaborations: Marc has collaborated with researchers like Katie Elliott and Thomas Hofweber, contributing to interdisciplinary dialogues in philosophy and physics.

Soroush Rafiee Radمشاهده پروفایل

پژوهشگر ارشد

• Probability Theory
• Logic
• Philosophy of Mathematics
• +۵ مورد دیگر

Dr. Soroush Rafiee Rad is a Research Fellow in the Department of Philosophy at the University of St Andrews, working within the School of Philosophical & Anthropological Studies. His research focuses on the intersection of logic, probability theory, and philosophy, with particular emphasis on foundational aspects of quantum mechanics, Bayesian inductive logic, and social choice theory. Dr. Rafiee Rad's research interests span multiple areas including: Probability Theory and its philosophical foundations Quantum Logic and mathematical structures in quantum mechanics Bayesian Inductive Logic and objective probability Order Theory and lattice structures Entailment relations and reasoning with inconsistencies Deliberative democracy and social choice theory His recent publications demonstrate a strong interdisciplinary approach, bridging philosophy, mathematics, and social sciences. The research trends indicate a consistent focus on formal methods to address philosophical questions, particularly in the areas of uncertainty representation, logical foundations of quantum mechanics, and collective decision-making processes. His work often involves collaborations with researchers from diverse fields, reflecting the interdisciplinary nature of his research agenda. Dr. Rafiee Rad is affiliated with the Logic & Metaphysics research group at the University of St Andrews.

• Probability Theory
• Mathematical Statistics
• Random Measures
• +۴ مورد دیگر

Riccardo Passeggeri is an Assistant Professor (with tenure) in Statistics at the Department of Mathematics, Imperial College London. He serves on the Applied Probability Section committee of the Royal Statistical Society and maintains an office at Huxley 6M14. His research bridges theoretical probability and applied statistical methodologies. Research Focus: Professor Passeggeri investigates foundational and applied aspects of statistics, with emphasis on: Advanced probability frameworks (random measures, stochastic analysis) Bayesian non-parametric methods Theoretical robustness in statistical models Extreme value distributions and their applications He actively seeks PhD candidates to collaborate on projects within these domains.

• Neuropsychology
• Traumatic Brain Injury
• Alzheimer's Disease
• +۳ مورد دیگر

Jeff Schaffert serves as an Assistant Professor of Psychiatry at UT Southwestern Medical Center (UTSW), where he completed his doctoral and residency training. As a clinical neuropsychologist, he provides outpatient services in the UTSW neuropsychology clinic and inpatient care at University Hospital, specializing in cognitive assessment and dementia-related disorders. Dr. Schaffert's educational background includes doctoral and residency training exclusively at UT Southwestern Medical Center, though specific degree dates and institutions prior to UTSW are not detailed in available sources. His primary research investigates the impact of head-injury exposure on later-life cognitive decline, with particular emphasis on traumatic brain injury (TBI) as a risk factor for Alzheimer's disease and related dementias. He examines how sports-related head trauma, especially in NFL retirees, contributes to chronic traumatic encephalopathy (CTE) and accelerates neurodegenerative processes. His work integrates neuropathological data with clinical neuropsychological assessments to identify biomarkers and risk predictors for dementia onset. Analysis of his 2020-2023 publications reveals a concentrated focus on three interconnected domains: 1) The relationship between TBI/CTE and Alzheimer's pathology using autopsy-confirmed cases; 2) Neuropsychological outcomes in aging athletes, particularly NFL retirees; and 3) Methodological advancements in dementia diagnostics through Bayesian modeling and biomarker validation. His research consistently addresses gaps in understanding how head trauma modifies dementia trajectories across diverse populations. No scientific awards, fellowships, or major honors are documented in the provided materials. While clinical mentoring roles are implied through his faculty position, no formal student advisement (PhD/Master's) or grant funding details are specified in the available information. His collaborative work primarily involves UTSW colleagues including Cullum, LoBue, Didehbani, and Hart across multiple high-impact publications. Dr. Schaffert's research operates within UT Southwestern's integrated neuroscience and psychiatry infrastructure, though specific laboratory affiliations or dedicated research teams are not explicitly identified in the source text.

• Economic Statistics
• Machine Learning
• Bayesian Statistics
• +۴ مورد دیگر

Naoki Awaya is an Assistant Professor at the School of Political Science and Economics , Waseda University. His research focuses on economic statistics, computational statistics, and financial econometrics with applications in machine learning and Bayesian inference. Education : Not explicitly stated Current Projects : Developing Bayesian estimation methods for nonstationary economic time series and structural changes His work includes tree-based models for probability distributions and advanced MCMC sampling techniques. He teaches graduate-level econometrics courses (Econometrics I/II) using R programming, emphasizing methodological rigor and theoretical foundations. Research interests span high-dimensional data analysis, density ratio estimation, and financial market applications through multivariate Hawkes processes. Recent publications highlight innovations in: Unsupervised tree boosting Hidden Markov Pólya trees Particle rolling MCMC algorithms Nonstationary errors-in-variables models

• Statistical Science
• Earthquake Prediction
• Actuarial Science
• +۵ مورد دیگر

Shunichi Nomura is an Associate Professor at the Faculty of Commerce, Graduate School of Accountancy, with a PhD in Statistical Science from The Graduate University for Advanced Studies (Sokendai). His research bridges statistical methodology with applications in earthquake forecasting and actuarial science. Education: PhD in Statistical Science (Sokendai). He develops Bayesian space-time models for earthquake recurrence prediction and applies data mining techniques to insurance risk and healthcare data. His work integrates mathematical statistics with seismological observations to improve probabilistic hazard assessments. Recent publications focus on nonstationary renewal processes, slip rate inversion, and sparse estimation methods like group fused lasso. These studies span geophysics, actuarial mathematics, and machine learning applications. Scientific Awards: Actuarial Excellent Paper, The Japanese Actuarial Society (2017). He leads JSPS-funded research projects on spatiotemporal earthquake modeling and Bayesian prediction frameworks. His teaching includes advanced statistics and data science courses for actuarial students.

• Statistical Finance
• Time Series Analysis
• Mathematical Statistics
• +۱ مورد دیگر

Kenichiro Tamaki is an Associate Professor at the School of Political Science and Economics, Waseda University. He holds appointments in both the Faculty of Political Science and Economics and the Graduate School of Economics, with additional sub-affiliation in the Faculty of Social Sciences. His research focuses on statistical methods applied to financial time series analysis. 2008.04 - Present: Associate Professor, Faculty of Political Science and Economics, Waseda University 2005.04 - 2008.03: Research Associate, Faculty of Science and Engineering, Waseda University Dr. Tamaki earned his Doctor of Science from Waseda University. His research interests center on Statistical Finance and Time Series Analysis, with particular emphasis on developing asymptotic theory for financial applications. His work bridges theoretical statistics with practical financial engineering problems, focusing on non-Gaussian processes, locally stationary time series, and higher-order asymptotic methods. His publication record shows consistent research focused on applying advanced statistical techniques to financial problems, particularly in option pricing, bond valuation, and time series modeling with non-Gaussian innovations. His work demonstrates progression from foundational time series asymptotics to more applied financial engineering problems while maintaining rigorous statistical methodology. Dr. Tamaki is an active member of several professional organizations: Mathematical Society of Japan Japanese Association of Financial Econometrics and Engineering Japan Statistical Society International Society for Mathematical Sciences He has led research projects including 'Generalized empirical likelihood for time series' (2013-2017) and 'Bayes approach to time series models' (2007-2009), with his research resulting in 11 publications with 28 citations and an h-index of 4 (Scopus data as of September 2025). Dr. Tamaki teaches graduate courses including Statistical Finance and Research Guidance on Statistical Finance, as well as undergraduate courses in mathematics for economics and advanced econometrics.

• Financial Mathematics
• Stochastic Processes
• Portfolio Optimization
• +۳ مورد دیگر

Dr. Hakam Kondakji is a Lecturer affiliated with the Business Mathematics Research group. His work focuses on financial mathematics and stochastic processes, particularly in the context of portfolio optimization and utility maximization under uncertain market conditions. Research Interests : Financial Mathematics, Stochastic Processes, Portfolio Optimization, and Utility Maximization in markets with Gaussian drift. Key Contributions : Presented innovative research on integrating expert opinions into financial models at major conferences like the German Probability and Statistics Days. Publications : Highlighted trends in high-frequency trading, partial information modeling, and risk management frameworks. Team Affiliation : Collaborates with Prof. Dr. Ralf Wunderlich and other researchers in the Business Mathematics division.

Anne-Laure Fougèresمشاهده پروفایل

استاد

• Statistics
• Probability
• Extreme Value Theory
• +۴ مورد دیگر

Anne-Laure Fougères is a Professor in the Department of Mathematics at Claude Bernard University Lyon 1, affiliated with the Camille Jordan Institute (CNRS UMR 5208) under the Probability, Statistics and Mathematical Physics team. Her research spans Statistics, Probability, and Mathematical Physics, with a focus on Extreme Value Theory and environmental/climate applications. Her work includes probabilistic forecasting calibration, spatial extreme risk modeling, multivariate dependence structures, and functional estimation under shape constraints. She has published extensively in top-tier journals like International Journal of Forecasting , Annals of Statistics , and Extremes , often with applications in hydrology and meteorology. Recent research trends show emphasis on Archimax copulas, ensemble forecast post-processing, and Bayesian model averaging for multivariate extremes. She leads the Probability, Statistics and Mathematical Physics team and serves on the French Statistical Society Council. Teaching responsibilities include the "Mathematics for the Environment and Climate" section of the "Maths in Action" master's program, courses at Polytech Lyon, and interdisciplinary Climate and Transitions instruction. She has supervised 9 PhD students, including Anne Sabourin and Alexis Huet, with research grants from Météo-France, EDF, and CIFRE.

Zak Mhammediمشاهده پروفایل

پژوهشگر ارشد

• Reinforcement Learning
• Control Theory
• Optimization
• +۵ مورد دیگر

Zak Mhammedi is a Postdoctoral Associate at the Massachusetts Institute of Technology working with Sasha Rakhlin on theoretical problems in machine learning. His research spans Reinforcement Learning, Control Theory, and Optimization with strong theoretical foundations. His educational background includes: PhD in Computer Science, 2021, Australian National University MPhil in Computer Science, 2016-2017, The University of Melbourne Mhammedi's research focuses on bridging theoretical guarantees with practical algorithms, particularly in online learning, projection-free optimization, and reinforcement learning. His work often addresses fundamental limitations in statistical learning while developing computationally efficient methods. He has made significant contributions to understanding risk-monotonicity, developing parameter-free online learning algorithms, and creating efficient exploration strategies for reinforcement learning. His publication record shows a consistent trajectory in theoretical machine learning, with numerous papers in top-tier venues including NeurIPS, ICML, and COLT. His work demonstrates expertise across multiple subfields including PAC-Bayesian theory, online convex optimization, and control theory applications to machine learning. His scientific achievements include: Oral Presentation at NeurIPS 2021 (awarded to approximately 1% of submissions) Spotlight Presentation at NeurIPS 2020 Spotlight Presentation at NeurIPS 2018 (awarded to approximately 3% of submissions) Mhammedi actively contributes to the academic community through program committee service for COLT 2021 and 2022, and as a reviewer for major conferences including COLT, NeurIPS, and ICML across multiple years. His technical expertise spans adaptive methods, computational efficiency, constrained optimization, and theoretical aspects of deep learning and reinforcement learning.