جست‌وجوی استاد و پژوهشگر

Badr-Eddine Chérief-Abdellatifمشاهده پروفایل

پژوهشگر

• Mathematical Statistics
• Machine Learning Theory
• Generalized Bayesian Inference
• +۶ مورد دیگر

Badr-Eddine Chérief-Abdellatif is a CNRS Researcher at Sorbonne Université , affiliated with the Laboratoire de Probabilités, Statistique et Modélisation (LPSM) . His research spans mathematical statistics and machine learning theory , focusing on generalized Bayesian inference , robustness , and variational inference . He previously held a postdoctoral position at the University of Oxford and earned his PhD in 2020 from Institut Polytechnique de Paris under Pierre Alquier. His work addresses robust statistical methods using Maximum Mean Discrepancy (MMD) , PAC-Bayesian theory , and kernel techniques . Recent publications explore meta-learning , VAE reconstruction guarantees , and missing data analysis . He received the Best Student Paper Award at AISTATS 2022 and a Best Paper Award at ACML 2019. Chérief-Abdellatif contributes to online learning , copula estimation , and sparse deep learning . His theoretical results on variational inference have been published in top venues like NeurIPS , JASA , and JMLR . He also co-invented a patent for proportions determination in biological ensembles (2024). Scientific Awards : Best Student Paper Award, AISTATS 2022 Best Paper Award, ACML 2019 Travel Award for Academic Conferences

Kirsten Schorningمشاهده پروفایل

استادیار

• Design of experiments
• Biostatistics
• Model selection
• +۳ مورد دیگر

Kirsten Schorning is a Junior Professor of Mathematical Statistics at the Department of Statistics, Faculty of Statistics, Technical University of Dortmund. She maintains her office in the Mathematics Building, Room 725, and leads a research group focused on statistical methodology development with applications in biostatistics and toxicology. Education: Bachelor's and Master's degree in Mathematics, Ruhr-University Bochum (2007-2012) Doctorate in Mathematics (Dr. rer. nat.), Ruhr-University Bochum (2012-2016) Dr. Schorning's research primarily centers on Design of Experiments , Biostatistics , and Model Selection and Averaging . Her work develops innovative statistical methodologies for optimal experimental design, particularly in drug combination studies, toxicological assessments, and dose-response modeling. She has made significant contributions to addressing day-to-day variability in toxicity assessments and developing optimal concentration designs for in vitro experiments. Analysis of her recent publications (2019-2025) reveals a strong focus on optimal experimental design methodologies with applications in pharmaceutical research and toxicology. Her work bridges theoretical statistics with practical applications, particularly in determining effective drug doses, analyzing mixture toxicity, and developing robust statistical approaches for concentration-response relationships. A notable trend is her increasing focus on addressing real-world complexities such as day-to-day variability in toxicity assessments and developing guidance frameworks for toxicological dose-response experiments. Dr. Schorning leads a research group at TU Dortmund that includes team members Sabine Bell (secretary), Pauline Baur, Julia Eichhorn, Justin Lamberti, and Onur Gül. Her research has significant implications for pharmaceutical development, environmental toxicology, and clinical trial design, providing statisticians and researchers with improved methodological tools for experimental planning and analysis.

• Monetary Economics
• Macroeconomics
• Econometrics
• +۱۱ مورد دیگر

Francisco J. Ruge-Murcia is Professor of Economics at McGill University, Montréal, where he also served as Department Chair from 2019 to 2024. He is an elected President of the Canadian Economics Association (2021-2022) and a Senior Houblon-Norman Fellow at the Bank of England (Fall 2025). His research blends macroeconomics, monetary economics, and econometrics to study central-bank behaviour, inflation dynamics, and collective decision-making in policy committees. Education Ph.D. in Economics, University of Virginia, USA M.A. in Economics, University of Virginia, USA B.Sc. in Industrial Engineering, Universidad Industrial de Santander, Colombia Research Focus Professor Ruge-Murcia’s work centres on monetary economics , macro-econometrics , and collective decision-making in monetary policy committees . He develops and estimates nonlinear DSGE models to analyse how central banks set policy, how shocks propagate through production networks, and how price rigidity and skewness risk affect aggregate dynamics. A unifying theme is the interplay between committee governance (voting records, deliberation, chairman influence) and macro-financial outcomes (inflation, bond prices, exchange rates). Recent projects use high-frequency data—including Twitter sentiment—to measure the impact of trade-policy uncertainty on exchange rates. Scientific Awards & Fellowships President, Canadian Economics Association, 2021–2022 Bank of Canada Fellowship Award, 2017–2021 Postdoctoral Research Fellowship, Bank of Spain, 1999–2000 Top-5 % author ranking in RePEc across multiple metrics Grants & Institutional Support His research has been supported by the Bank of Canada Fellowship, the Bank of Spain, and recurring grants from CIREQ. Ongoing collaborations with the Federal Reserve Banks of Richmond and Minneapolis, the IMF, and the Bank of England underpin his international network. Labs & Research Groups He is affiliated with the Centre interuniversitaire de recherche en économie quantitative (CIREQ) and maintains active ties to policy institutions such as the Bank of Canada, the Bank of England, and the Society for Economic Dynamics.

Hanti Linمشاهده پروفایل

دانشیار

• Philosophy of Science
• Formal Epistemology
• Inductive Logic
• +۴ مورد دیگر

Hanti Lin serves as Associate Professor in the Department of Philosophy at the University of California, Davis, where his research bridges formal epistemology, philosophy of science, and computational methodologies. His work addresses foundational challenges in scientific inference, particularly focusing on justifying inductive reasoning beyond traditional statistical frameworks. Education: Ph.D. in Philosophy, Carnegie Mellon University (2013) M.S. in Logic, Computation and Methodology, Carnegie Mellon University (2010) M.A. in Philosophy, University College London (2007) B.A. in Physics, National Taiwan University (2003) Research Focus: Lin's scholarship centers on resolving Hume's problem of induction through novel convergence frameworks, with significant contributions to causal inference without faithfulness assumptions and unified theories of inductive logic . His interdisciplinary approach integrates statistics , machine learning theory , and formal epistemology to justify scientific inference methods that resist conventional statistical justification. Recent work demonstrates increasing sophistication in connecting theoretical computer science with philosophical epistemology. Publication Trends: Analysis of Lin's 15 most recent publications reveals a clear trajectory toward synthesizing machine learning practice with philosophical foundations. The period 2022-2025 shows heightened focus on internalist reliabilism in statistical practice, historical-philosophical analysis of machine learning , and unified convergence frameworks spanning formal learning theory, statistical inference, and supervised learning. This evolution reflects growing recognition of philosophy's role in addressing fundamental limitations in contemporary AI methodologies. Awards: No scientific awards were documented in the provided materials. Teaching & Mentoring: Lin teaches advanced courses including "Theory of Knowledge," "Formal Epistemology," and "Logic, Probability, and Artificial Intelligence," emphasizing rational justification in scientific inquiry. While specific advisees aren't listed, his curriculum design demonstrates commitment to training next-generation researchers in interdisciplinary methodology. His postdoctoral position at Australian National University preceded his current UC Davis appointment, establishing his research trajectory.

Jonathan Joseph Chipmanمشاهده پروفایل

استادیار

• Causal Inference
• Randomization Methods
• Clinical Trials Design
• +۳ مورد دیگر

Jonathan Joseph Chipman is an Assistant Professor of Biostatistics at the University of Utah , affiliated with the Department of Population Health Sciences and the Cancer Biostatistics Shared Resource at the Huntsman Cancer Institute (HCI). He also holds an adjunct appointment in the Department of Family & Preventive Medicine . Education: BS in Statistical Science from Brigham Young University, MS in Biostatistics from the University of Minnesota, and PhD in Biostatistics from Vanderbilt University under Dr. Robert Greevy. Research Interests: Chipman specializes in causal inference , randomization methods , and adaptive monitoring schemes for clinical trials. His work emphasizes reducing modeling assumptions, improving trial efficiency through covariate-adjusted randomization , and integrating clinical context into statistical estimation. Methodological Contributions: He has developed sequential matched randomization frameworks and second-generation p-value monitoring techniques. His collaborations span cancer survivorship, prostate cancer screening, and skin cancer prevention studies. Teaching: Courses include Design and Implementation of Clinical Trials (MDCRC 6040), Statistical Methods for Clinical Trials (PHS 7070), and Advanced Statistical Computing in R (PHS 7045). Formerly taught Paradigms of Statistical Inference (PHS 7060). Advisees: Currently mentoring PhD student Yeonjung Jo on prostate cancer prognostication projects. Contact: Email: jonathan.chipman@hci.utah.edu Laboratory: Chipman Lab

Jasper Vrugtمشاهده پروفایل

استاد

• Hydrology
• Computational Science
• Geophysics
• +۴ مورد دیگر

Jasper Vrugt is a Professor at the University of California, Irvine, with joint appointments in Civil and Environmental Engineering and Earth System Science. He holds a Ph.D. (2004) and M.S. (1999), both earned cum laude from the University of Amsterdam. His research focuses on complex Earth system dynamics through integrated numerical modeling, measurement techniques, and inverse methodologies. Research interests span computational science, hydrology, geophysics, and uncertainty quantification. Key themes include diagnosing epistemic errors in models, quantifying uncertainty in environmental predictions, and developing next-generation inference methods. Applications range from soil moisture-precipitation feedbacks to carbon-nitrogen cycling and hydropower optimization. His recent publications demonstrate strong emphasis on Bayesian uncertainty quantification, diagnostic model evaluation, and climate-hydrology interactions. Trends include advanced statistical methods for nonstationary extremes, soil moisture-cloud coupling, and novel sensitivity analysis techniques. Dr. Vrugt leads an active research group advising multiple graduate students. His team develops computational tools for environmental modeling and maintains collaborations with institutions like Los Alamos National Laboratory.

Gang Hanمشاهده پروفایل

استاد

• Biostatistics
• Epidemiology
• Personalized Medicine
• +۷ مورد دیگر

Gang Han is a Professor in the Department of Epidemiology & Biostatistics at Texas A&M University, where he develops and applies advanced statistical methodologies to address critical challenges in personalized medicine, clinical trials, and public health research. His work bridges theoretical biostatistics with practical applications in cancer outcomes, diabetes management, and dementia caregiving, significantly contributing to evidence-based healthcare interventions. Education PhD in Statistics, Ohio State University (2008) MS in Statistics, Ohio State University (2005) BS in Computer Science, Beijing University of Technology (2003) Dr. Han's research program focuses on cutting-edge statistical techniques including survival analysis for cancer studies, Bayesian methods for small sample sizes, and innovative trial designs for translational research. His work on HIV viral load modeling and personalized medicine frameworks demonstrates rigorous methodological development applied to complex health problems. Recent publications reveal increasing emphasis on health disparities affecting rural populations, racial minorities, and aging adults, particularly in diabetes care and dementia support systems. Analysis of recent publications (2024-2025) shows dominant themes in chronic disease management (diabetes, cancer), gerontological research (dementia caregiving, fall prevention), and methodological innovation (latent profile analysis, Bayesian-frequentist hybrid models). His work consistently addresses health equity through urban-rural comparisons and racial/ethnic stratification, with strong emphasis on practical implementation in Texas-based healthcare systems and community interventions. Scientific Awards No awards documented in available sources Dr. Han's academic contributions include teaching statistical theory, clinical trials methodology, and personalized medicine applications. While specific mentorship details are unavailable, his extensive publication record indicates active supervision of research projects and collaboration with clinical investigators. Current work focuses on digital health interventions for dementia caregivers and predictive modeling for diabetes cost management, supported through multi-institutional partnerships evident in his research portfolio. He leads development of innovative tools like the GAMEPLAN4CARE online platform and contributes to community-based programs addressing diabetes self-management and fall prevention, demonstrating commitment to translating statistical research into actionable public health solutions.

Junnan Heمشاهده پروفایل

استادیار

• Econometrics
• Decision Theory
• Model Sparsity
• +۳ مورد دیگر

Junnan He is an Assistant Professor in the Department of Economics at Sciences Po (temporarily on leave) and will be the Alliance Visiting Professor at Columbia University's Department of Economics starting Fall 2025. He holds a BSc in Mathematical Economics and Mathematics from the Australian National University (ANU), an MA in Economics from Washington University in St. Louis (WUSTL), and a PhD in Economics from WUSTL. His research focuses on econometrics, decision theory, model sparsity, stochastic choice models, and industrial organization. Key research interests include variable selection in economic models, random choice models, Bayesian methods, and the application of econometric techniques to understand decision-making processes. His work has been recognized through prestigious awards such as the WUSTL Dissertation Fellowship (2017), the Best PhD SPEED Presentation at the OxMetrics Conference (2018), and the ANR Young Researcher Grant (2024) for his project on Complex Decision Making. His publications span topics ranging from VC funding gender gaps to market concentration dynamics in renewables and theoretical advancements in utility models. He has contributed to both empirical and theoretical work in econometrics, bridging methodological innovation with applied economic questions. Professional activities include organizing the IO/Metrics Seminar and maintaining an active presence in academic networks. His research agenda emphasizes bridging gaps between econometric theory and real-world applications in decision-making and market structures.

Zoltan Dienesمشاهده پروفایل

استاد

• Bayesian Methods
• Statistical Inference
• Consciousness Studies
• +۵ مورد دیگر

Prof. Zoltan Dienes is a Professor in Experimental Psychology at the University of Sussex, Director of the Centre for Open Science and Research Reform. His work focuses on Bayesian statistical methods, unconscious mental processes, and reforming scientific practices. He advocates for Registered Reports and computational reproducibility. Education: BA (Natural Sciences, Cambridge), MA (Experimental Psychology, Macquarie), D.Phil (Experimental Psychology, Oxford). Research interests include Bayesian inference, phenomenological control (e.g., hypnotic suggestibility), and distinguishing conscious/unconscious knowledge. He critiques conventional statistical practices and promotes open science initiatives. Key grants include studies on mindfulness, implicit cognition, and hypnotisability. Editor roles in journals like Psychology of Consciousness and Royal Society Open Science . Teaches courses on conscious/unconscious processes and research reform. Active in promoting reproducible research and registered reports.

• Cognitive Psychology
• Visual Perception
• Neuroscience
• +۳ مورد دیگر

Dr. Sven Panis is a Researcher affiliated with the Professorship for Neural Control of Movement at ETH Zurich's Department of Health Sciences and Technology (D-HEST). His work focuses on cognitive processes, visual perception, and response time dynamics, employing advanced statistical methods like event history analysis. He investigates topics such as global/local processing, inhibition of return, and attentional mechanisms in spatial and temporal contexts. Key research interests include understanding how perceptual and cognitive processes interact over time, with applications to neuropsychological disorders like autism spectrum disorder (ASD). His methodological contributions span Bayesian and frequentist frameworks for analyzing time-to-event data in behavioral experiments. Panis has published extensively on visual search, motor activation dynamics, and object recognition, emphasizing temporal resolution in cognitive tasks. His findings challenge traditional reliance on mean response times, advocating for nuanced analyses of behavioral and neural processes. No scientific awards are explicitly mentioned in the provided texts. His advising and grant activities are not detailed here, but his affiliation suggests involvement in collaborative academic projects within D-HEST's interdisciplinary environment.

• Statistical Machine Learning
• High-Dimensional Statistics
• Nonparametric Methods
• +۷ مورد دیگر

Ann Lee is a Professor and Co-Director of the Ph.D. Program in the Department of Statistics & Data Science at Carnegie Mellon University, with a joint appointment in the Machine Learning Department. She has been at CMU since 2005, after serving as the J.W. Gibbs Assistant Professor of Applied Mathematics at Yale University. Her academic journey began with a PhD in Physics from Brown University in 2002, preceded by BSc and MSc degrees in Engineering Physics from Chalmers University of Technology in Sweden. Dr. Lee's educational background includes: PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on developing statistical methodology for complex data and problems in the physical sciences, with particular emphasis on trust-worthy scientific inference and reliable uncertainty quantification. She works at the intersection of classical statistics and machine learning, developing methods for simulation-based inference and experimental design. Dr. Lee is especially interested in likelihood-free inference, calibrated probabilistic forecasting, and interpretable diagnostics of generative models, with applications in astronomy and hurricane intensity guidance involving satellite imagery and large surveys. Dr. Lee co-founded the STAMPS (STAtistical Methods for the Physical Sciences) research group with Mikael Kuusela in 2018, which is transitioning to a CMU Research Center in Fall 2024. The group hosts public colloquia-style webinars and weekly research meetings for students and faculty at CMU and UPitt. Her notable scientific contributions have been recognized through: Her paper "Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery" being selected for The Best of AOAS session at the 2023 Joint Statistical Meeting Her student Luca Masserano winning the ASA Best Student Paper Award at the 2023 Joint Statistical Meeting Her student Alex Shen winning a Poster Award at the Machine Learning and the Physical Sciences Workshop, NeurIPS 2023 Dr. Lee has advised numerous PhD students to completion, with research spanning statistical machine learning, high-dimensional statistics, and applications in physical sciences. She has secured research funding supporting her work on statistical methods for physical sciences and has collaborated extensively with researchers across disciplines. Her current research projects focus on advancing likelihood-free inference methods and applying them to challenging problems in climate science and astronomy. She leads the STAMPS research group, which is becoming a CMU Research Center in Fall 2024, and organizes workshops including the PHYSTAT-SBI workshop on "Simulation Based Inference in Fundamental Physics" and the Hammers & Nails workshop on "Frontiers in Machine Learning in Cosmology, Astro & Particle Physics."

Luca Maestriniمشاهده پروفایل

مدرس

• Methodological and Computational Statistics
• Statistical Theory
• Variational Approximations
• +۴ مورد دیگر

Dr. Luca Maestrini is a Lecturer of Statistics at the Research School of Finance, Actuarial Studies & Statistics at the Australian National University (ANU), Canberra. His research focuses on methodological and computational statistics, with expertise in variational approximations, generalized linear mixed models (GLMMs), and statistical theory. He previously held postdoctoral positions at ANU (2022–2024) under Prof. Francis Hui and Prof. Alan Welsh, and at the University of Technology Sydney (2018–2022) under Prof. Matt Wand. He completed his PhD in Statistics at the University of Padova, Italy (2015–2019), studying under Prof. Nicola Sartori and co-supervisors Prof. Alessandra Salvan and Prof. Matt Wand. He also served as a Research Fellow at the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research interests span computational statistics, Bayesian inference, and applications in forensic science, ecology, and finance. Notable collaborations include work on inverse problems, multivariate abundance data analysis, and stochastic variational inference for GARCH models. He currently chairs the Australian Capital Territory branch of the Statistical Society of Australia. Key research trends in his articles include advancements in variational inference frameworks, asymptotic improvements to GLMMs, and statistical methods for postmortem interval estimation using lipid degradation biomarkers. His work bridges theoretical developments with practical applications in diverse fields. He has advised students through ANU’s research programs and contributed to ACEMS initiatives. His lab focuses on computational methods and applied statistical modeling, emphasizing interdisciplinary collaboration.

Demetrius Kantarelisمشاهده پروفایل

استاد

• Economics of the Firm
• Law and Economics
• Statistical Analysis
• +۲ مورد دیگر

Demetrius Kantarelis, Ph.D., is a Professor of Economics at Assumption University with doctoral degrees from Clark University and the National and Kapodistrian University of Athens. His research spans economics of the firm, law and economics, and statistical analysis. Dr. Kantarelis has authored numerous publications including 'Law and Economics – Property, Contracts, Torts' (2024) and 'Theories of the Firm' (2020). He serves as editor for multiple academic journals including the International Journal of Economics and Business Research. Honors include the Paul Ziegler Presidential Award for Excellence in Scholarship (2009) and Emerald Literati awards. His global academic engagement includes keynote addresses at international conferences in Croatia, Japan, and Bulgaria, along with visiting scholarships at Harvard University.

Steve Chungمشاهده پروفایل

دانشیار

• Time Series Analysis
• Semi-parametric/Nonparametric Estimation
• Volatility Models
• +۶ مورد دیگر

Steve Chung is an Associate Professor in the Department of Mathematics at California State University, Fresno (Fresno State). He holds a B.S. in Applied Mathematics from Cal Poly Pomona, an M.S. in Statistics from the University of Florida, and a Ph.D. in Statistics from Florida State University. His research focuses on Time Series Analysis , Semi-parametric/Nonparametric Estimation , Volatility Models , and Data Science . Recent work spans environmental science, medical imaging, financial modeling, and vascular surgery techniques. He teaches a range of mathematics courses including Math 6, Math 11, and advanced statistics electives. Key contributions include studies on spatio-temporal air quality modeling, comparative ultrasound techniques in vascular disease diagnosis, and volatility analysis in foreign exchange markets. No scientific awards are listed, though his interdisciplinary research bridges statistics with applied fields.

Oliver James Clarkمشاهده پروفایل

مدرس

• Computational Neuroscience
• Neurodegenerative Diseases Modeling
• Bayesian and Frequentist Statistics
• +۳ مورد دیگر

Dr. Oliver James Clark is a Lecturer in the School of Psychology at Manchester Metropolitan University (MMU), part of the Faculty of Health and Education. His research focuses on computational neuroscience, particularly modeling brain structures and neurodegenerative diseases like Alzheimer’s and Parkinson’s. He holds a PhD in Psychology (2019) and specializes in statistical methods (Bayesian/frequentist) and neuroimaging tools (FSL, Freesurfer). Teaching responsibilities include quantitative methods, biopsychology, and research supervision for MSc Forensic Psychology students. Recent projects involve analyzing diffusion tensor imaging (DTI), object categorization neuroscience, and glymphatic system dynamics in early Alzheimer’s. Publications emphasize interdisciplinary approaches linking computational methods with neuroimaging, including a notable 2023 Cerebral Cortex paper challenging language network roles in categorization. No specific awards are listed, though ongoing research explores sleep’s role in neurodegenerative biomarkers.