جست‌وجوی استاد و پژوهشگر

Rebecca Marionمشاهده پروفایل

پژوهشگر

• Dimensionality Reduction
• Feature Selection
• Nonlinear Embedding
• +۳ مورد دیگر

Rebecca Marion is a Researcher at UCLouvain specializing in machine learning, with expertise in dimensionality reduction and feature selection. She holds a Doctor of Science from UCLouvain (awarded 2021) for her work on statistical and machine learning methods for identifying variable clusters. Her education: Doctor of Science, Statistical and Machine Learning Methods for Identifying Clusters of Variables, UCLouvain (2021) Marion's research focuses on nonlinear dimensionality reduction (e.g., t-SNE, UMAP) and feature selection stability. She develops gradient-based explanation methods for complex embeddings and investigates the robustness of machine learning models. Her work bridges theoretical advances with practical data science applications. Recent publications highlight her contributions to explainable AI: a 2024 paper on gradient-based explanations for nonlinear embeddings and a 2023 study improving feature selection stability in regression. These works address critical challenges in interpretability and reliability of machine learning systems. No scientific awards are listed in the available profile information. Information about student advising and research grants is not provided in the current data. She is part of UCLouvain's machine learning research community, though specific lab or team affiliations are not detailed.

John Pepperمشاهده پروفایل

استاد

• Public Economics
• Econometrics
• Social Program Evaluation
• +۷ مورد دیگر

John Pepper is the Merrill S. Bankard Professor of Economics and currently serves as Interim Chair in the Department of Economics at the University of Virginia, College of Arts & Sciences. His research focuses on public economics, econometrics, and the evaluation of social programs using advanced statistical methods to address data limitations. Research Interests: His work centers on partial identification, misreporting in survey data, and causal inference in policy evaluation. Key areas include vocational rehabilitation, food security (SNAP, WIC), mental and physical disability, gun laws, and intergenerational welfare transmission. He develops and applies econometric techniques such as bounded-variation assumptions and monotone instrumental variables to draw robust conclusions under uncertainty. Publication Trends: Over the past two decades, his research has consistently addressed public policy questions using rigorous econometric frameworks. Recent publications (2020–2024) emphasize mental health, food security, gun policy, and disability, often employing partial identification to handle data ambiguity. His collaborations with leading economists like Charles Manski and Brent Kreider reflect his influence in methodological and applied economics. Best Economics Paper Award, Food Safety and Nutrition section of the Agricultural & Applied Economics Association, Honorable Mention, 2024 Advising and Grants: While specific students are not listed, he mentors through collaborative research and large-scale evaluations. He has contributed to major national reports for the National Research Council on topics including the death penalty, firearms violence, and illegal drugs. His work on vocational rehabilitation includes a forthcoming 2025 Springer book on return-on-investment models, indicating sustained funding and institutional support. Labs and Research Teams: He is part of collaborative research teams focused on public policy evaluation, often working with multidisciplinary groups from institutions such as the National Research Council. His projects involve complex data analysis from sources like the Panel Study of Income Dynamics (PSID) and RSA-911, and he contributes to working paper series and policy reports that inform government and academic discourse.

Trang Nguyenمشاهده پروفایل

عضو هیئت علمی

• Causal Inference
• Mediation Analysis
• Treatment Effect Heterogeneity
• +۱۲ مورد دیگر

Trang Nguyen is an Associate Research Professor in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health, with a joint affiliation in Biostatistics. She is a member of the Stuart Lab and focuses on advancing causal inference methods for public health research, particularly in mental health, HIV/AIDS, and social justice. Her educational background includes a PhD and MHS from Johns Hopkins Bloomberg School of Public Health (2021, 2014), an MS from Harvard School of Public Health (2001), and a BA from Ha Noi Foreign Trade University (1995). Her training was guided by prominent advisors including Karen Bandeen-Roche, Liz Stuart, Betsy Ogburn, Constantine Frangakis, Amy Knowlton, and Renee Johnson. Nguyen's research centers on causal inference, with key interests in mediation analysis, treatment effect heterogeneity, missing data, measurement error, sensitivity analysis, and generalizability. She develops and applies rigorous statistical methods to address data limitations and complex causal questions in observational studies. Her recent publications (2019–2025) show a strong trend in applying machine learning and advanced statistical models—such as causal forests and principal stratification—to real-world public health problems, especially in HIV care, substance use treatment, and health disparities. The articles reflect interdisciplinary collaboration and methodological innovation. Application of Causal Forest Model to Examine Treatment Effect Heterogeneity in Substance Use Disorder Psychosocial Treatments Practical challenges in mediation analysis: a guide for applied researchers Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects Depression and associated factors among HIV-positive smokers receiving care at HIV outpatient clinics in Vietnam Estimation of place-based vulnerability scores for HIV viral non-suppression She has contributed to open science through R packages such as mediationClarity , PIsens , and latentMAR , which implement estimators for mediation, sensitivity analysis, and latent missingness models. These tools support transparent and reproducible causal inference. Nguyen was originally from Vietnam, where she worked in public health and community development before pursuing advanced training in the U.S. She continues to apply her expertise to global health challenges, particularly in low-resource settings.

• Changepoint Detection
• High-dimensional Time Series
• Statistical Inference
• +۳ مورد دیگر

Haeran Cho is Professor of Statistical Science in the School of Mathematics at the University of Bristol, holding a BSc and PhD in Statistics. Her research focuses on developing foundational methodologies for detecting structural changes in complex high-dimensional data streams, with applications spanning finance, environmental monitoring, and biomedical engineering. Her primary research interests include changepoint detection in non-sparse regression frameworks, factor model diagnostics for tensor time series, and robust nonparametric segmentation techniques. She pioneers approaches that handle heavy-tailed distributions, temporal dependence, and high-dimensional scaling—addressing critical limitations in classical change point theory through adaptive covariance scanning and multiscale inference frameworks. Professor Cho received the Research Prize in 2013 for her contributions to statistical theory. She currently leads the £1.2M EPSRC-funded project Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS, 2024-2029), developing real-time anomaly detection systems for industrial applications. Previous projects include quantile factor modeling for high-dimensional time series (2019). Her software implementations ( CptNonPar , mosum , fnets ) have become standard tools in statistical computing, with over 500 citations. She actively collaborates through Horizon Europe initiatives and supervises postgraduate researchers in statistical methodology development.

• Econophysics
• Complex Networks
• Statistical Physics
• +۷ مورد دیگر

Prof. Rosario Nunzio Mantegna is a Full Professor in the Department of Physics and Chemistry - Emilio Segrè at the University of Palermo (Unipa), Italy. He has held office hours in Building 18, Viale delle Scienze, focusing on appointments via email at rosario.mantegna@unipa.it. Research Interests: Econophysics, Complex Networks, Financial Market Dynamics, Air Traffic Systems, and Statistical Physics Applications. Methodological Expertise: Network Validation, Correlation Filtering, Hierarchical Clustering, and Stochastic Modeling. His work bridges physics, finance, and data science through network-based approaches to complex systems. Key contributions include analyzing financial indices, market lead-lag relationships, and air traffic networks. Publications span interdisciplinary topics from autism spectrum disorders to volcanic impact on ATM systems.

Mehrdad Naderiمشاهده پروفایل

مدرس

• Classification
• Cluster Analysis
• Finite Mixture Models
• +۵ مورد دیگر

Mehrdad Naderi is a Lecturer in Statistics at the Department of Mathematics, Physics, and Electrical Engineering , Northumbria University . His academic journey includes a PhD in Mathematical Statistics from Shahid Bahonar University of Kerman (2017) and postdoctoral research at National Chung Hsing University (Taiwan), Ferdowsi University of Mashhad (Iran), and University of Pretoria (South Africa). Education: PhD in Mathematical Statistics, Shahid Bahonar University of Kerman (2017) His research focuses on applied statistical inference with emphasis on classification , cluster analysis , factor analysis , finite mixture models , and EM algorithm for robust estimation. He has contributed to multivariate and matrix-variate analysis, particularly in handling outliers and asymmetrical data structures. Recent work includes three-way data clustering using matrix-variate normal distributions and robust Bayesian inference for censored mixture models. His publications demonstrate expertise in distribution theory, statistical computation, and applications to financial data, environmental modeling, and astrophysics. Current collaborations span multiple institutions, focusing on heavy-tailed distributions and computational methods for complex data structures.

Ramya Korlakai Vinayakمشاهده پروفایل

استادیار

• Machine Learning
• Statistical Inference
• Crowdsourcing
• +۳ مورد دیگر

Ramya Korlakai Vinayak is the Dugald C. Jackson Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison's College of Engineering. Her research bridges theoretical machine learning with human-centered AI applications. Education: Postdoctoral Researcher, Paul G. Allen School of Computer Science and Engineering, University of Washington Ph.D. and M.S. in Electrical Engineering, California Institute of Technology (Caltech) B.Tech. in Electrical Engineering, Indian Institute of Technology Madras (IIT M) Research Focus: Her work centers on machine learning foundations with emphasis on statistical inference, crowdsourcing, and human-AI collaboration. Key themes include modeling heterogeneous human preferences, developing robust out-of-distribution detection systems, and creating culturally-aware generative models. She pioneers methods for pluralistic alignment where AI systems accommodate diverse human values through ideal point modeling and adaptive feedback mechanisms. Publication Trends: Recent publications (2023-2025) reveal three converging trajectories: (1) Human-in-the-loop frameworks for reducing false positives in safety-critical systems, (2) Metric learning innovations using limited preference data, and (3) Critical examinations of cultural limitations in generative AI. Her work consistently addresses real-world challenges like cognitive overload in crowdsourcing and biases in face generation systems. Research Infrastructure: She leads an active research group with a dedicated lab website (https://ramyakv.github.io/) focusing on developing theoretically-grounded yet practically-deployable AI systems that respect human plurality.

• Statistical Modeling
• Machine Learning
• Regression Analysis
• +۳ مورد دیگر

Wang You-Gan is an accomplished academic researcher with over three decades of publication history spanning from 1991 to 2024. His work demonstrates a strong foundation in statistical methodology with applications across multiple domains including environmental science, fisheries management, machine learning, and computational statistics. His research has been published in high-impact journals across statistics, computer science, environmental science, and biology. Wang You-Gan's primary research interests include statistical modeling, regression analysis (particularly support vector regression), longitudinal data analysis, machine learning applications, and environmental statistics. His work shows a clear evolution from early fisheries and environmental applications to broader computational statistical methods with applications in energy systems, genomics, and cloud computing. His research demonstrates strong methodological development coupled with practical applications. His recent publications (2022-2024) show a strong focus on advanced regression techniques, particularly support vector regression with innovations in handling heterogeneous variances, autoregressive processes, and automatic hyperparameter selection. He has also expanded into machine learning applications for energy demand forecasting, air quality prediction, and data center optimization. His work often bridges theoretical statistical development with practical implementation across diverse domains. Wang You-Gan has received significant citation impact for his methodological contributions, with several papers accumulating over 100 citations. His collaborations span multiple institutions and disciplines, indicating his work's broad relevance across fields. His research has important applications in environmental monitoring, energy systems management, computational biology, and cloud computing infrastructure. The consistent publication record over more than 30 years demonstrates his sustained contribution to statistical methodology and its applications.