Cluster Robust Inference Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۰۶-۷۲۰ از ۷۴۱ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

John Pepperمشاهده پروفایل

استاد

University of VirginiaUnited States

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.

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Andreas Kurzمشاهده پروفایل

پژوهشگر

Paris Lodron University of SalzburgAustria

Andreas Kurz is a Senior Scientist at the Department of Psychology, University of Salzburg, where he conducts research in psychological diagnostics and psychometric modeling. His work focuses on statistical inference in small sample contexts and conditional likelihood methods, particularly in educational assessment and admission procedures. Position: Senior Scientist Institution: University of Salzburg Department: Department of Psychology Research Group: Admission Procedure for Teaching Professions in the Central Cluster Andreas Kurz holds a Master of Science in Psychology from UMIT TIROL (2021) and a Diploma in Civil Engineering (equivalent to MSc ETH) from ETH Zurich (1995). His academic journey includes roles as a Student Assistant at UMIT TIROL (2019–2022) and a Scientific Assistant at ETH Zurich (2007–2009). His research lies at the intersection of psychology and statistics, with a strong emphasis on methodological rigor in psychometric testing. He develops and applies advanced statistical techniques for small sample inference, particularly using resampling and conditional likelihood frameworks. His work supports valid and reliable assessment in educational and psychological settings. The publications and software tools developed by Andreas Kurz reflect a consistent trend in advancing statistical methodology for psychometrics. His contributions include peer-reviewed articles on the gradient test and conditional inference, as well as R packages like tcl and tclboot that implement these methods for practical use in research and assessment. Andreas Kurz has actively participated in international conferences such as IMPS 2021 and IMPS 2022, presenting on statistical testing in conditional likelihood frameworks. His work is supported by research in psychometrics and statistical computing, with no explicit mention of external grants or advising roles. He is involved in the development of admission procedures for teaching professions, indicating applied research with societal impact in education. His technical expertise bridges civil engineering, psychology, and statistical programming, contributing to robust assessment systems.

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John Platigمشاهده پروفایل

استادیار

University of VirginiaUnited States

John Platig is an Assistant Professor at the University of Virginia in the departments of Genome Sciences and Public Health Sciences. His research focuses on understanding how gene regulatory networks change in response to stimuli and disease states, combining computational methods with genomics and systems genetics. He holds a PhD in Physics from the University of Maryland and a BS in Physics from Wright State University, followed by postdoctoral training in computational biology at Harvard Chan School of Public Health. His research interests include gene regulatory networks, systems genetics, multi-omic data integration, and network science. Specific questions explored include the impact of non-coding genetic variation on transcriptional networks and modeling post-transcriptional regulatory mechanisms in disease. Platig's work emphasizes method development for network inference, such as the Network Zoo computational package, and addresses translational questions in COPD, cancer, and Mycobacterium tuberculosis. His publications consistently explore network-based approaches to uncover regulatory mechanisms underlying complex traits and diseases. Key contributions include analysis of eQTL networks, sex differences in gene expression across tissues, and systems-level modeling of polygenic adaptation. His lab's computational framework integrates multi-omics datasets to generate experimentally testable hypotheses about regulatory logic in health and disease.

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Tselil Schrammمشاهده پروفایل

استادیار

University of California, San DiegoUnited States

Tselil Schramm is an Assistant Professor in the Department of Statistics at Stanford University, with courtesy appointments in Computer Science and Mathematics. She is actively engaged in research and teaching in theoretical computer science and statistics. Department: Department of Statistics School: School of Humanities and Sciences University: Stanford University Office: CoDa E254 Email: tselil@stanford.edu She earned her PhD from UC Berkeley under Prasad Raghavendra and Satish Rao, followed by postdoctoral work at Harvard and MIT with Boaz Barak, Jon Kelner, Ankur Moitra, and Pablo Parrilo. Her research lies at the intersection of theoretical computer science and statistics, focusing on high-dimensional estimation, information-computation tradeoffs, sum-of-squares algorithms, and random graph theory. She develops algorithms for statistical problems and investigates the boundaries between what is statistically possible and what is computationally feasible. Her recent publications span topics including the overlap-gap property, discrepancy algorithms, robust message passing, semidefinite programming, spectral clustering, and random geometric graphs, appearing in top venues such as STOC, FOCS, COLT, NeurIPS, and The Annals of Statistics. She teaches a range of courses, including Introduction to Statistics (STATS 60), Theory of Statistics II (STATS 300B), and Machine Learning Theory (STATS 214 / CS 228M), reflecting her expertise in both foundational and advanced statistical theory. Runner-up for Best Paper at COLT 2021 Invited to STOC 2022 special issue of SICOMP Invited to SODA 2016 special issue of ACM Transactions on Algorithms Invited to CCC 2019 special issue of Theory of Computing Tselil Schramm advises and collaborates with numerous students and researchers, including Shuangping Li, Misha Ivkov, and Siqi Liu. She has been involved in multiple research grants and projects, particularly in the areas of high-dimensional inference and algorithmic robustness. Her work often bridges theoretical guarantees with practical algorithmic design. She is affiliated with Stanford’s theoretical computer science and statistics research groups, contributing to a vibrant academic environment. Her future work is expected to further explore the limits of efficient computation in statistical settings, with potential applications in machine learning, signal processing, and network analysis.

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Trang Nguyenمشاهده پروفایل

عضو هیئت علمی

Johns Hopkins UniversityUnited States

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.

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Shuchin Aeronمشاهده پروفایل

دانشیار

Tufts UniversityUnited States

Shuchin Aeron is an Associate Professor in the Department of Electrical and Computer Engineering at Tufts School of Engineering, with joint appointments in the Departments of Computer Science and Mathematics. He holds a Ph.D. from Boston University (2009) and completed postdoctoral research at Schlumberger Doll Research, focusing on borehole acoustic signal processing. His research spans statistical signal processing, machine learning, compressed sensing, and information theory, with applications in geophysics, bioengineering, and imaging. Aeron has authored over 175 publications and holds patents in acoustic signal processing. He received the NSF CAREER Award (2016) and is a Senior Member of the IEEE. Educations: Ph.D., Electrical Engineering, Boston University, 2009 M.S., Electrical Engineering, Boston University, 2004 B.Tech., Indian Institute of Technology, 2002 Research Interests: Statistical signal processing (SSP), inverse problems, compressed sensing, information theory, convex optimization Machine learning applications in geophysical signal processing, imaging, and bioengineering His work emphasizes optimal sampling and recovery of multidimensional signals, with contributions to compressed sensing architectures and generative models for particle physics experiments. He leads NSF-funded projects on data science and domain generalization, and collaborates with industry partners like Schlumberger and Mitsubishi Electric Research Labs. Awards: NSF CAREER Award (2016) Mitsubishi Electric Research Lab Research Gift (2015) Grants and Funding: NSF HDR TRIPODS (2019–2023) AFOSR: Enabling Trusted Human-Like Artificial Teammates (2018–2023) NSF: Optimal Sampling and Recovery for Multilinear Signals (2013–2016) Aeron teaches advanced courses in probabilistic systems analysis, information theory, and machine learning. He directs the Tufts Data Science undergraduate and graduate programs, and serves on editorial boards of journals including Frontiers in Signal Processing and IEEE Transactions on Geoscience and Remote Sensing .

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Travis Martinمشاهده پروفایل

استادیار

University of UtahUnited States

Travis Martin is an Assistant Professor (Lecturer) in the School of Computing at the University of Utah. His primary focus is on computer science undergraduate education. His research interests include computer science theory, machine learning, network science, graph algorithms, and mechanism design. He holds a PhD in Computer Science and Engineering (CSE) from the University of Michigan, completed in June 2016 under the advisement of Mark Newman and Mike Wellman. Previously, Martin worked as a Software Engineer at Recursion Pharmaceuticals and Google, contributing to data engineering and machine learning integration in Android systems. His academic work bridges theoretical computer science with practical applications in networks and algorithms, evident from his publications in journals like Phys. Rev. E and World Wide Web. Despite his research background, Martin currently does not supervise undergraduate or graduate research. His professional activities include open-source contributions and maintaining an updated code portfolio.

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Min Xuمشاهده پروفایل

استادیار

Rutgers, The State University of New JerseyUnited States

Min Xu is an Assistant Professor in the Department of Statistics at Rutgers University – New Brunswick. He is affiliated with the School of Arts and Sciences and focuses his research on theoretical and methodological aspects of machine learning and high-dimensional statistics, with applications in network analysis and nonparametric estimation. Education: Ph.D. in Machine Learning, Carnegie Mellon University (2015) B.S. in Electrical Engineering and Computer Science (with minor in Mathematics), UC Berkeley Research Interests: Min Xu’s research lies at the intersection of machine learning , high-dimensional statistics , and network science . He develops computationally scalable methods with strong theoretical guarantees for complex data structures, particularly in nonparametric estimation , network analysis , and large-scale inference . His work addresses fundamental challenges in estimating high-dimensional distributions and understanding the structure of evolving networks, with applications in economics and social sciences. Grants & Funding: NSF Grant DMS-2113671 NSF Grant DMS-2311299 Research Trends: Across his publications, a consistent theme is the development of statistically rigorous methods for high-dimensional and network data. His work spans optimal estimation in stochastic block models, convex M-estimation, and inference on dynamic network structures, with a strong emphasis on theoretical guarantees and practical scalability. Affiliations: Previously, Min Xu served as a departmental postdoctoral researcher in the Statistics Department at the Wharton School, University of Pennsylvania. He is currently based at Hill Center, Rutgers University.

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Herbie Leeمشاهده پروفایل

استاد

University of California, Santa CruzUnited States

Herbie Lee is a Professor in the Department of Statistics at the Jack Baskin School of Engineering, University of California, Santa Cruz. He also serves as the Interim Dean of Social Sciences. His faculty office is located in 537A Baskin Engineering, and he can be reached at 831-459-1655 (though email is preferred). Lee completed his educational journey with a strong foundation in mathematics and statistics: B.S. in Mathematics from Yale University with a specialty in statistics Ph.D. in Statistics from Carnegie Mellon University (1998), advised by Larry Wasserman Post-doctoral work at Duke University's ISDS department Professor Lee's research focuses on Bayesian statistics, particularly in the areas of computer models, spatial inverse problems, simulator emulation, adaptive experimental design, and optimization. His work bridges theoretical statistics with practical applications across diverse domains including cosmology (dark energy modeling), aircraft damage detection, electronic health records analysis, and environmental monitoring. He has made significant contributions to the understanding of neural networks within a statistical framework. An analysis of his recent publications reveals a strong emphasis on Gaussian process modeling, Bayesian optimization, and nonstationary spatial statistics. His work increasingly addresses complex real-world problems requiring sophisticated statistical approaches for mixed data types, constrained optimization, and real-time decision making. The interdisciplinary nature of his research is evident in collaborations with institutions like Lawrence Livermore National Laboratory and the Naval Postgraduate School. Among his notable achievements: Supervised PhD student Bobby Gramacy won the prestigious Savage Award Authored two books on Bayesian optimization and multiscale modeling Published over 50 peer-reviewed articles in top statistics and machine learning journals Professor Lee has supervised numerous graduate students, with his most recent PhD student graduating in 2024. His research has been supported by various grants, though specific funding sources aren't detailed in the provided text. His teaching portfolio includes courses on Bayesian statistics, design and analysis of computer simulation experiments, and statistical methods for biological, environmental, and health sciences. He is actively involved in several professional statistical societies including the International Society for Bayesian Analysis (ISBA), American Statistical Association (ASA), and Institute of Mathematical Statistics (IMS).

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Nicolas Garcia Trillosمشاهده پروفایل

دانشیار

University of Wisconsin-MadisonUnited States

Nicolas Garcia Trillos is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, with research spanning applied analysis, computational probability, statistics, and machine learning. His work focuses on the intersection of calculus of variations, optimal transport, and partial differential equations in learning systems. His educational background includes: Bachelor's degree in Mathematics from Universidad de Los Andes, Bogotá, Colombia (2010) Ph.D. in Mathematics from Carnegie Mellon University (2015) Prager Assistant Professor (postdoctoral position) at Brown University (2015-2018) Garcia Trillos' research centers on the mathematical foundations of machine learning , developing tools in optimal transport and calculus of variations to analyze graph-based learning and continuum models. Key contributions address adversarial robustness , federated learning , and spectral clustering , providing theoretical guarantees for large-scale problems through PDE frameworks. Recent publications (2022-2025) demonstrate a cohesive focus on geometric approaches to adversarial robustness , using optimal transport to derive classification bounds and study solution existence. He has advanced federated learning via consensus-based bi-level optimization (CB2O) and developed Fermat distances for metric approximation, bridging pure mathematics with statistical learning theory. No scientific awards were mentioned in the provided text. There is no information available regarding student advising, research grants, or laboratory affiliations in the source material.

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Sylvain Sardyمشاهده پروفایل

دانشیار

University of GenevaSwitzerland

Sylvain Sardy is an Associate Professor in the Department of Mathematics at the University of Geneva, where he conducts research at the intersection of statistics, optimization, and machine learning. He is affiliated with the Analysis, Mathematical Physics and Probability research group and has held significant editorial positions including Associate Editor for Computational Statistics and Data Analysis since 2020. Professor Sardy's research focuses on statistical machine learning, sparsity, and optimization with applications spanning astronomy, chemometrics, finance, and tomography. His work develops innovative methods for high-dimensional data analysis, particularly using wavelet-based approaches and LASSO regularization techniques for feature selection, denoising, and model selection. His publications reveal a consistent focus on finding sparse signals in complex datasets across diverse scientific domains. Professor Sardy has mentored numerous graduate students, currently supervising PhD candidate Maxime van Cutsem and having previously guided Dr. Xiaoyu Ma, Dr. Pascaline Descloux, Prof. Jairo Diaz Rodriguez, and Dr. Caroline Giacobino. His Master's students include Jairo Diaz (now Professor at Universidad del Norte, Colombia), Jean-Luc Baeriswyl, and others who have pursued careers in academia, industry, and education. His academic service includes leadership roles as Swiss representative at the European Regional Committee of the Bernoulli Society (2014-2018), President of the Doctoral School of Applied Statistics and Probability (2010-2013), and Student Advisor for the Mathematics Section (2008-2015). His teaching portfolio includes Optimization with Applications I, Statistical Machine Learning, and Pharmaceutical Statistics and Methodology, reflecting his expertise in statistical methodology and its practical implementation.

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Walter Dempseyمشاهده پروفایل

دانشیار

University of Michigan-Ann ArborUnited States

Walter Dempsey is an Associate Professor of Biostatistics at the University of Michigan School of Public Health and Assistant Research Professor at the Institute for Social Research. His research develops statistical methods for digital health, focusing on experimental design for multi-stage decision making, modeling of complex longitudinal data, and analysis of relational network structures. Education: Ph.D. in Statistics, University of Chicago (2015) B.Sc. in Mathematics, Statistics and Economics, University of Chicago (2009) Research Focus: Dr. Dempsey's work integrates statistical theory with health applications, particularly in mobile health (mHealth) technologies. His methodological research spans three interconnected areas: (1) Designing adaptive trials for health decision-making; (2) Developing hierarchical latent variable models for intensive longitudinal data from wearables and sensors; (3) Creating statistical frameworks for analyzing interaction networks that satisfy invariance principles while capturing empirical behavior patterns. Publication Trends: His recent work demonstrates strong emphasis on network modeling, mobile health interventions, and causal inference methods. Publications frequently appear in top statistics and machine learning venues including JASA, Biometrika, ICML, and NeurIPS, with consistent focus on developing interpretable models for health applications. Student Advising: Hera Shi (PhD, 2023) - Time-varying treatment effects in micro-randomized trials Yuhua Zhang (PhD, 2023) - Statistical methods for network data Madeline Abbott (Current PhD) - Latent variable models for intensive longitudinal data Easton Huch (Current PhD) - Robust Bayesian methods for causal inference Laboratory: Leads the Dempsey Lab developing statistical methodologies for digital health, with projects spanning network analysis, survival modeling, and adaptive intervention design.

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Bret Hanlonمشاهده پروفایل

دانشیار

University of Michigan-Ann ArborUnited States

Bret Hanlon, PhD is an Associate Professor in the Department of Biostatistics at the University of Michigan School of Public Health, where he serves as Associate Director of Clinical Trials for the SABER Research program. Trained at Cornell University (PhD, 2009), Harvard University (postdoc), and with earlier degrees from Texas Tech University (MS, 2005) and University of North Carolina (BS, 2003), Dr. Hanlon brings extensive expertise in biostatistical methodology and clinical research to his academic role. Dr. Hanlon's research spans both methodological development and applied statistical collaboration in biomedical and health services research. His methodological work focuses on high-dimensional inference , robust estimation techniques , branching processes with random effects , and regularized ordinal regression . Applied interests center on clinical trial design , particularly cluster- and stepped-wedge randomized trials, and analytic support for independent data monitoring in multi-site industry-sponsored studies. His work spans oncology , cardiology , bariatric surgery , and critical care , with recent projects using machine learning for risk prediction and scenario planning to guide end-of-life decision-making. Dr. Hanlon's publication record shows consistent productivity, with publications spanning biostatistics methodology, surgical outcomes, cancer research, and obesity studies. His recent work demonstrates increasing focus on machine learning applications in public health and clinical decision support. His methodological contributions in regularized regression and high-dimensional data analysis have been implemented in widely used statistical software packages. Aging Biostatistics Cancer Cardiovascular Health Clinical Trials Health Care Health Informatics Modeling Women's Health As a biostatistician with deep expertise in clinical trial design and analysis, Dr. Hanlon has served as principal investigator and lead statistician on numerous industry- and federally funded studies, including large-scale trials in oncology, cardiology, and population health. His collaborative work spans academic medicine and independent statistical oversight for pharmaceutical trials, demonstrating his ability to bridge theoretical statistical methodology with practical clinical applications.

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Jean Morrisonمشاهده پروفایل

استادیار

University of Michigan-Ann ArborUnited States

Jean Morrison serves as the John G Searle Assistant Professor in the Department of Biostatistics at the University of Michigan School of Public Health, a position she assumed in 2020 following postdoctoral work at the University of Chicago and doctoral studies at the University of Washington. Her academic trajectory reflects deep specialization in statistical methodologies for complex biological systems. Her educational foundation includes: PhD in Biostatistics, University of Washington (2016) BA in Mathematics, University of Chicago (2009) Dr. Morrison's research centers on statistical genetics and genomics , with pioneering work in high-dimensional phenotype analysis (e.g., brain imaging, proteomics, and clinical trait clusters). She develops advanced causal inference frameworks, notably for Mendelian randomization with robust pleiotropy handling, and integrates empirical Bayes methodologies with deep learning for genomic applications. Her approach emphasizes biologically interpretable low-rank representations of genetic associations. Recent publications (2022-2025) reveal escalating focus on multi-omics integration (genome, transcriptome, proteome), probabilistic fine-mapping of causal variants, and methodological refinements for Mendelian randomization. Her work increasingly addresses genetic pleiotropy complexity through factor analysis and develops computational tools like GWASBrewer for realistic simulation of genetic data. No scientific awards are documented in available sources. Similarly, public records contain no details regarding student advising, research grants, or laboratory leadership.

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Bingkai Wangمشاهده پروفایل

استادیار

University of Michigan-Ann ArborUnited States

Bingkai Wang is an Assistant Professor in the Department of Biostatistics at the University of Michigan School of Public Health. His research focuses on causal inference, clinical trials, and statistical methods for complex data. PhD in Biostatistics from Johns Hopkins University (2021) BS in Mathematics from Fudan University (2016) His work spans causal inference , machine learning , and test-negative designs in infectious disease research, with methodological contributions to semiparametric efficiency theory and cluster-randomized trials . Recent articles highlight advancements in model-robust inference , stepped-wedge trial designs , and handling incomplete outcomes in clinical trials. He has received multiple awards, including the 2024 IMS New Researcher Travel Award and the Margaret Merrell Award . Contact: bingkai@umich.edu

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