جستجوی استاد راهنمای دانشگاهی

• Robust Statistics
• Machine Learning
• Biostatistics
• +۳ مورد دیگر

Matias Salibian-Barrera is a full Professor in the Department of Statistics at the University of British Columbia's Faculty of Science, where he leads research in robust statistical methodologies with applications across machine learning, biostatistics, and environmental sciences. His research focuses on developing computationally efficient robust techniques resistant to outliers and model misspecification. Key areas include: Robust regression and inference for high-dimensional data Robust causal inference with complex observational data Robust functional and spatial data analysis Integration of robust methods with deep learning architectures Applications in genomics, climate science, and finance His recent publications demonstrate a strong trend toward unifying robust statistics with modern machine learning, particularly in adversarial settings and high-dimensional inference, while maintaining rigorous theoretical foundations. Notable honors include: CRM–SSC Prize in Statistics Fellow of the American Statistical Association Killam Research Prize Coxeter-James Prize He actively supervises doctoral students and secures major grants from NSERC and CIHR, focusing on robust methods for biomedical and environmental data. His work involves collaborations with UBC's Machine Learning Group and the Pacific Institute for the Mathematical Sciences, where he develops open-source software for robust statistical computing.

• Public Health and Health Services
• Statistics
• Environmental Sciences
• +۴ مورد دیگر

Dr. Monica Pirani is a Lecturer in Biostatistics at the School of Public Health within the Faculty of Medicine at Imperial College London. She previously held roles as a Research Associate in Biostatistics at Imperial College and a Research Fellow in Statistics at the University of Southampton, collaborating with the Oceanographic Centre. Prior to this, she spent 10 years in Italy working as a Statistician and Sociologist for Public Health Agencies and the University of Modena and Reggio Emilia. Monica Pirani holds a PhD in Environmental Studies from King's College London, and multiple advanced degrees including an MSc and BSc in Biostatistics and Experimental Statistics from the University of Milano-Bicocca, an MSc in Epidemiology from the Catholic University of the Sacred Heart (Rome), and a Specialization in Sociology and a Degree in Political Science from the University of Bologna. Her research focuses on applying statistical methods to understand human-environment interactions, emphasizing the One Health framework. Key interests include spatial and spatio-temporal statistics, Bayesian methods, time-series analysis, and multi-source data fusion. She investigates ecological impacts of climate change, environmental health dynamics, and epidemiological phenomena, with notable work in São Paulo (Brazil) on temperature-related mortality and vector-borne diseases like dengue. Her methodological contributions span hierarchical Bayesian models and nonparametric statistics. Pirani’s articles (15 most recent listed) reflect a growing emphasis on climate change and pandemic health impacts. Recent work explores temperature-associated mortality trends, air pollution effects on brain health, and virtual collaboration strategies for interdisciplinary research. Her studies often bridge environmental science with public health policy, particularly in vulnerable populations and urban settings. She contributes to the Earth Observation Network, Epidemiology and Biostatistics department, and the Grantham Institute at Imperial College London. These affiliations support her focus on environmental monitoring, epidemiological analysis, and climate change mitigation strategies.

Nathan Kallusمشاهده پروفایل

دانشیار

• Machine Learning
• Causal Inference
• Operations Research
• +۳ مورد دیگر

Nathan Kallus is an Associate Professor at Cornell Tech and Cornell University, affiliated with the Department of Operations Research and Information Engineering (ORIE), as well as Computer Science (CS), Economics, Statistics, and Computational Applied Mathematics (CAM). His research focuses on data-driven decision-making, causal inference, optimization, and machine learning. Kallus holds a PhD from MIT and undergraduate degrees from UC Berkeley. He leads the Netflix Machine Learning & Inference Research team and advises students in topics like reinforcement learning, causal ML, and policy evaluation. His work bridges theory and practical applications, with contributions to A/B testing, off-policy evaluation, and spatiotemporal causal inference. Education: PhD in Operations Research (MIT), BA in Pure Mathematics, BS in Computer Science (UC Berkeley). Current research emphasizes causal inference powered by ML, distributional RL for LLM post-training, and efficient sequential decision-making. Recent projects include GST-UNet for spatiotemporal data, Value-Guided Search for reasoning, and nonparametric IV inference. His work has been recognized for its methodological rigor and impact on fields like healthcare, digital platforms, and public policy. Students and Collaborators : Advises PhD students (e.g., Antonia Oprescu, Kaiwen Wang) and alumni in academia and industry roles. Collaborates on projects spanning causal ML, fair AI, and large-scale experimentation. Recruits motivated PhD candidates for interdisciplinary research. Labs and Teams : Research Director at Netflix’s Machine Learning & Inference group, leading work on decision rules, recommendation systems, and causal analysis. Active in Cornell’s ORIE department and affiliated with interdisciplinary initiatives in AI and statistics.

Adityanand Guntuboyinaمشاهده پروفایل

دانشیار

• Nonparametric Statistics
• High-Dimensional Statistics
• Shape-Constrained Estimation
• +۳ مورد دیگر

Adityanand Guntuboyina is an **Associate Professor and Deputy Chair** in the **Department of Statistics** at the **University of California, Berkeley**. His research focuses on **nonparametric and high-dimensional statistics**, **shape-constrained statistical estimation**, **empirical processes**, and **statistical information theory**. He holds a PhD and has contributed to foundational work in modern statistical methodologies. His research emphasizes theoretical advancements in statistical estimation under shape constraints, with applications to machine learning and high-dimensional data analysis. Notable contributions include methodologies in convex regression, mixture models, and empirical Bayes approaches. Prof. Guntuboyina has published extensively in top-tier journals like the *Journal of the American Statistical Association* and *The Annals of Statistics*. His work bridges statistical theory with computational methods, addressing challenges in modern data science. He advises on grants related to nonparametric estimation and collaborates widely in the statistics community. His lab focuses on developing robust statistical tools for complex datasets, emphasizing both theoretical rigor and practical applicability.

Azeem Shaikhمشاهده پروفایل

استاد

• Econometric theory
• Partial identification
• Multiple testing
• +۶ مورد دیگر

Azeem M. Shaikh is the Ralph and Mary Otis Isham Professor of Economics at the University of Chicago's Kenneth C. Griffin Department of Economics and serves as Department Chair. He also holds the Thornber Research Fellowship and co-directs the Becker Friedman Institute’s Big Data Initiative. His research focuses on econometric theory, including multiple testing, resampling methods (e.g., bootstrap), and partially identified models. He has contributed to applications in policy evaluation and causal inference, with notable work on randomized experiments and imperfect compliance. Educated at Duke University (B.S. in Mathematics) and Stanford University (Ph.D. in Economics), Shaikh has been affiliated with the University of Chicago since 2007. His research has received NSF funding and prestigious awards such as the Dennis J. Aigner Award for Applied Econometrics and Alfred P. Sloan Fellowship. He is an elected fellow of the Econometric Society and International Association for Applied Econometrics, and serves on editorial boards of the Econometrics Journal and Journal of Econometrics . Shaikh’s work bridges theoretical econometrics with practical policy challenges, emphasizing methodological rigor in addressing real-world problems like education program evaluation and health survey biases. His recent studies explore nonrepresentative sampling in population health research and novel variance estimators for stratified experiments. Awards: Hoover Fellowship, Sloan Fellowship, Econometric Society Fellowship Grants: NSF support for econometric methods in clustered data and covariate-adaptive randomization Key Contributions: Randomization inference frameworks, multiple testing adjustments, and causal effect identification under monotonicity

Dong-Hyuk Kimمشاهده پروفایل

مدرس ارشد

• Industrial Organization
• Econometrics
• Microeconomics
• +۳ مورد دیگر

Dr. Dong-Hyuk Kim is a Senior Lecturer in the School of Economics at the University of Queensland, within the Faculty of Business, Economics and Law. He holds a PhD in Bayesian Econometrics for Auction Models from the University of Arizona (2010). His research focuses on Industrial Organization, Econometrics, and Microeconomics, with emphasis on auction theory, risk aversion, and development economics. Dr. Kim is affiliated with the Centre for Efficiency and Productivity Analysis and has published in top journals such as Journal of Econometrics and European Economic Review . His work bridges theoretical econometrics with applied economic problems, including optimal reserve pricing strategies, bidder behavior analysis, and the empirical relevance of ambiguity in auction models. Recent projects include studies on drought-resistant maize varieties in Malawi and government expenditure impacts in Sri Lanka. Dr. Kim is available for PhD supervision and actively contributes to methodological advancements in structural estimation techniques. Publications span topics like procurement mechanisms, risk-averse bidding strategies, and demand modeling using random coefficients frameworks. Ongoing research explores carbon emission trading in China's coal sector and managerial impacts on firm performance post-privatization in Vietnam.

• Nonstationary time series econometrics
• Nonparametric statistics
• Econometric Theory
• +۲ مورد دیگر

Professor Qiying Wang is a faculty member in the School of Mathematics and Statistics at the University of Sydney, holding the rank of Professor. His research focuses on advanced statistical theory and econometrics, particularly in nonstationary time series analysis, cointegrating regression, and asymptotic methods. He has published extensively in top journals such as Econometric Theory and Journal of Econometrics . Research Interests: Nonstationary time series econometrics, nonparametric statistics, econometric theory, local time theory, and self-normalized limit theory. He is a leading expert in nonlinear cointegrating regression, developing methodologies for handling endogeneity and nonstationarity in economic and financial data. Selected Grants: 2016: Nonlinear cointegrating regression with endogeneity (Australian Research Council DP) 2012: Asymptotics in nonlinear cointegrating regression (Australian Research Council DP) Key Contributions: Authored the monograph Limit Theorems for Nonlinear Cointegrating Regression (2015) and pioneered techniques for optimal bandwidth selection in cointegrating models. His work bridges theoretical advancements with practical econometric applications.

• Machine Learning
• Artificial Intelligence
• Bayesian Methods
• +۵ مورد دیگر

Mark van der Wilk is an Associate Professor in the Department of Computer Science at the University of Oxford and a Tutorial Fellow at Hertford College. His research focuses on machine learning, particularly Bayesian methods, equivariance in neural networks, and applications in science and industry. He leads a research group addressing challenges in generalization, architecture design, and efficient learning. Education: PhD in Machine Learning from the University of Cambridge (2017), undergraduate degree in Engineering. Previously held academic positions at Imperial College London and worked in industry with Dr. James Hensman. Research Interests: Automatic discovery of inductive biases, architecture optimization, Bayesian deep learning, causal inference, and applications in bioengineering and chemistry. Collaborates with industry and academia on practical machine learning solutions. Awards: Best Paper Award at NeurIPS/ICML conferences, Qualcomm Innovation Fellowship (PhD). Active in open-source contributions, including GPflow and GPflux libraries. Key Projects: Development of Competitive Amplification Networks for molecular diagnostics, turbulence testing for LLMs, and sparse Gaussian process approximations. Supervises PhD students focusing on alignment of mathematical and practical machine learning challenges.

Anuj Abhishekمشاهده پروفایل

استادیار

• Inverse Problems
• Integral Geometry
• Microlocal Analysis
• +۳ مورد دیگر

Anuj Abhishek is an Assistant Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University, affiliated with the College of Arts and Sciences. His research focuses on statistical and numerical methods for inverse problems, integral geometry, and microlocal analysis. He holds a faculty position in a top-tier mathematics department and contributes to advancing mathematical techniques for solving complex imaging and data analysis challenges. Contact: axa1828@case.edu. Research Interests: Dr. Abhishek's work bridges theoretical mathematics with applied problems, particularly in developing robust algorithms for inverse problems. His interests include the Radon transform, Bayesian methods, and numerical solutions for partial differential equations. He explores applications in medical imaging (e.g., electrical impedance tomography) and signal processing. Recent Work Trends: His publications (2018–2025) emphasize advancing inversion techniques for Radon transforms, improving neural network-based approaches for inverse problems, and refining Bayesian estimators for imaging modalities. Key themes include noise resilience, adaptive estimation, and interdisciplinary applications in physics and medicine. Advising & Grants: No specific grants or advisees are listed, but his research likely involves graduate students in applied mathematics and statistics. His website (https://sites.google.com/view/anujabhishek/home) may provide further details on collaborative projects and funding.

Hal Sternمشاهده پروفایل

استاد

• Bayesian Statistical Methods
• Forensic Science
• Psychology
• +۵ مورد دیگر

Hal Stern is Chancellor’s Professor and Provost/Executive Vice Chancellor at the University of California, Irvine (UCI), affiliated with the Department of Statistics within the Donald Bren School of Information & Computer Sciences. He co-directs the National Institute of Standards and Technology (NIST)-funded Center for Statistics and Applications in Forensic Evidence, focusing on improving forensic evidence reliability. Additionally, he leads the National Institute of Mental Health (NIMH)-funded Conte Center, studying early-life adversity impacts on neurodevelopment and mental health. His research integrates Bayesian statistical methods with applications in forensic science, psychology, and neuroscience. Key research areas include forensic evidence analysis (e.g., bloodstain patterns, footwear impressions) and the neurobiological consequences of unpredictable early environments. Stern has pioneered statistical frameworks for combining data across studies to address real-world challenges, such as reducing wrongful convictions through rigorous forensic science. His work on maternal sensory signal unpredictability has revealed links between early-life stress and later mental health outcomes, including depression and anxiety. His funded projects bridge disciplines, uniting statisticians, psychologists, and neuroscientists. Notable collaborations include developing biomarkers for early-life adversity and refining methods to assess forensic evidence reliability. Stern’s administrative roles at UCI further emphasize his leadership in advancing interdisciplinary research and academic governance.

Lei Fangمشاهده پروفایل

مدرس

• Bayesian Machine Learning
• Statistical Learning
• Wireless Sensor Networks
• +۴ مورد دیگر

Dr. Lei Fang is a Lecturer in the School of Computer Science at the University of St Andrews. Their research focuses on Bayesian machine learning, statistical learning, wireless sensor networks, and adversarial machine learning. They advise PhD students Zhongliang Guo and Weiye Li. Key research interests include Bayesian nonparametrics, transfer learning, and human activity recognition through sensor data analysis. Dr. Fang's work spans interdisciplinary areas such as federated learning for healthcare applications and security in neural networks. They are affiliated with the Centre for Research into Ecological & Environmental Modelling, contributing to projects involving complex system analysis and sensor network optimization. Recent publications highlight advancements in adversarial attacks on signature verification systems, link prediction in heterogeneous social networks, and AI-driven software repository analysis. Their research emphasizes practical applications in cybersecurity, healthcare monitoring, and environmental modeling.

Yuichi Kitamuraمشاهده پروفایل

استاد

• Econometrics
• Nonparametric Statistics
• Bayesian Methods
• +۴ مورد دیگر

Yuichi Kitamura is the Alfred Cowles Professor of Economics and Management at Yale University. He holds a Ph.D. in Economics from Yale and has held faculty positions at the University of Minnesota, University of Wisconsin, and University of Pennsylvania before returning to Yale in 2004. His research focuses on econometrics, including empirical likelihood, inverse problems, nonparametric Bayesian methods, and dynamic discrete choice models. Recently, he developed frameworks for analyzing economic theories using computational science advancements. Editor of Japanese Economic Review Associate/Co-editor for journals like Econometrica , Econometric Theory , and Journal of Business and Economic Statistics Kitamura has received prestigious awards such as the Nakahara Prize and Sloan Fellowship. He has held visiting roles at institutions including the University of Bristol, Cambridge University, and Bar-Ilan University. His research bridges computational science and economic theory, emphasizing methodological innovation. Grants include multiple NSF awards. He actively contributes to academic governance through editorial roles and conferences, including plenary speaking engagements at major international events.

Jennifer Hillمشاهده پروفایل

استاد

• Causal Inference
• Bayesian Statistics
• Machine Learning
• +۳ مورد دیگر

Dr. Jennifer Hill is a Professor of Applied Statistics, Co-Chair of the Department of Applied Statistics, Social Science, and Humanities (ASH), and Co-Director of the PRIISM Center at New York University's Steinhardt School. She holds a PhD in Statistics from Harvard University (2000) and a post-doctoral fellowship in Child and Family Policy from Columbia University (2002). Her research focuses on causal inference methods, Bayesian nonparametric techniques, and addressing challenges in observational studies. Notable contributions include the development of the bartCause software for machine-learning-based causal analysis. Key research areas include causal modeling, policy evaluation, and data science for social impact. Hill co-founded the MS in Applied Statistics for Social Science Research (A3SR), emphasizing social impact through data science. She has been recognized with the 2021 NYU Distinguished Teaching Award and the 2016 Miller Prize for her influential work on bias in grouped data analysis. Her courses cover causal inference, missing data analysis, and data science communication. Hill’s work bridges statistical innovation with real-world applications in health, education, and social policy. The A3SR program, with its dual MPA option, reflects her commitment to training socially responsible data scientists.

Shanshan Zhaoمشاهده پروفایل

دانشیار مدعو

• Biostatistics
• Environmental Health
• Cancer Research
• +۷ مورد دیگر

Dr. Shanshan Zhao is an Adjunct Associate Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill and a Principal Investigator at the National Institute of Environmental Health Sciences (NIEHS). Her research focuses on developing statistical methods to understand environmental and social influences on health, particularly in cancer epidemiology and risk prediction. She holds a secondary appointment in the NIEHS Epidemiology Branch and leads a group integrating statistical methodology with population-based studies. Her work emphasizes disease risk assessment using survival analysis, longitudinal data, and biomarker evaluation. Key research areas include multivariate failure time data, environmental mixture analysis, and epigenetic biomarkers. She has contributed to studies on breast, prostate, and colorectal cancers, emphasizing spatial-temporal modeling and public health applications. Zhao’s awards include the 2014 ASA David P. Byar Travel Award and recognition for her 2023 NIEHS publication on environmental breast cancer risk factors. Her team develops software tools like mhazard for multivariate survival analysis and STAFT for spatio-temporal modeling. Her lab collaborates across disciplines to advance environmental health sciences, with a focus on translating statistical innovations into actionable insights for clinicians and policymakers.

Rebecca Killickمشاهده پروفایل

استاد

• Time Series Analysis
• Changepoint Detection
• Wavelet Methods
• +۱ مورد دیگر

Professor Rebecca Killick is Chair of Statistics at Lancaster University's School of Mathematical Sciences. She develops statistical models for nonstationary time series with applications in environmental science, health monitoring, and econometrics. Research expertise includes: Changepoint detection algorithms Locally stationary wavelet processes Multivariate changepoint methods Environmental time series forecasting Recent publications apply changepoint methods to soil moisture dynamics, ecosystem services, and economic indicators. Work appears in statistics and interdisciplinary journals with strong computational focus. Current research group includes: 9 PhD students and postdoctoral researchers Projects funded by EPSRC, NERC, and Innovate UK Software contributions: changepoint R package (CRAN) changepoint.np for nonparametric detection EnvCpt for environmental applications