جست‌وجوی استاد و پژوهشگر

Gennady Samorodnitskyمشاهده پروفایل

استاد

• Stochastic Modeling
• Heavy Tailed Distributions
• Long-Range Dependence
• +۶ مورد دیگر

Gennady Samorodnitsky is a Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University. He holds a B.S. from the Moscow Steel and Alloys Institute (1978), M.S. from Technion – Israel Institute of Technology (1983), and a D.Sc. from Technion (1986). He joined Cornell in 1988 and has held visiting positions at the University of North Carolina at Chapel Hill and Boston University. His research focuses on stochastic processes, particularly heavy-tailed distributions, long-range dependence, and extreme value theory, with applications in finance, teletraffic, and climate modeling. Education: B.S., Moscow Steel and Alloys Institute, USSR, 1978 M.S., Technion – Israel Institute of Technology, 1983 D.Sc., Technion – Israel Institute of Technology, 1986 Samorodnitsky’s research interests span stochastic modeling, including heavy-tailed processes, self-similar processes, and extreme value analysis. He examines the behavior of financial and telecommunication systems under long memory and non-Gaussian conditions. Key areas include the statistical analysis of extremes in climate data and the theoretical foundations of stable and infinitely divisible processes. His work bridges probability theory with applications in risk management, network traffic analysis, and climate science. His publications explore topics such as high-level excursion sets in random fields, tail inference, and the interplay between ergodic theory and stochastic processes. He has contributed to books like Stochastic Processes and Long Range Dependence and authored numerous technical reports on topics like ruin probabilities and multivariate extremes. Samorodnitsky teaches advanced courses, including ORIE 7590: Martingales in Discrete and Continuous Time , and maintains an active role in academic conferences and collaborations. His research group investigates cutting-edge problems in high-dimensional extremes, privacy-aware learning, and topological data analysis.

Brent A. Johnsonمشاهده پروفایل

استاد

• Biostatistics
• Semi-parametric methods
• Missing data analysis
• +۹ مورد دیگر

Brent A. Johnson is a Professor of Biostatistics, Ophthalmology, and Neurology at the University of Rochester Medical Center. He holds joint appointments in the School of Medicine and Dentistry and leads the Biostatistics and Computational Biology Department. His research focuses on methodological advancements in semi-parametric methods, causal inference, and adaptive treatment strategies, with substantive applications in HIV/AIDS, environmental health, and clinical epidemiology. Johnson collaborates widely across disciplines, including cardiology, neurology, and global health, particularly in Sub-Saharan Africa. He directs the Biostatistics Core for the University of Rochester's Center for AIDS Research and serves as an on-call statistician for the Flaum Eye Institute. Education: PhD in Statistics from North Carolina State University (2003), MS in Biostatistics from University of Minnesota (1997), BA in Mathematics from St. Olaf College (1995). Awards include the NSF VIGRE Fellowship and Best Student Paper Award from the American Statistical Association. Research Interests: Dynamic treatment regimes, personalized medicine, missing data methods, biomarker development, and HIV prevention strategies. His lab develops statistical tools for observational studies and clinical trials, with recent work on machine learning approaches for censored outcomes and causal inference. Grants and Funding: NIH grants for HIV/AIDS research, environmental health projects, and methodological development. Collaborations include the AIDS Clinical Trials Group (ACTG), the South African TDF study, and the El Paso Asthma Cohort. Labs/Teams: Directs the Brent Johnson Lab, focusing on statistical methodology for biomedical applications. Key lab members include postdoctoral fellows like John Rice and alumni Li Li (Eli Lilly) and Xin Lu (Sanofi).

• Bayesian Methods
• Stochastic Processes
• Time Series Analysis
• +۳ مورد دیگر

Tevfik Aktekin is a Professor in the Department of Decision Sciences at the Paul College of Business and Economics, University of New Hampshire. His research focuses on Bayesian inference and stochastic modeling in service systems and business analytics. Education : Ph.D. in Decision Sciences (George Washington University), M.B.A. in Management Decision Making (George Washington University), B.S. in Mechanical Engineering (Yildiz Technical University). His work applies Bayesian state-space models to multivariate count data, with applications in call center staffing, product modification cycles, and public health analysis. He has published in journals like Annals of Applied Statistics , Bayesian Analysis , and European Journal of Operational Research . The 15 most recent articles highlight trends in sequential Bayesian learning for dynamic systems (e.g., bike-sharing, web traffic), time series of non-Gaussian data, and stochastic optimization under uncertainty. These works bridge statistical theory with practical applications in business and health.

Partha Lahiriمشاهده پروفایل

استاد

• Survey Sampling
• Small-Area Estimation
• Bayesian Inference
• +۳ مورد دیگر

Partha Lahiri is a Professor at the University of Maryland, College Park, holding dual appointments in the Joint Program in Survey Methodology (JPSM) and the Department of Mathematics . He also serves as an Adjunct Research Professor at the Institute of Social Research, University of Michigan, Ann Arbor. Previously, he was the Milton Mohr Distinguished Professor of Statistics at the University of Nebraska-Lincoln. His expertise spans Survey Sampling , Small-Area Estimation , Bayesian and Empirical Bayes Inference , and Resampling Methods . He has advised organizations such as the United Nations Development Program, World Bank, and Gallup, and contributed to panels like the U.S. Census Advisory Committee. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. Research interests include methodological advancements in small area estimation, survey data integration, and statistical linkage. His work emphasizes practical applications in policy and public health, including poverty measurement and pandemic-related studies (e.g., mask effectiveness perception during the pandemic). He has authored influential papers on topics like multi-goal prior selection, probabilistic record linkage, and resampling techniques for uncertainty quantification. His leadership roles, including directing JPSM, highlight his commitment to training future survey methodologists. Awards: ASA and IMS Fellowships, ISI Elected Membership Advisory Roles: U.S. Census Advisory Committee, National Academy of Sciences panels Publications Trends: Focus on small area estimation, Bayesian methods, and policy-relevant applications Grants/Projects: Collaborations with international organizations and academic institutions on statistical methodologies Lahiri’s research bridges theoretical statistics and practical survey challenges, with a strong emphasis on data integration and uncertainty estimation in complex systems.

Jonathan A. Kelnerمشاهده پروفایل

استاد

• Algorithms
• Complexity Theory
• Spectral Graph Theory
• +۴ مورد دیگر

Jonathan A. Kelner is a Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) . His research bridges pure mathematics and algorithms, focusing on spectral graph theory, combinatorial optimization, and distributed computing.

Marcelo Pereyraمشاهده پروفایل

دانشیار

• Mathematical Imaging
• Bayesian Analysis
• Computational Imaging
• +۳ مورد دیگر

Marcelo Pereyra is an Associate Professor in the School of Mathematics and Computer Science at Heriot-Watt University and the Maxwell Institute for Mathematical Sciences. He is a visiting professor at the Physics Laboratory of École Normale Supérieure de Lyon (ENS de Lyon) from April 8–29, 2023, hosted by Julián Tachella. His research focuses on Bayesian analysis, computational imaging, and inverse problems, with applications in signal processing and machine learning. Education: Electronic engineering degrees from universities in Buenos Aires and Toulouse, followed by a PhD in signal processing from the Université de Toulouse (2012). Postdoctoral fellowships included roles at the University of Bristol (2012–2016), funded by Marie Curie, Brunel, and French Ministry of Defense grants. In 2019, he held a visiting professorship at the Institut Henri Poincaré. Current collaboration with ENS de Lyon’s SiSyPh team involves developing Bayesian-deep learning methods for blind/semi-blind inverse imaging problems and uncertainty quantification in pandemic modeling (e.g., COVID-19 reproduction number estimation). His work bridges Bayesian inference, convex optimization, and Monte Carlo sampling techniques. Awards: Marie Curie Fellowship, Brunel Postdoctoral Fellowship, French Ministry of Defense Fellowship. Key Projects: Bayesian imaging with Plug-and-Play priors, empirical Bayesian regularization estimation, sparse Bayesian mass-mapping in astronomy. He delivered a seminar on April 26, 2023, titled “Machine Learning and Signal Processing.”

• Time Series Analysis
• Econometric Theory and Practice
• Heteroskedastic Models
• +۶ مورد دیگر

Hsein Kew is a Senior Lecturer in the Department of Econometrics and Business Statistics at Monash University. He holds a PhD in Economics from the University of Melbourne and previously worked as a researcher at the Melbourne Institute of Applied Economic and Social Research, focusing on empirical analyses of social security and labor market interactions. PhD in Economics, University of Melbourne Researcher, Melbourne Institute of Applied Economic and Social Research Senior Lecturer, Department of Econometrics and Business Statistics, Monash University His research primarily centers on time series analysis , heteroskedastic models , and non-parametric methods , with applications in financial econometrics and forecasting. He teaches Financial Econometrics and Data Analysis in Business, contributing to both theoretical and applied econometric education. The recent publications by Hsein Kew span from 2014 to 2024 and reflect a consistent focus on advanced econometric methodology. The articles demonstrate expertise in predictive regression models , unit root testing under volatility shifts , long memory processes , and autocorrelation testing under complex dependency and heteroskedastic structures . These works are published in top-tier journals such as the Journal of Econometrics and Econometric Theory , indicating a strong contribution to econometric theory and robust inference under non-standard conditions. Notable research projects include an Australian Research Council (ARC)-funded project titled A new class of statistical methods for analysing long memory time series models with heteroskedasticity (2010–2013), where he served as a Chief Investigator. This project aligns with his ongoing research interests in modeling time series with long memory and time-varying volatility. ARC Research Project: A new class of statistical methods for analysing long memory time series models with heteroskedasticity (2010–2013) While no scientific awards are listed in the provided text, his sustained publication record and involvement in funded research indicate active scholarly engagement. He has collaborated with prominent econometricians such as David Harris and Jiti Gao, suggesting integration into a strong research network. There is no mention of advising students or leading a lab, but his role as a Senior Lecturer implies teaching and mentorship responsibilities.

Lan Wenمشاهده پروفایل

استادیار

• Causal Inference
• Survival Analysis
• Biostatistics
• +۳ مورد دیگر

Lan Wen is an Assistant Professor in the Department of Biostatistics at the University of Waterloo. Their research focuses on causal inference, survival analysis, and missing data methodologies with applications in public health and epidemiology. They specialize in developing statistical methods for estimating causal effects in observational studies and clinical trials, particularly in the presence of confounding variables, censoring, and complex treatment regimens. Lan's work bridges theoretical advancements and practical applications, addressing challenges in longitudinal cohort studies, HIV prevention strategies, and comparative effectiveness research. They have contributed to methodologies such as the parametric g-formula, robust estimation techniques, and meta-analysis approaches for evaluating model performance. Key contributions include studies on grace periods in treatment comparisons, stochastic interventions in HIV prevention, and interpretability in causal inference. Their research emphasizes rigorous statistical modeling to inform healthcare policy and improve population health outcomes.

Yeying Zhuمشاهده پروفایل

دانشیار

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

Yeying Zhu is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. Her research focuses on causal inference, machine learning applications in networks, and statistical methodologies for handling missing data and confounding variables. She holds a Ph.D. in Statistics from The Pennsylvania State University (2013), an M.S. in Statistics & Applied Probability from the National University of Singapore (2008), and a B.S. in Statistics with a minor in International Economics & Trade from East China Normal University (2006). Her work bridges theoretical statistics with practical challenges in causal analysis, including mediation analysis, propensity score modeling, and network treatment effects. Recent research explores UAV-enabled integrated networks using reinforcement learning and anomaly detection in satellite systems through ensemble learning techniques. She has contributed to advancements in high-dimensional causal mediation analysis and semi-parametric estimation techniques. Notable areas of contribution include: Causal inference frameworks for non-ignorable missing data Machine learning-driven solutions for UAV trajectory optimization and satellite anomaly detection Development of robust statistical methods in longitudinal data and instrumental variable models Her articles reflect interdisciplinary collaborations spanning statistics, computer science, and engineering, emphasizing methodological innovation and real-world applicability.

HaiYing Wangمشاهده پروفایل

دانشیار

• Big Data Subsampling
• Optimal Experimental Design
• High-Dimensional Inference
• +۲ مورد دیگر

HaiYing Wang is an Associate Professor in the Department of Statistics at the University of Connecticut's College of Liberal Arts and Sciences. She earned her PhD from the University of Missouri and previously held positions at the University of New Hampshire. Her research develops optimal subsampling methods for big data analysis, focusing on model selection, survival analysis, and high-dimensional regression. Dr. Wang creates computationally efficient algorithms for massive datasets that maintain statistical accuracy while reducing computational burden. She has made significant contributions to subsampling techniques for logistic regression, quantile regression, and accelerated failure time models. Her methodological innovations enable practical analysis of streaming data and distributed computing environments. Dr. Wang teaches courses in computing for statistical data science, nonparametric methods, and statistical inference for big data. She serves as associate editor for multiple statistical journals and develops open-source software for efficient statistical computing.

• Non-parametric statistics
• High-dimensional statistics
• Bioinformatics
• +۴ مورد دیگر

Professor Jian Zhang is a Professor of Statistics at the University of Kent's School of Mathematics, Statistics and Actuarial Science. His research focuses on non-parametric and high-dimensional statistics, bioinformatics, computational biology, statistical genetics, neuroimaging methods, and Bayesian modeling. He has advised students including Jie Li and Tong Wang. His work spans theoretical advancements and applied methodologies across diverse fields such as genomics, neuroimaging, and biomedical data analysis. Publications highlight contributions to Bayesian inference, neuroimaging techniques, and statistical genetics. Notable collaborations include studies on mixture models for genetic association analysis and beamforming methods for functional connectivity. He holds an ORCID iD and is based at Canterbury Campus, University of Kent.

• Time Series Analysis
• Statistics
• Regression Modeling
• +۳ مورد دیگر

Rongning Wu is an Associate Professor at the Paul H. Chook Department of Information Systems and Statistics within the Zicklin School of Business at Baruch College, City University of New York . Holding a Ph.D. and M.S. in Statistics from Colorado State University and a B.S. in Applied Mathematics from Southeast University, Dr. Wu specializes in advanced statistical modeling with particular focus on time series analysis and count data models. Education : Ph.D./M.S. in Statistics (Colorado State University), B.S. in Applied Mathematics (Southeast University) Current Roles : Associate Professor, Committee Chair (Statistics Faculty Recruiting), Co-organizer (Department Research Seminar Series) Dr. Wu's research explores robust statistical methodologies for complex time series data, including Least Absolute Deviation estimation , single-index models with time series errors , negative binomial models for count series , and tail-trimmed absolute deviation techniques . His work addresses challenges in parameter estimation, variance modeling, and structural change detection for both finite and infinite variance processes. Recent publications highlight advancements in change-point estimation for count time series , conditional maximum likelihood for INGARCH models , and semiparametric approaches for discrete-valued processes . Dr. Wu's methodological contributions find applications in financial data analysis and public health research, particularly in modeling HIV transmission dynamics related to incarceration. Scientific Honors : Honored Faculty, Baruch College (2009-2011) IMS Travel Award (2009) James L. Madison Memorial Award (2004) Franklin A. Graybill Award (2003) As an active researcher, Dr. Wu has secured multiple PSC-CUNY grants (2008-2022) totaling over $52,000 for projects spanning time series estimation, regression modeling, and empirical likelihood methods. He has served on various academic committees including the ZSB Graduate Curriculum Committee and the Statistics Faculty Recruiting Committee, while maintaining editorial reviewing roles for 15+ international journals.

Kyle D. Rudserمشاهده پروفایل

استاد

• Clinical Trials
• Survival Analysis
• Longitudinal Analysis
• +۱ مورد دیگر

Kyle D. Rudser is a Professor in the Division of Biostatistics and Health Data Science at the School of Public Health, University of Minnesota - Twin Cities. He serves as the Director of the Biostatistical Design and Analysis Center (BDAC) within the Clinical and Translational Science Institute (CTSI). His educational background includes: PhD in Biostatistics from University of Washington (2007) MS in Biostatistics from University of Washington (2005) BA in Mathematics and Chemistry from St. Olaf College (2002) Dr. Rudser's research focuses on statistical methodologies including clinical trial design and monitoring, survival analysis techniques, longitudinal data analysis, and nonparametric approaches. His work emphasizes developing innovative statistical models for complex biomedical data with applications across diverse clinical domains. His publication portfolio demonstrates extensive expertise in survival analysis methodologies and clinical trial design, with recent collaborative work expanding into interdisciplinary health research areas including obesity interventions, urinary health epidemiology, and chronic disease management. Awards and recognition: Member, Delta Omega Honorary Society in Public Health As BDAC Director, he leads biostatistical support for translational research initiatives across the university medical center.

Bryan E. Shepherdمشاهده پروفایل

استاد

• Biostatistics
• Causal Inference
• Measurement Error
• +۳ مورد دیگر

Bryan E. Shepherd serves as Professor of Biostatistics and Biomedical Informatics and Vice Chair of Faculty Affairs in the Department of Biostatistics at Vanderbilt University. As a primary faculty member, he leads methodological research while overseeing departmental academic operations within the Vanderbilt University Medical Center ecosystem. His foundational education includes a PhD in Biostatistics from the University of Washington, establishing his technical expertise in advanced statistical methodologies. This training underpins his dual focus on theoretical innovation and real-world health applications. Dr. Shepherd's research centers on solving complex data challenges through novel biostatistical frameworks, particularly causal inference under measurement error, two-phase sampling optimization, and longitudinal analysis of error-prone clinical data. His work bridges theoretical statistics and urgent public health needs, with HIV research forming a major application domain where he addresses disparities in treatment access, care continuum outcomes, and comorbid conditions across global populations. Recent methodological breakthroughs include R packages for semiparametric likelihood estimation and rank correlation analysis. Analysis of his 2023-2025 publications reveals a dominant trend toward methodological solutions for data imperfections (measurement error, selective sampling) combined with high-impact applications in HIV epidemiology. His Latin American and African cohort studies consistently examine sex disparities, treatment efficacy, and social determinants of health, while his statistical innovations focus on efficiency gains in causal estimation and correlation modeling for clustered data. No specific scientific awards are documented in the available institutional profile. Though student advising details are unlisted, his extensive publication record—particularly collaborative international studies—implies active mentorship of junior researchers. His leadership as Vice Chair of Faculty Affairs suggests significant administrative responsibilities alongside research. Grant activity is inferred from multi-country HIV studies involving Vanderbilt's Center for Quantitative Sciences and Data Coordinating Center. Dr. Shepherd operates within Vanderbilt's collaborative research infrastructure, notably the Center for Quantitative Sciences (CQS) and Biostatistics Data Coordinating Center (VBDCC). His work leverages these resources for large-scale analyses of HIV cohorts across Latin America and Nigeria, focusing on treatment outcomes, genetic risk factors, and health system barriers. The Vanderbilt Nigeria Biostatistics Training Program (VN-BioStat) reflects his commitment to global capacity building in biostatistics.

Ilya Shpitserمشاهده پروفایل

دانشیار

• Causal Inference
• Missing Data
• Graphical Models
• +۵ مورد دیگر

Ilya Shpitser is a John C. Malone Associate Professor in the Department of Computer Science at Johns Hopkins University (JHU), within the Whiting School of Engineering. He is also a member of the Malone Center for Engineering in Healthcare and the Data Science and AI Institute. His research focuses on causal inference, missing data, graphical models, algorithmic fairness, and semi-parametric inference, with applications in healthcare, public health, and criminal justice. Shpitser holds a BA in Computer Science and Mathematics from UC Berkeley (1999), and MS (2004) and PhD (2008) in Computer Science from UCLA. Postdoctoral work included Harvard University and the University of Southampton. He has received awards such as the Causality in Statistics Education Award (2017) and the NSF CAREER Award (2020). His work addresses disparities in algorithmic bias, causal pathway analysis, and high-dimensional observational data. Key contributions include developing causal inference methods for missing data and fairness, as well as software tools like Ananke. He serves as an associate editor for the Journal of Causal Inference and the American Journal of Epidemiology. His research is funded by NSF, NIH, DARPA, and the Office of Naval Research. Shpitser advises numerous PhD students, many of whom have transitioned to academic and industry roles. His interdisciplinary collaborations span biostatistics, computer science, and public health, emphasizing real-world applications of causal methods.