جست‌وجوی استاد و پژوهشگر

Kelley M. Kidwellمشاهده پروفایل

استاد

• Clinical Trial Design
• Sequential Multiple Assignment Randomized Trials (SMARTs)
• Bayesian Methods
• +۵ مورد دیگر

Kelley M. Kidwell is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. She specializes in clinical trial design and analysis, particularly through Sequential Multiple Assignment Randomized Trials (SMARTs) and small-sample methodologies. Her work bridges statistical innovation with real-world applications in chronic diseases, rare diseases, and mental health. Education PhD in Biostatistics, University of Pittsburgh (2012) BS in Mathematics, Bucknell University (2007) Research Focus Dr. Kidwell's research emphasizes adaptive clinical trial frameworks, including: Development of snSMART (small-sample SMART) designs with Bayesian and Frequentist methods Incorporation of patient preferences into dynamic treatment regimens Applications in adolescent risky behaviors, insomnia, chronic pain, suicide prevention, and oncology Article Trends Her recent publications demonstrate expertise in: Bayesian and Frequentist statistical approaches Adaptive trial design for rare and chronic conditions Telehealth and digital health implementation Pharmacogenetics in cancer treatment Alcohol use disorder interventions Physical activity and stroke rehabilitation

• Machine Learning
• Artificial Intelligence
• Data Science
• +۷ مورد دیگر

Pan Xu is a tenure-track Assistant Professor at Duke University with joint appointments in the Department of Biostatistics & Bioinformatics , Department of Computer Science , and Department of Electrical & Computer Engineering . He earned his Ph.D. in Computer Science from the University of California, Los Angeles (2021) and completed postdoctoral training at the California Institute of Technology (2021-2022) . Research Focus: Machine Learning, Reinforcement Learning, Optimization, and High-Dimensional Statistics with applications in Bioinformatics and Healthcare. Key Contributions: Developing algorithms for robust sequential decision-making under uncertainty, improving Thompson Sampling efficiency, and advancing multi-agent reinforcement learning. Scientific Recognition: NSF award for exploration in decision-making (2023) Whitehead Scholar award (2023) PIMCO Postdoctoral Fellowship (2022) Best Paper Award at ACM FAccT (2023) Teaching: Offers graduate courses in machine learning and decision-making frameworks. Service: Serves as Area Chair for ICML, NeurIPS, and AAAI; Action Editor for TMLR.

• Structural Equation Modeling
• Bayesian Statistics
• Adolescent Mental Health
• +۳ مورد دیگر

Dr. G Smid is a Researcher in the Department of Psychology within the Faculty of Social and Behavioural Sciences at Utrecht University, specializing in Clinical Psychology. His expertise centers on advanced statistical methodologies applied to psychological research, with particular emphasis on structural equation modeling under constrained data conditions. His primary research domains include Structural Equation Modeling, Bayesian Statistics, Adolescent Mental Health, Clinical Psychology, Data Synthesis, and Systematic Reviews. He has pioneered critical methodological frameworks for Bayesian SEM applications with small samples, developed interpretable synthetic data techniques, and investigated adolescent mental health determinants including harmful sexual behavior and immigration-related impacts. His work bridges complex statistical theory with clinical psychology applications. Analysis of Dr. Smid's 13 publications (2014-2023) reveals a dominant methodological trajectory focused on overcoming small-sample limitations in psychological research. His most influential contributions address Bayesian prior specification in SEM, measurement equivalence validation, and adolescent mental health risk factors. The publications demonstrate consistent innovation in statistical methodology while maintaining strong clinical relevance, particularly in adolescent psychology and mental health assessment.

Hanti Linمشاهده پروفایل

دانشیار

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

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

• Statistics
• Probability
• Machine Learning
• +۶ مورد دیگر

Feng Yu is a Senior Lecturer in Statistics at the School of Mathematics, University of Bristol, focusing on probability and statistical methods. Research interests include Bayesian inference, statistical hypothesis testing, machine learning applications in medical research, and interdisciplinary work with biomedical engineering. Key contributions involve developing methods for multiple comparison procedures, dose-finding trials, and 3D printing parameter optimization using AI. Recent publications highlight trends in integrating statistical rigor with biomedical innovation, spanning clinical trials, dose-finding studies, and additive manufacturing for bone scaffolds and implants. Collaborations include work on hybrid systems for tissue regeneration. Projects include "Girsanov Transformation and the Rate of Adaptation" (2011-2013), exploring probabilistic models in evolutionary biology. Affiliations are centered on the University of Bristol, with no scientific awards mentioned.

• Statistics
• Applied Statistics
• Precision Agriculture
• +۳ مورد دیگر

Signe Marie Jensen is an Associate Professor in the Department of Plant and Environmental Sciences at the University of Copenhagen's Faculty of Science. Her research bridges statistical methodology with agricultural applications, focusing on precision agriculture and plant phenotyping. She leads innovative work applying computer vision and machine learning to solve critical agricultural challenges. Education: MSc in Mathematical Economics, University of Copenhagen (2007) PhD in Applied Statistics, University of Copenhagen (2015) Dr. Jensen's research interests center on statistical methods for simultaneous inference and analysis of phenotyping data. Her work has increasingly focused on applying advanced machine learning techniques, particularly YOLO algorithms, to agricultural challenges including pest detection, weed management, and plant phenotyping. She develops statistical frameworks that enable more accurate and efficient analysis of complex agricultural data. Her recent publications reveal a strong trend toward interdisciplinary research combining statistics, computer vision, and plant science. The majority of her 2024-2025 publications focus on applying YOLO-based object detection systems to agricultural problems, particularly in precision farming applications. These works demonstrate her ability to translate statistical theory into practical agricultural technologies that address food security and sustainable farming challenges. Dr. Jensen's research output shows strong international collaboration, with co-authors from multiple countries working on agricultural technology development. Her work on the PhenoLab platform represents a significant contribution to high-throughput plant phenotyping infrastructure. She maintains active research in both theoretical statistics (simultaneous inference methods) and applied agricultural technology development, demonstrating remarkable versatility across these domains. Her current work on deep learning applications for crop monitoring represents the cutting edge of precision agriculture research.

Rolf Larssonمشاهده پروفایل

استاد

• Time Series Analysis
• Econometrics
• Statistical Modeling
• +۵ مورد دیگر

Rolf Larsson is a Professor at the Department of Mathematics at Uppsala University, Sweden. His academic career spans theoretical statistics, econometrics, and applied statistical analysis across multiple disciplines. His primary research interests include: Time Series Analysis and Econometrics Statistical Inference and Methodology Panel Data Analysis Climate Data Modeling Bayesian and Frequentist Statistics Multivariate Statistical Methods Discrete Data Analysis Professor Larsson's publication record demonstrates exceptional breadth across statistical theory and applications. His work on autoregressive models, unit root testing, and confidence distributions represents significant theoretical contributions to statistics. He has also made important applied contributions in climate science through his analysis of ice core data examining relationships between temperature, CO2, and methane. His methodological innovations span from discrete factor analysis to shrinkage estimators in beta regression models, with applications in econometrics, educational data, and medical research. His recent work on confidence distributions for autoregressive parameters (2024) continues his long-standing interest in foundational statistical theory. His collaborative work extends across disciplines, including medical statistics research examining the relationship between serotonergic medication, sunshine, and suicide. His publication record shows consistent high-quality output across top statistical and econometric journals over multiple decades.

Elena Goldmanمشاهده پروفایل

استاد

• Financial Econometrics
• Bayesian Econometrics
• Risk Management
• +۳ مورد دیگر

Elena Goldman is a Professor of Finance and Economics at the Lubin School of Business, Pace University. She holds a PhD in Economics from Rutgers University and has held key academic roles including Undergraduate Finance Program Chair (2012–2015). Her professional engagements include a fellowship at the U.S. Securities and Exchange Commission (2016) and membership on the Education Committee of the Professional Risk Managers' International Association (PRMIA). Her research specializes in: Financial Econometrics : Bayesian methods, volatility modeling, and time series analysis. Risk Management : Systemic risk assessment, margin modeling, and procyclicality mitigation. International Finance : Corporate finance dynamics in emerging markets (e.g., Indian firms' export-leverage relationships). Her publications (15 most recent) predominantly explore econometric innovations for financial risk quantification, Bayesian/frequentist statistical frameworks, and emerging-market corporate behaviors. Trends indicate a focus on regulatory applications (e.g., CCP margin requirements) and methodological rigor in volatility modeling. Awards & Honors: Fellow, Securities and Exchange Commission (2016)

Imon Banerjeeمشاهده پروفایل

عضو هیئت علمی

• Statistics
• Machine Learning
• Probability Theory
• +۳ مورد دیگر

Imon Banerjee is a Research Assistant Professor and IEMS Alumni Fellow at the McCormick School of Engineering, Northwestern University. He earned his PhD from Purdue University under Vinayak Rao and Harsha Honnappa, and completed his Bachelors and Masters in Statistics at the Indian Statistical Institute, specializing in Mathematical Statistics. He is currently on the 2025 academic job market and maintains an Erdős number of 4. Northwestern University: Research Assistant Professor, IEMS Alumni Fellow Purdue University: PhD in Statistics Indian Statistical Institute: B.S. & M.S. in Statistics His research spans statistical theory, computational methods, and applied probability. He has rigorously developed statistics for controlled Markov chains in finite and continuous state spaces, with Bayesian and Frequentist approaches, and is compiling these works into a monograph. He explores geometrical interpretations of statistics (pioneered by Efron) and has extended Kalman filter methodology for non-Gaussian noise, demonstrating sub-optimality of traditional estimators. Collaborative work includes sparse principal component analysis (sparse PCA). Imon actively seeks collaboration in geometry-driven statistical modeling, Markov chains, and non-Gaussian estimation. He welcomes discussions via email and is currently engaged in advancing theoretical frameworks for bootstrapping and statistical geometry. Scientific Awards: IEMS Alumni Fellow (Northwestern University) Imon is affiliated with Northwestern University's McCormick School of Engineering and Purdue University's Department of Statistics (alumni). His work bridges theoretical rigor with practical applications in computational statistics, state estimation, and machine learning.

Hongmei Jiangمشاهده پروفایل

استاد

• High-throughput genomic analysis
• Metagenomic data analysis
• Microbiome data normalization
• +۵ مورد دیگر

Hongmei Jiang is a Professor of Statistics and Data Science and the MS Program Director in the Department of Statistics and Data Science at Northwestern University's Weinberg College of Arts & Sciences. She holds a Ph.D. from Purdue University (2004). Her research focuses on statistical methodologies for genomic and metagenomic data analysis, including microbiome normalization, multiple comparisons, survival analysis, and precision medicine applications. She has contributed significantly to understanding microbial community dynamics and their implications in health and disease. Her work spans bioinformatics, epigenetics, and clinical statistics, with recent publications addressing microbiome data imputation, primate gut microbiota's role in metabolism, and cardiovascular disease risk prediction using neural networks. She has collaborated on studies involving epigenetic aging, HIV/HPV interactions in carcinogenesis, and forensic microbial source tracking. Dr. Jiang's methodological innovations include novel statistical frameworks for differential abundance testing, quasi-likelihood models for microbiome data, and graphical lasso extensions for omics network analysis. Her research emphasizes interdisciplinary applications across biology, medicine, and ecology, leveraging both frequentist and machine learning approaches. Education: Ph.D. in Statistics, Purdue University, 2004 Affiliations: Department of Statistics and Data Science, Northwestern University Labs/Teams: MS Program Leadership, Metagenomic Data Analysis Group

Thomas Severiniمشاهده پروفایل

استاد

• Likelihood-Based Methods
• Higher-Order Asymptotic Approximations
• Statistical Methodology in Finance
• +۳ مورد دیگر

Thomas Severini is a Professor of Statistics and Data Science at Northwestern University's Weinberg College of Arts & Sciences. He holds a Ph.D. from the University of Chicago (1987). His research focuses on likelihood-based statistical methods, including higher-order asymptotic approximations and applications in finance, econometrics, and sports analytics. He has authored influential works such as Introduction to Statistical Methods for Financial Models (2017) and Analytic Methods in Sports (2014). Key research interests include: (1) Development of statistical methodology for complex models, (2) Application of likelihood-based techniques in finance/econometrics, (3) Analysis of sports performance data. Notable contributions include work on integrated likelihood functions and jet lag effects on MLB performance. Publications span top journals like Biometrika , Journal of Econometrics , and Proceedings of the National Academy of Sciences . His work bridges theoretical statistics with practical applications in diverse domains.

Emmanuel J. Dorleyمشاهده پروفایل

استادیار

• Artificial Intelligence
• Virtual Agents
• Intelligent Tutoring Systems
• +۳ مورد دیگر

Dr. Emmanuel J. Dorley is an Assistant Professor in the Department of Computer and Information Science and Engineering at the University of Florida's Herbert Wertheim College of Engineering. His research explores intelligent systems for interpersonal skills development, including virtual agent-based learning simulations, interpersonal skills modeling/assessment, and feedback visualization. Previously, he was a PostDoc at USC Information Sciences Institute through a CRA Computing Innovation Fellowship. Education: Ph.D. in Computer Science, University of Southern California (2021) M.S. in Computer Science, University of Southern California (2021) M.Sc. in Robotics, University of Birmingham (2014) B.S. in Computer Engineering, North Carolina A&T (2013) Research Focus: Dr. Dorley specializes in Virtual Agents, Intelligent Tutoring Systems, and Automated Feedback mechanisms to enhance interpersonal skill development. His work bridges artificial intelligence with educational psychology to create adaptive learning environments. Publication Trends: Recent articles focus on AI applications in education and negotiation training, demonstrating consistent emphasis on human-agent interaction, personalized learning systems, and reproducibility in educational technology research. Awards: New Brunswick High School Hall of Distinguish Alumni (2022) CRA Computing Innovation Fellow (2021) USC Order of Arête (2021) NSF Graduate Research Fellowship (2014) Fulbright Fellowship (2013)

• Statistical Machine Learning
• High-Dimensional Statistics
• Likelihood-Free Inference
• +۳ مورد دیگر

Summary Ann B. Lee is a Professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University (CMU). She serves as Co-Director of the Ph.D. Program in Statistics. Previously, she was the J.W. Gibbs Assistant Professor at Yale University and a visiting researcher at Brown University. Her research focuses on developing statistical methods for complex data in physical sciences, including trust-worthy uncertainty quantification, likelihood-free inference, and applications in astronomy, climate science, and hurricane dynamics. Education: Ph.D. in Physics from Brown University (2002); M.Sc./B.Sc. in Engineering Physics from Chalmers University of Technology (Sweden). Key Research Interests: - Scientific Machine Learning - Uncertainty Quantification (UQ) - Likelihood-Free Inference - Tropical Cyclone Analysis - High-Dimensional Data Modeling She leads the STAMPS (STAtistical Methods for Physical Sciences) research group, which bridges classical statistics and machine learning. Recent work includes methods for estimating ocean thermal responses to hurricanes, probabilistic forecasting, and diagnostics for generative models. Her team collaborates with climate and astrophysics communities, hosting public webinars and symposia. Advising: Supervised over 15 PhD students, including graduates now in academia and industry. Current advisees include Luca Masserano and Alex Shen. Labs/Teams: Co-directs the STAMPS Research Center at CMU, launching in Fall 2024 as a university-wide initiative.

Carlos Cuevasمشاهده پروفایل

استاد

• Victimization and Trauma
• Sexual Violence
• Family Violence
• +۴ مورد دیگر

Carlos Cuevas serves as Professor of Criminology and Criminal Justice and Co-Director of the Violence and Justice Research Lab at Northeastern University's School of Criminology and Criminal Justice within the College of Social Sciences and Humanities. His educational background includes a PhD in Clinical Psychology from the California School of Professional Psychology at Alliant International University. Professor Cuevas specializes in victimization and trauma research with emphasis on sexual violence, family violence, and psychological assessment among Latino populations. His work examines mental health impacts of hate and bias victimization, service utilization patterns, and teen dating aggression. Current projects include developing instruments for measuring bias victimization among youth through NIJ-funded collaborations. Analysis of his recent publications reveals consistent focus on Latino victimization experiences using community samples and longitudinal designs. His research bridges criminology, clinical psychology, and trauma studies with methodological diversity spanning psychometric development, community surveys, and trauma theory applications. Scientific recognition includes: President Elect, Trauma Division (56) of the American Psychological Association He actively mentors graduate students as evidenced by co-authorship on multiple publications, with research supported by National Institute of Justice grants focusing on bias victimization assessment and teen dating violence prevention. The Violence and Justice Research Lab under his co-direction conducts interdisciplinary studies on victimization patterns, trauma responses, and justice system interactions particularly within minority communities.

David Kaplanمشاهده پروفایل

دانشیار

• Econometrics
• Quantile Regression
• Statistical Inference
• +۳ مورد دیگر

David Kaplan is an Associate Professor and Director of Doctoral Studies in the Department of Economics at the University of Missouri, within the College of Arts and Science. His research focuses on Econometrics, particularly in quantile regression, statistical inference, and policy analysis. He holds a Ph.D. (details unspecified) and has advised notable students including Qian Wu, Wei Zhao, Xin Liu, and Longhao Zhuo. His work bridges theoretical econometrics and applied policy evaluation, with a strong emphasis on methodological rigor and real-world applications. Key research themes include auction theory, ordinal data analysis, and health economics. His recent publications explore topics such as consensus ranking of distributions and the impact of robust norming in clinical classification. He has contributed to high-impact journals like the Journal of Econometrics and the Journal of Business & Economic Statistics. Dr. Kaplan’s academic leadership includes directing doctoral studies, ensuring rigorous training in econometric theory and applied methods. His work frequently involves collaborations and methodological innovations, addressing challenges in policy evaluation and statistical modeling. He maintains an active presence in academic communities through Google Scholar and institutional affiliations.