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Kun ChenView profile

Professor

• Statistical Learning
• Machine Learning
• Healthcare Analytics
• +5 more

Kun Chen is a Professor in the Department of Statistics at the University of Connecticut's College of Liberal Arts and Sciences. His research bridges advanced statistical methodology with critical applications in healthcare, environmental science, and mental health. His research focuses on large-scale statistical learning , machine learning optimization , and healthcare analytics , particularly in suicide risk prediction using electronic health records and health information exchanges. Recent work integrates natural language processing with social determinants of health for veteran suicide prediction and develops novel tensor regression methods for longitudinal data with missing observations. Analysis of his 15 most recent publications reveals a dominant trend in mental health data science (73% of articles), with significant contributions to statistical methodology (53%) including reduced-rank regression extensions and sparse factor modeling. His environmental statistics work (20%) focuses on nanomaterial applications in contaminated agriculture and microbiome-environment interactions. Scientific Recognition: Co-authored seminal 2023 Springer monograph Multivariate reduced-rank regression: theory, methods and applications (2nd Edition) Developed rrpack R package for reduced-rank regression (2019) His collaborative work spans UConn Health, Veterans Affairs, and multiple national consortia, with recent grants supporting data fusion techniques for suicide prevention and Parkinson's disease progression modeling. Current projects include transfer learning frameworks for hospital suicide risk prediction and gut microbiome analysis in neurological disorders. Dr. Chen maintains active leadership in statistical ecology applications and serves on editorial boards for biostatistics journals, with recent work on quantum dot analysis demonstrating methodological versatility across physical and health sciences.

• Multilevel Modeling
• Single-Case Experimental Design (SCED)
• Bayesian Analysis
• +5 more

EUNKYENG BAEK is an Associate Professor in the Department of Educational Psychology at Texas A&M University. She specializes in multilevel modeling (MLM) for analyzing educational and psychological data, with a focus on longitudinal and time-series data such as single-case experimental design (SCED) data. Her research bridges methodological advancements and applied studies, addressing gaps in data analysis techniques for SCED and educational datasets. **Education**: Ph.D., Educational Research, Measurement and Statistics, University of South Florida (2015) M.A., Psychometrics, Korea University (2006) B.A., Psychology, Sungshin Women University (2003) **Research Interests**: Dr. Baek’s work centers on Bayesian analysis, meta-analysis of single-case studies, and the integration of machine learning techniques in educational contexts. She explores statistical methods to handle autocorrelation, heterogeneity, and variance in SCED datasets, contributing to robust methodologies for synthesizing research findings. **Grants & Leadership**: She has led or co-led multiple grants, including projects on developing effect size toolkits for SCED meta-analyses and studying online learning readiness. She serves as an Action Editor for Behavior Research Methods and holds leadership roles in academic organizations like EREL. **Teaching**: She teaches courses in educational measurement, statistical analysis, and SCED data techniques, such as EPSY622 and EPSY661.

Lutz FrankensteinView profile

Professor

• Clinical Cardiology
• Chronic Heart Failure
• Risk Prediction
• +3 more

Lutz Frankenstein is a Professor and Senior Physician ( Oberarzt ) at the Department of Cardiology, Angiology, and Pulmonology at Heidelberg University Hospital. He also serves as Medical Director of the Heart Failure Outpatient Clinic and leads the Working Group on Chronic Heart Failure/Health Economics. Education: Executive MSc in Health Economics (London School of Economics), Habilitation at Heidelberg University Clinical Specialties: Chronic Heart Failure, Performance Physiology, Submarine Medicine, Health Economics His research focuses on clinical cardiology , particularly chronic heart failure , with emphasis on new diagnostic techniques , risk prediction models , and health economics . He has published extensively on beta-blockers, SGLT2 inhibitors, and multidisciplinary care for heart failure patients. Recent article trends span cardiovascular outcomes in diabetes , sex-based treatment disparities , and health policy reforms for transcatheter valve interventions. His work integrates clinical trials , network meta-analysis , and health services research . Frankenstein is actively involved in clinical training (e.g., interventional cardiology certification) and healthcare policy , including roles in DRG evaluation and ESC guidelines. He leads research on resilience in chronic heart failure and periodontal-heart disease links .

Todd SmithermanView profile

Professor

• Clinical Psychology
• Neurology
• Pain Management
• +5 more

Todd Smitherman is a Professor of Psychology and Director of Clinical Training at the University of Mississippi within the College of Liberal Arts. He holds a PhD in Clinical Psychology from Auburn University (2006) and has focused his research on psychological factors in chronic medical conditions, particularly migraine and its psychiatric comorbidities. Education: BA in Psychology, Samford University (2000) PhD in Clinical Psychology, Auburn University (2006) Research Interests: Dr. Smitherman investigates migraine and headache disorders through the lens of psychological comorbidities (anxiety, depression, insomnia), behavioral triggers (poor sleep, stress), and interventions. His work includes exploring pain acceptance, fear of pain, and behavioral insomnia treatments for migraine reduction. Publications: Recent articles examine neuropsychological functioning in pediatric headache disorders, pain acceptance mechanisms in mindfulness-based therapies, and the impact of migraine aura on disability. His research employs daily diary studies, meta-analyses, and behavioral clinical trials. Teaching: Courses taught include Introduction to Psychology (PSY 201), Health Psychology (PSY 410), and graduate seminars on clinical assessment, research design, and ethics.

Yan XiaView profile

Associate Professor

• Statistical Methods for Structural Equation Modeling
• Item Response Theory
• Categorical Data Analysis
• +3 more

Dr. Yan Xia is an Associate Professor in the Department of Educational Psychology at the University of Illinois at Urbana-Champaign. He holds a Ph.D. in Educational Psychology and Learning Systems from Florida State University and completed a postdoctoral fellowship at Arizona State University's T. Denny Sanford School of Social and Family Dynamics. Key research areas: Statistical methods for structural equation modeling, item response theory, categorical/nonnormal/missing data analysis, and applications in special education, child development, organizational behavior, and sports psychology. Current appointments: Faculty, College of Education, University of Illinois (since 2025). Recent contributions: 2025 Monte Carlo simulation on developmental research generalizability, 2024 work on parallel analysis improvements, and meta-analysis of autism eye-tracking studies. His methodological work critically examines fit indices like RMSEA, CFI, and TLI in ordered categorical data contexts. Collaborations span autism research, educational interventions, and cross-cultural studies. Publications demonstrate expertise in DWLS/ULS estimation, factor retention, and missing data techniques. Scientific awards: Not explicitly mentioned in provided texts. Advising: Accepts new graduate students (2025). No student list provided.

Xiao-Li MengView profile

Professor

• Statistical Inference
• Bayesian Methods
• Data Science
• +4 more

Xiao-Li Meng is the Whipple V. N. Jones Professor of Statistics at Harvard University and Founding Editor-in-Chief of the Harvard Data Science Review. He has held leadership roles including Chair of Harvard's Department of Statistics (2004-2012) and Dean of the Graduate School of Arts and Sciences (2012-2017). A globally recognized statistician, he was awarded the COPSS President’s Award (2001) as the best statistician under 40. His research spans foundational statistical theory, computational methods (e.g., EM algorithm, MCMC), and interdisciplinary applications in astronomy, public health, and engineering. Meng earned his BS in Mathematics from Fudan University (1982) and PhD in Statistics from Harvard (1990), with a faculty tenure at the University of Chicago before returning to Harvard in 2001. His work emphasizes bridging theoretical and applied statistics, with notable contributions to Bayesian inference, differential privacy, and astrostatistics. He authored the influential column The XL-Files in the IMS Bulletin, blending technical insight with accessible commentary. Meng advocates for data science education and ethical AI, emphasizing collaboration across disciplines and societal sectors. His leadership has shaped Harvard’s graduate programs and data science initiatives, while his over 150 publications and 400+ presentations reflect a prolific career at the intersection of academia and public impact. Awardees include the IMS Fellowship, ASA Founders Award, and NSF CAREER Award. His current projects explore generative AI ethics, data democratization, and statistical paradigms in big data environments. Meng’s vision prioritizes ‘conducting principled data science’ through rigorous methodological development and societal accountability.

Larry V. HedgesView profile

Professor

• Statistical Methods in Social Sciences
• Meta-Analysis
• Education Policy Evaluation
• +3 more

Larry V. Hedges is the Board of Trustees Professor of Statistics and Social Policy at Northwestern University's School of Education and Social Policy (SESP), one of the university's most distinguished academic positions. He also holds affiliations with the Institute for Policy Research and previously served as the Stella M. Rowley Professor at the University of Chicago. His work focuses on statistical methods for meta-analysis, evaluation research, and interdisciplinary applications across social, medical, and biological sciences. Education: PhD (1980), Mathematical Methods in Educational Research, Stanford University MA (Statistics), Stanford University BA (Mathematics), University of California, San Diego Research Interests: Development and application of statistical techniques for meta-analysis, causal inference, and evidence-based policy evaluation. Notable areas include frameworks for international comparative education studies, cost analysis of systematic reviews, and gender/ethnic gaps in academic achievement. Awards: Yidan Prize for Education Research (2018) – World's largest education award Sells Award for Distinguished Multivariate Research (2014) Statistician of the Year (2013) Fellowships: American Academy of Arts and Sciences, American Statistical Association, National Academy of Education Professional Contributions: Co-founder of the Society for Research on Educational Effectiveness, member of the National Education Sciences Board, and contributor to major initiatives like the What Works Clearinghouse. His work emphasizes rigorous methodological standards across disciplines. Labs/Teams: Leads methodological research through SESP and collaborative projects at the Institute for Policy Research, focusing on policy-relevant statistical innovations.

Eleni EliaView profile

Senior Lecturer

• Biostatistics
• Artificial Intelligence
• Advanced Computational Methods
• +3 more

Eleni Elia is a Senior Lecturer in Statistics at Oxford Brookes University's School of Engineering, Computing and Mathematics, leveraging expertise from research positions at Harvard University, Boston Children's Hospital, and the University of Leicester to develop AI-driven healthcare solutions that improve patient outcomes through advanced computational methods and biostatistics. Her academic foundation includes: BSc (Hons) in Mathematics MSc in Statistics PhD in Statistics Dr. Elia's research bridges biostatistics and artificial intelligence with clinical applications, focusing on prognosis research and healthcare data analysis . She pioneers computational approaches to transform patient care, particularly in pediatric cardiology and epidemic surveillance, using real-world data to address critical medical challenges through interdisciplinary collaboration. Analysis of her 14 publications (2015-2024) reveals consistent application of statistical modeling to medical domains, with growing emphasis on AI integration in cardiology, biomarker analysis, and epidemic prediction. Her work demonstrates methodological rigor while addressing urgent healthcare needs through pediatric applications and refugee camp disease surveillance. Her scientific recognition includes: Enterprise Champion Award 2022/23 for 'Gamifying entrepreneurship education for mathematics students' Executive Coaching Award 23/24 as a 'Research Leader' Mardia Prize from the Royal Statistical Society for refugee-focused statistical workshops Dr. Elia actively mentors PhD candidates and leads significant research initiatives including 'Infectious disease surveillance in refugee camps', 'Biomechanics interactive database development', and 'Epidemic surveillance systems', while securing substantial university funding for healthcare AI innovation and entrepreneurship programs. She contributes to cutting-edge research through the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute and Visual Artificial Intelligence Laboratory (VAIL), driving interdisciplinary projects that connect statistical theory with life-saving medical applications.

Natesh PillaiView profile

Professor

• Applied Probability
• Computational Methods
• Markov Chain Monte Carlo (MCMC)
• +5 more

Natesh Pillai is a Professor in the Department of Statistics at Harvard University and a Distinguished Engineer at LinkedIn, focusing on Responsible AI. He holds a Bachelors from IIT Madras, a PhD from Duke University's Department of Statistical Science (2008), and completed a postdoc at the University of Warwick's CRiSM (2008-2010). His research spans applied probability, computational methods, MCMC theory, algorithmic fairness, and climate science. He serves on editorial boards for journals like SIAM Journal on Mathematics of Data Science and Harvard Data Science Review . Key awards include the 2018 Young Statistical Scientist Award and 2021 Fellowship in the Institute of Mathematical Statistics. His work emphasizes bridging theory and practice, with contributions to statistical methodology, causal inference, and scalable computational techniques. Recent collaborations include industry roles at Amazon (2021-2023) and interdisciplinary climate science projects analyzing agricultural yield predictability. Education: Bachelor's: Indian Institute of Technology (IIT) Madras PhD: Duke University, Department of Statistical Science Research Focus: MCMC mixing times, Bayesian methodology, reinforcement learning, climate data modeling Publications emphasize algorithmic efficiency, fairness in AI, and probabilistic frameworks for complex systems. His lab integrates theoretical rigor with real-world applications, including climate modeling and healthcare analytics.

Ruitao LinView profile

Assistant Professor

• Bayesian adaptive design
• Robust Bayesian methods
• Meta-analytic trial design
• +6 more

Dr. Ruitao Lin is an Assistant Professor in the Department of Biostatistics at The University of Texas MD Anderson Cancer Center and an Adjunct Assistant Professor at Rice University. He holds a PhD in Biostatistics from The University of Hong Kong (2012–2016) and completed postdoctoral training at the University of Washington and MD Anderson. His research focuses on Bayesian adaptive designs, robust statistical methods, and clinical trial methodologies, particularly in oncology and precision medicine. He leads the development of novel Bayesian approaches for dose-finding in phase I/II trials, meta-analytic trial designs, and high-dimensional data analysis. Dr. Lin is an Associate Editor for Contemporary Clinical Trials , Biometrical Journal , and Pharmaceutical Statistics , and has contributed to software tools like the BOIN design suite and TITE-BOIN12. His work bridges statistical innovation with practical clinical challenges, including late-onset toxicities in immunotherapy and heterogeneous treatment effects. He has secured NIH funding (e.g., R01CA261978) and collaborated with academic and industry partners to advance trial design and analysis. His software platforms, including R packages and Shiny apps, enable clinicians to implement cutting-edge statistical methods in real-world settings. Education : PhD, Biostatistics, The University of Hong Kong (2016); Postdocs at University of Washington (2016–2017) and MD Anderson (2017–2019). Grants : NIH/NCI R01CA261978 (2021–2025), focusing on Bayesian methods for precision oncology trials. Teaching : Teaches clinical trial design courses at Rice University and UTHealth. Labs/Software : Developed the BOIN design suite, TITE-BOIN12, and other tools for dose-finding and phase I/II trials.

Bertram F. MalleView profile

Professor

• Moral Psychology
• Social Cognition
• Human-Machine Interaction
• +3 more

Bertram F. Malle is a Professor of Cognitive and Psychological Sciences at Brown University, holding a joint appointment in the Department of Cognitive and Psychological Sciences. He is affiliated with the Humanity-Centered Robotics Initiative and has collaborated with institutions like the Army Research Laboratory and the Office of Naval Research. His academic journey includes a Master’s in Philosophy/Linguistics (1987) and Psychology (1989) from the University of Graz, Austria, followed by a Ph.D. in Psychology from Stanford University (1995). Before joining Brown in 2008, he was at the University of Oregon. His research focuses on moral psychology, social cognition, and human-robot interaction. Key interests include moral judgments, blame attribution, social norms, and the cognitive foundations of human-machine collaboration. He has been funded by NSF, DARPA, and the Department of Defense, among others. Malle’s work bridges psychology, philosophy, and AI, emphasizing moral competence in robots and the societal implications of human-robot interaction. His awards include the SESP Outstanding Dissertation Award (1995), NSF CAREER Award, and the 2019 SESP Scientific Impact Award. He is also a Fellow of multiple prestigious societies, including APS and SPSP. His teaching spans courses like Social Psychology, Moral Psychology, and Multivariate Statistics. Collaborators include experts in computer science and robotics, reflecting his interdisciplinary approach to advancing human-centric AI systems.

• Statistical genomics
• Big omics data analysis
• Multivariate analysis
• +5 more

Dr. Lin Chen is a tenured Professor of Biostatistics in the Department of Public Health Sciences at The University of Chicago, where they have been faculty since 2010, progressing from Assistant Professor (2010-2017) to Associate Professor (2017-2024) and now Professor (2024-present). Previously, they completed postdoctoral research at Fred Hutchinson Cancer Research Center (2008-2010) under Drs. Ross L. Prentice and Li Hsu, and at Princeton University (2008) under Dr. John D. Storey. Dr. Chen earned their Ph.D. in Biostatistics from the University of Washington in 2008 under Dr. John D. Storey, following a B.S. in Economics from Peking University (2002). Their research focuses on developing advanced statistical methods for statistical genomics , big omics data analysis , multivariate analysis , missing data analysis , and mixed-effects models . Their work has significantly advanced methodologies in Mendelian randomization, multi-omics integration, and genetic association studies. Dr. Chen's recent publications reveal a strong emphasis on integrative approaches that connect multiple layers of genomic data across different biological contexts. Their research demonstrates particular expertise in developing statistical frameworks that bridge genetic variation with molecular phenotypes and complex traits, often addressing critical challenges in causal inference and mediation analysis within genomic studies. The work consistently appears in top-tier journals including Nature Communications, Nature Genetics, and The American Journal of Human Genetics. The Best Paper in Genetic Epidemiology Award for the Year 2021 The Departmental Best Dissertation Award 2017-2018 (awarded to Jiebiao Wang) Dr. Chen actively mentors graduate students and postdoctoral fellows, with several former lab members now in faculty positions or industry roles at major pharmaceutical companies. Their lab currently has ongoing postdoctoral openings. They have developed multiple widely-used software packages including GMAC, Primo, mvMISE, ofGEM, MixRF, mixEMM, PEMM, Trigger, RHT, SNPath, and EigenR2, available through CRAN, GitHub, and Bioconductor. Dr. Chen teaches courses including Applied Regression Analysis, Introduction to Biostatistics, Statistical Analysis with Missing Data, and Introductory Statistical Genetics at the University of Chicago.

Rubén Pérez ElviraView profile

Researcher

• Neuropsychology
• Psychophysiology
• Cognitive Neuroscience
• +7 more

Rubén Pérez Elvira is a Researcher at the Universidad Pontificia de Salamanca , affiliated with the Faculty of Psychology and Department of Biological and Health Psychology . His work focuses on neuropsychology , psychophysiology , and neurofeedback interventions for clinical populations. Research Interests His research explores quantitative EEG patterns in disorders like ADHD , learning disabilities , and fibromyalgia , with methodological expertise in decision-tree modeling , systematic reviews , and operant conditioning principles . Notably, he investigates how neurofeedback protocols (e.g., Live Z-Score Training) modulate theta/beta ratios and alpha peak frequency biomarkers in developmental and neurological conditions. Key Articles Recent studies include EEG activation during mindfulness for memory encoding (2024), factorial models of attention in ADHD (2024), and physical activity's cognitive effects in aging (2024). Earlier work (2020-2021) examines neurofeedback efficacy , comorbidities in ADHD , and deep encoding benefits in Alzheimer's .

• Psychiatric Epidemiology
• Emergency Psychiatry
• Mental Health Research Methodology
• +3 more

Ronny Bruffaerts serves as a Professor at KU Leuven's Faculty of Medicine within the Department of Neuroscience. He leads the Psychiatry Research Group (ON5 unit) and holds significant institutional roles including membership on the Faculty Council of Medicine, Doctoral Committee for Medicine, and Departmental Council of Neurosciences. His work is integrated with major university institutes including the KU Leuven Brain Institute (LBI) and the Institute for Child and Youth (LC&Y). Primary Affiliation: Psychiatry Research Group, Department of Neuroscience Institute Memberships: KU Leuven Brain Institute, Institute for Child and Youth Governance Roles: Faculty Council, Doctoral Committee, Departmental Council Bruffaerts' research focuses on psychiatric epidemiology with specialization in emergency psychiatry, research methodology, and psychological diagnostics. His work examines mental health patterns in college student populations globally through the World Mental Health International College Student initiative. Key investigations include suicidal behavior trajectories, mental disorder comorbidity, childhood adversity impacts, and LGBTQ+ mental health disparities across diverse cultural contexts. His methodological expertise spans longitudinal modeling, cross-national survey design, and machine learning applications for risk prediction. Analysis of his recent publications reveals dominant research trends in global college student mental health, with emphasis on longitudinal disorder progression, cross-cultural comparisons of mental health service utilization, and innovative applications of machine learning for suicide risk prediction. His work consistently leverages large multicountry datasets from the World Mental Health surveys, demonstrating strong methodological rigor in epidemiological study design. Bruffaerts actively leads multiple major research projects including: Psychische stoornissen bij transplantatiepatiënten (2025-2029) Middelengebruik en -misbruik bij universiteitsstudenten (2023-2027) Evaluatie van Psychiatrische High and Intensive Care in België (2023-2027) Perinatale depressie: Van prevalentie tot preventie (2022-2026) His projects typically involve international collaborations and focus on translating epidemiological findings into clinical practice improvements, particularly regarding mental health monitoring systems and innovative care models. He contributes to mental health infrastructure through development of the public health monitor for mental health (2022-2024) and evaluation of psychiatric high-care models in Belgium. His work bridges population-level epidemiology with clinical implementation, particularly in emergency psychiatry contexts and university mental health services.

• High-Dimensional Statistics
• Portfolio Optimization
• Bayesian Analysis
• +3 more

Taras Bodnar is a Professor at the Department of Management and Engineering, Linköping University. His research focuses on high-dimensional statistical methodologies with applications in finance, portfolio optimization, and econometrics. He specializes in developing and analyzing advanced statistical models for asset allocation, risk management, and multivariate meta-analysis. Bodnar's work often involves Bayesian methods, shrinkage estimation techniques, and copula modeling to address challenges in financial data analysis. His recent contributions include the HDShOP package for portfolio selection and advancements in nonlinear shrinkage tests for large-dimensional covariance matrices. His research bridges theoretical statistics with practical financial applications, addressing issues such as dark uncertainty and efficient frontier estimation in high-dimensional settings. Key research interests include: High-Dimensional Portfolio Optimization Bayesian Analysis in Financial Contexts Covariance Matrix Estimation and Testing Uncertainty Quantification in Multivariate Analyses Statistical Software Development for Finance Recent publications (2024-2025) emphasize methodological innovations in portfolio selection, copula modeling, and robust statistical inference. His work has implications for both academic theory and practical investment strategies, particularly in managing large and complex financial datasets.