Keunhyun (Keun) Park is an Assistant Professor of Urban Forestry at the University of British Columbia (UBC), affiliated with the Department of Forest Resources Management . He also holds an Adjunct Professor position at Utah State University in the Department of Landscape Architecture and Environmental Planning. Education: BSc and MSc in Landscape Architecture from Seoul National University; PhD in Urban Planning and Design from the University of Utah Research Lab: Faculty lead of the Urban Nature Design Research Lab ( under_lab ) His research focuses on designing healthy, just, and resilient cities through urban nature , with particular emphasis on: Environmental justice and equitable access to urban green spaces Human behavior in public spaces using drone/sensor/VR technology Smart growth urban design impacts on public health and ecological systems Recent publications demonstrate expertise in GIS applications , pedestrian behavior analysis , and urban planning across 20+ studies from 2013-2025. Collaborations include the Vancouver Park Board , Metro Vancouver , and Wasatch Front Regional Council .
Valentijn M.T. de Jong is an Assistant Professor at Utrecht University, specializing in methodological advancements in biostatistics and epidemiology. His research focuses on causal inference, missing data analysis, and meta-analytical techniques in medical studies. Research Trends: Recent publications highlight his expertise in statistical methods for handling missing data (e.g., Heckman selection models), causal inference in individual-participant data meta-analyses, and enhancing prediction model discrimination in healthcare research. His work spans disciplines like epidemiology, biostatistics, and health data science.
Bryan S. Graham is a Professor of Economics at the University of California, Berkeley. He specializes in econometrics, focusing on network formation, social interactions, and panel data analysis. His research explores topics such as peer effects, poverty traps, and small sample properties of econometric methods. Graham holds a Ph.D. from Harvard University (2005) and has held visiting positions at Harvard, CEMFI (Spain), and NYU. He is an elected Fellow of the International Association of Applied Econometrics. Education highlights include a Rhodes Scholarship (1997–2000) at Oxford University, a Fulbright Scholarship (1997–1998) at the Australian National University, and a B.A. in Quantitative Economics from Tufts University (1993–1997). His work has been published in top journals like Econometrica and the Review of Economic Studies . Key awards include NSF grants (multiple), the Review of Economics Studies Tour, and the Daniel Ounjian Prize. Graham’s research has practical applications in policy analysis, particularly in education and social spillover effects. He also actively contributes to academic service, including editorial roles at Review of Economics and Statistics and Journal of Econometrics .
George Hripcsak is the Vivian Beaumont Allen Professor of Biomedical Informatics and Director of Medical Informatics Services at New York-Presbyterian Hospital, Columbia University. He holds affiliations with the Vagelos College of Physicians and Surgeons and the Data Science Institute (DSI). His expertise spans clinical informatics, electronic health records (EHRs), and medical knowledge representation standards. Hripcsak earned degrees in chemistry, medicine, and biostatistics, and is a board-certified internist. Research focuses on leveraging EHR data for clinical research and patient safety through data mining and causal inference techniques. Notable contributions include the Arden Syntax (a national standard for medical knowledge representation) and leadership in the Observational Health Data Sciences and Informatics (OHDSI) network. He chairs the AMIA Standards Committee and has advised federal health informatics policies under HIPAA. His academic awards include Fellowships in the American College of Medical Informatics (1995) and New York Academy of Medicine. Current projects emphasize federated learning, genomic risk prediction, and large-scale real-world evidence analysis through initiatives like LEGEND-T2DM and All of Us Research Program. Educations: MD, Biostatistics, Chemistry Labs/Teams: OHDSI, DSI, Medical Informatics Services Grants & Funding: Not explicitly listed in provided texts
Nicola Ballhausen is an Assistant Professor in the Department of Developmental Psychology at the Tilburg School of Social and Behavioral Sciences, Tilburg University, Netherlands. Her research focuses on cognitive and psychological aspects of aging, particularly prospective memory, executive function, and social influences on cognitive health in older adults. Her research interests include prospective memory , cognitive aging , metacognition , problem-solving in older adults , social cognition , and longitudinal studies of aging . She investigates how factors such as intergenerational contact, sense of purpose, and technology use impact cognitive performance and well-being in later life. Her work integrates experimental, survey-based, and qualitative methods, often in cross-national datasets like the Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA). The most recent articles highlight a strong focus on prospective memory across the lifespan , the role of social engagement in cognitive functioning , and designing accessible cognitive interventions for older adults. Her publications span top journals in gerontology, psychology, and cognitive science, reflecting interdisciplinary collaboration and methodological rigor. Dr. Ballhausen contributes to research aligned with the UN Sustainable Development Goals, particularly those related to healthy aging and well-being. She collaborates extensively with researchers across Europe and has contributed to major reference works such as The Oxford Handbook of Human Memory . Her work includes both empirical studies and methodological advancements, such as exploratory structural equation modeling. While no formal students or awards are listed, her role in supervising research and contributing to academic training is implied through her position and course involvement. Dr. Ballhausen is involved in the design and evaluation of web-based cognitive tools, such as the Shared, Web-based, Intelligent Flexible Thinking Training (SWIFT), emphasizing user-centered design and ecological validity. Her work bridges fundamental cognitive research with practical applications for aging populations.
Jeffrey Smith holds the Paul T. Heyne Distinguished Chair in Economics and Richard Meese Chair in Applied Econometrics at the University of Wisconsin-Madison. Previously, he served as Professor of Economics and Public Policy at the University of Michigan, with prior faculty positions at the University of Western Ontario (1994-2001) and University of Maryland (2001-2005). Education includes: B.A. Economics & B.S. Computer Science, University of Washington (1985) M.A. Economics, University of Chicago (1987) Ph.D. Economics, University of Chicago (1996) Research focuses on experimental and non-experimental methods for evaluating social and educational interventions, labor market impacts of university quality, and statistical treatment rules for government programs. Primary domains include Labor Economics, Public Economics, Econometrics, and Program Evaluation, with emphasis on causal inference methodologies and policy applications. Publications demonstrate consistent focus on econometric innovations in program evaluation, particularly quasi-experimental designs, treatment effect heterogeneity, and labor policy efficacy. Recent work examines conditional cash transfers, matching estimators, and behavioral incentives in welfare programs. Awards: Royal Economic Society Prize (1999) for contributions to evaluation methodology Consulting engagements include governments of the United States, Canada, United Kingdom, and Australia on evaluation frameworks and policy design.
Michele Jonsson-Funk, PhD, is an Associate Professor in the Department of Epidemiology at the UNC Gillings School of Global Public Health and Director of the Center for Pharmacoepidemiology. She leads the Pharmacoepidemiology Program within the Department and serves as an Affiliate Faculty member at the Injury Prevention Research Center. Dr. Jonsson-Funk is also a Fellow of the International Society for Pharmacoepidemiology and holds leadership roles in several professional organizations, including the Board of the International Society for Pharmacoepidemiology and advisory roles in epidemiology and clinical research networks. Dr. Jonsson-Funk holds a PhD in Epidemiology from the University of North Carolina at Chapel Hill (2003), a Master of Science in Public Health (MSPH) in Epidemiology from UNC Chapel Hill (2000), and a BA in Psychology from Reed College (1994). Her research focuses on pharmacoepidemiology, methods for estimating treatment effects in observational data, and women’s health. She examines topics such as sex differences in medication safety and efficacy, maternal and child health outcomes, and the application of electronic health records for population-level analyses. Her work integrates data from Medicare, Marketscan, and the Carolina Data Warehouse for Health to evaluate treatment effectiveness and safety. Dr. Jonsson-Funk has received several honors, including: Fellow, International Society for Pharmacoepidemiology (2018–present) Research Fellow, Sheps Center for Health Services Research, UNC Chapel Hill (2005–present) Member, Delta Omega Honorary Public Health Society, Theta Chapter (2004–present) Her research portfolio includes grants such as an R01 evaluating treatment effect heterogeneity in cardioprotective medications. She advises on interdisciplinary teams through her leadership roles in the Building Interdisciplinary Research Careers in Women’s Health (BIRCWH) Program and the Observational Pharmaco-Epidemiology Research & Analysis (OPERA) network. She also leads the Pharmacoepidemiology Research Lab Network under BIRCWH. Dr. Jonsson-Funk collaborates with clinical colleagues on studies addressing pelvic floor disorders, medication use during pregnancy, and diabetes-related cancer risks. Her methodological contributions include improving propensity score techniques and addressing confounding by frailty in observational studies.
Yves Rosseel is a Professor at Ghent University (UGent) specializing in Structural Equation Modeling (SEM) , Psychometrics , and Statistical Methodology . With over 15 recent publications (2024-2025), he focuses on small-sample SEM solutions, factor score regression, measurement error, and Bayesian extensions. His work bridges Statistics with applications in Psychology , Education , and Neuroimaging . Research Trends Developed Mixture Multigroup SEM for cross-group comparisons Proposed Information-Theoretic Hypergraphs in psychometrics Advanced Two-Stage Estimation for round-robin data Created blavaan R package for Bayesian SEM Investigated Measurement Error in hypothesis testing Scientific Contributions Published 84 Social Sciences papers, 37 Statistics works, and 11 Neuroimaging studies Promoted 8 PhDs including Sara Dhaene and Julie De Jonckere Co-authored 12+ works with Marijke Welvaert and 10+ with Stijn Vanheule
Elizabeth Richey serves as a Teaching Assistant Professor in the Department of Psychology at the University of Pittsburgh's Dietrich School, where she teaches undergraduate courses including Introduction to Psychology, Cognitive Psychology, Research Methods Lab, and specialized topics in Learning and Motivation. Holding a PhD in Cognitive Psychology from the same institution, she bridges theoretical knowledge with practical educational applications through her dual roles as educator and learning sciences researcher. Education PhD in Cognitive Psychology, University of Pittsburgh Research Focus Dr. Richey's research program investigates how cognitive, metacognitive, and motivational factors influence mathematics and science learning across educational stages from middle school to college. Her work emphasizes educational technology applications, particularly digital learning games and intelligent tutoring systems, with concentrated examination of gender dynamics, self-explanation mechanisms, and belonging interventions. Through controlled laboratory experiments and classroom-based studies, she explores how technological implementations can enhance learning equity and engagement while mitigating factors like math anxiety. Publication Trends Analysis of her 2022-2025 publications reveals a cohesive research trajectory centered on game-based mathematics education, with 80% of recent work addressing gender effects in digital learning environments. Key methodological approaches include randomized controlled trials examining mindfulness interventions, latent variable modeling of student behavior, and multi-dimensional gender frameworks that move beyond binary classifications. Her findings consistently demonstrate how contextual factors—such as narrative design in learning games and AI-human collaboration models—significantly impact learning outcomes, particularly for female students in mathematics contexts.
Yi Li is the M. Anthony Schork Collegiate Professor of Biostatistics at the University of Michigan School of Public Health. With a PhD in Biostatistics from the University of Michigan (1999) and postdoctoral training at Harvard (1999-2000), Dr. Li has established himself as a leading researcher in statistical methodology with applications across multiple biomedical domains. Dr. Li's research spans survival analysis, data science, high-dimensional inference, machine learning, deep learning, spatial data analysis, random-effects models, clinical trial design, and infectious disease modeling. His methodological work finds application in cancer genetics/genomics, radiomics, racial disparity analysis, chronic disease research, and opioid overuse studies. With over 230 publications in major statistical journals including JASA, Biometrika, JRSSB, and Biometrics, as well as premier subject matter journals like PNAS, JAMA, and JCO, Dr. Li's work has significantly impacted both statistical theory and biomedical applications. His research portfolio demonstrates consistent evolution from foundational methodological work in survival analysis and spatial statistics to cutting-edge applications in high-dimensional data, machine learning, and deep learning approaches for complex biomedical problems. The recent publications reveal increasing focus on integrating multiple data sources, causal inference in observational studies, and developing interpretable machine learning models for clinical applications. Dr. Li's work has been continuously supported by NIH funding since 2003, including multiple National Cancer Institute grants (R01 CA95747, 1P01CA134294-010002, R21CA157219, R01CA249096, R01CA269398) and a National Institute on Aging grant (R21AG058198). He actively collaborates with researchers from the University of Michigan and Harvard University on clinical and observational studies. As an educator, Dr. Li has taught advanced courses in survival analysis and statistical methods, mentoring the next generation of biostatisticians. His methodological contributions have been widely recognized through invitations to serve on NIH study sections (BMRD 2008-2012, EPIC 2015-2019) and as Associate Editor for leading statistical journals including Journal of the American Statistical Association, Biometrics, and Scandinavian Journal of Statistics.
Richard Nielsen is an Associate Professor in the Department of Political Science at MIT, affiliated with the Institute for Data, Systems, and Society (IDSS), the Security Studies Program (SSP), and the Center for International Studies (CIS). His research integrates quantitative methods with ethnographic insights to study Middle East politics, religion, political violence, and gender dynamics. He holds a PhD in Government and AM in Statistics from Harvard University, and a BA in Political Science from Brigham Young University. His first book, *Deadly Clerics* (2017), examines clerical radicalization in Sunni Islam, while current work explores female religious authority in digital spaces. Education: PhD in Government (Harvard, 2013), AM in Statistics (Harvard, 2010), BA in Political Science (BYU, 2007). Research focuses on: Islamic authority dynamics, online religious preaching (especially by women), counterterrorism, and methodological innovations in text analysis. He develops tools for Arabic text analysis and advises on computational social science methodologies. His work bridges political science, computer science, and Islamic studies. Teaching includes courses on international relations, political methodology, and Middle East politics. He has mentored over 20 PhD students, many now in academic and policy roles. Grants and collaborations include Carnegie Fellowship research and MIT's interdisciplinary initiatives. Labs/Teams: Political Methodology Lab (MIT), affiliated with IDSS and SSP. His work emphasizes computational tools for social science, such as the *arabicStemR* package for text analysis.
Dr. Ram Bajpai is a Lecturer in Epidemiology/Applied Statistics at Keele University's School of Medicine. He joined in 2019 as part of the Research Institute for Primary Care and Health Sciences, combining active research and teaching roles. Previously, he worked at the Lee Kong Chian School of Medicine (Nanyang Technological University, Singapore) and the Army College of Medical Sciences (India). Education: BSc in Statistics/Mathematics (University of Lucknow), MSc Health Statistics (Banaras Hindu University), PhD in Medical Statistics (Guru Gobind Singh Indraprastha University). Research focuses on cross-domain applications of statistical/epidemiological methods, including survival analysis, Bayesian methods, risk prediction modelling, and evidence synthesis. Teaching experience includes biostatistics modules for medical students at multiple institutions. Current research interests span prognostic studies, meta-analysis, complex data analysis, and design of epidemiological studies. Key contributions include systematic reviews on gout prophylaxis safety, dementia prognostic factors, and long-term outcomes of pediatric COVID-19. Active in collaborative projects on aging populations, musculoskeletal health, and public health interventions.
Professor Rajen Shah is a faculty member in the Statistical Laboratory at the University of Cambridge, part of the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics. His research focuses on statistical methodology, particularly in high-dimensional data analysis, machine learning, and robust statistical inference. He is known for contributions to areas such as change-point regression, inverse propensity score weighting, and efficient estimation techniques in complex models. Key research interests include developing novel methods for variable selection, robust hypothesis testing, and scalable algorithms for large-scale data. He has collaborated on interdisciplinary projects, such as functional genomics studies (e.g., screening conserved genes of unknown function). His work often emphasizes theoretical rigor alongside practical applications in fields like causal inference and computational statistics. Prof. Shah has published extensively in top-tier journals like The Annals of Statistics , Bernoulli , and Journal of the Royal Statistical Society Series B . His recent articles address challenges in cross-validation for change-point detection, rank-transformed subsampling, and sandwich boosting methods. He is actively involved in the academic community, contributing to the Cambridge Statistics Clinic and supervising research in statistical methodology. His research group is affiliated with the Statistical Laboratory at the Centre for Mathematical Sciences, Cambridge. The lab focuses on advancing statistical theory and applications, with a strong emphasis on high-dimensional and assumption-lean methods.
Laura M. Stapleton is Chair of the Department of Human Development and Quantitative Methodology and a Professor at the University of Maryland’s College of Education. She previously served as Interim Dean and Associate Dean for Research, Innovation, and Partnerships. Her academic roles include leadership in the NSF-funded Quantitative Research Methods Scholars Program (2019–2025) and membership on Maryland’s Accountability and Implementation Board for education reform. Education Background: Ph.D. in Measurement, Statistics, and Evaluation from the University of Maryland. Prior to academia, she worked as an economist at the Bureau of Labor Statistics and in educational research roles at the American Association of State Colleges and Universities and the University of Maryland’s institutional research department. Research focuses on complex survey data analysis, multilevel latent variable models, and mediation testing. Key interests include administrative data utilization, STEM education equity, and policy evaluation. Her work emphasizes bridging statistical rigor with practical educational challenges. Publications span methodological advancements in multilevel modeling, synthetic data strategies, and the integration of administrative datasets. Recent work addresses school-based prevention study attrition and the design effects of multilevel samples. Awards include AERA Fellowship (2023), election as President of the Society for Multivariate Experimental Psychology (2025), and recognition for mentoring and teaching excellence. She has led over $10M in grants, including NSF-funded initiatives to train early-career STEM equity researchers. Current projects include the BCSER Quantitative Research Methods Program (2022–2025) and collaborations on arts education impacts and intergroup relationship interventions. She advises on state-level longitudinal data systems and chairs the Maryland State Longitudinal Data System Center’s Research Branch (2013–2018).
Ti John is a Research Fellow at Aalto University's Department of Computer Science within the School of Science. He is affiliated with Professor Marttinen's research group and the Probabilistic Machine Learning group led by Professor Samuel Kaski. His work connects with the Finnish Center for Artificial Intelligence (FCAI) and the Helsinki Institute for Information Technology (HIIT). Dr. John's research focuses on machine learning, particularly Bayesian optimization, Gaussian processes, and point process models. His work spans theoretical developments in neural processes and practical applications in healthcare analytics and large language models. He has made significant contributions to equivariant neural processes, causal mediation analysis in healthcare, and interpretability of additive models. His publication record shows consistent output with 17 publications between 2021-2024, including multiple papers at top AI conferences like NeurIPS, ICML, and ICLR. His research demonstrates strong interdisciplinary connections between statistical modeling, artificial intelligence, and healthcare applications. Active reviewer for NeurIPS, ICLR, AISTATS Reviewer for Journal of Machine Learning Research Member of Finnish Center for Artificial Intelligence project Dr. John has been actively contributing to the machine learning community through peer review and conference participation, demonstrating expertise across multiple subfields of artificial intelligence and statistical modeling.
