Find Academic Supervisors

Rolf LeferingView profile

Professor

• Clinical Research
• Trauma Surgery
• Outcome Evaluation
• +7 more

Rolf Lefering is a Professor for Health Services Research in Surgical Medicine at the University of Witten/Herdecke. He serves as the director of the Institute for Research in Operative Medicine (IFOM) at the Faculty of Health, Department of Human Medicine. His work focuses on clinical research, outcome evaluation, scoring systems, and quality assurance in trauma surgery and intensive care medicine. Education: Doctorate in Medicine (University of Cologne, 1996), Habilitation in trauma scoring systems, Diploma in Mathematics (University of Münster, 1989) Research Interests: Development and validation of scoring systems (e.g., RISC score), quality assurance in trauma and intensive care medicine, economic analyses in healthcare, classification tree methods (CART), non-randomized treatment comparisons using propensity scores, meta-analyses, and quality of life assessment tools like the POLO chart for trauma patients. Professional Contributions: Key involvement in the DFG-funded Trauma Registry of the German Society for Trauma Surgery (NE 385/5-1 to 5-4), leadership in the Office of the Interdisciplinary Working Group on Quality Management in Intensive Care Medicine (DIVI), and active participation in the Cochrane Collaboration's Review Group on Appendectomy. Scientific Affiliations: German Society for Medical Informatics, Biometrics and Epidemiology (GMDS); Section Surgical Research (German Society for Surgery); Society for Medical Education (GMA); Society for Complexity in Acute Illness (SCAI)

• Causal Inference
• Bayesian Statistics
• Political Methodology
• +4 more

Thomas Leavitt is an Assistant Professor in the Department of Public Affairs at Baruch College's Marxe School of Public and International Affairs. His research bridges causal inference, Bayesian statistics, and design-based methodologies, with applications to racial/ethnic politics and comparative policy analysis in the U.S. and South Africa. Ph.D., Political Science, Columbia University M.Phil., Political Science, Columbia University M.A., Political Science, Columbia University M.A., Committee on International Relations, University of Chicago B.A., Political Science, DePauw University His work focuses on developing robust statistical frameworks for causal analysis, including averaged prediction models for pre-post policy evaluation, sensitivity analysis for racial disparity studies, and randomization-based Bayesian inference. He investigates methodological challenges in audit experiments, observational studies, and proprietary data's impact on meta-analysis. Recent publications include advancements in difference-in-differences methodology, causal mediation analysis, and algorithmic auditing techniques. His research integrates design-based rigor with Bayesian flexibility to address counterfactual assumptions and model uncertainty. Leavitt leads courses in advanced quantitative methods, causal analysis, and data-driven policy evaluation at both graduate and undergraduate levels, emphasizing practical applications for public service.

Roderick Joseph LittleView profile

Professor

• Biostatistics
• Missing Data Analysis
• Survey Sampling
• +3 more

Roderick Joseph Little is the Richard D. Remington Distinguished University Professor of Biostatistics at the University of Michigan School of Public Health. He is also a Professor in the Department of Statistics and a Research Professor at the Institute for Social Research. Dr. Little previously chaired the Biostatistics Department from January 2007 to December 2009 and from 1993 to 2001. Dr. Little earned his educational credentials from prestigious institutions: PhD in Statistics, London University, 1974 MSc in Statistics and Operational Research, London University, 1972 BA in Mathematics, Cambridge University, 1971 Dr. Little's primary research interests focus on the analysis of data with missing values and complex survey designs. His work has revolutionized methods for handling missing data, moving from ad-hoc approaches like discarding incomplete cases to sophisticated model-based methods using likelihood-based inferential techniques. He is particularly known for his work on pattern-mixture models and penalized spline of propensity prediction methods. His research also extends to model-based approaches for survey analysis that are robust to misspecification. Dr. Little's inferential philosophy is model-based and Bayesian, though he carefully considers the effects of model misspecification. His applied interests span mental health, demography, environmental statistics, biology, economics, and social sciences. Dr. Little has received numerous prestigious awards and recognitions including the Wilks' Memorial Award from the American Statistical Association, being named an ISI highly cited researcher, and election to the National Academy of Medicine. He is also a Fellow of the American Statistical Association, the American Academy of Arts and Sciences, and the Royal Statistical Society. Throughout his career, Dr. Little has chaired or co-chaired 30 doctoral committees, mentoring the next generation of statisticians. He has served in significant editorial and leadership roles, including as Coordinating and Applications Editor of the Journal of the American Statistical Association (1992-1994), co-editor of the Journal of Survey Statistics and Methodology (2016-2018), and Vice President of the American Statistical Association (2010-2012). From September 2010-January 2013, he served as the inaugural Associate Director for Research and Methodology and Chief Scientist at the U.S. Census Bureau. Dr. Little is best known for his seminal book "Statistical Analysis with Missing Data" (co-authored with Donald Rubin), now in its third edition (2019). His work has had profound impacts on both theoretical statistics and practical applications across numerous scientific disciplines.

Ekaterina KazakView profile

Associate Professor

• Financial Econometrics
• Statistical Learning
• Econometrics
• +2 more

Dr. Ekaterina Kazak is an Associate Professor in Econometrics at the Department of Economics , University of Birmingham . She joined the department in September 2024 after serving as a Lecturer in Econometrics at the University of Manchester. PhD in Econometrics (2019) and MSc in Econometrics (2015) from the University of Konstanz BSc in Economics (2013) from Lomonosov Moscow State University Research Interests: Financial Econometrics: Portfolio choice, Value-at-Risk, Expected Shortfall Statistical Learning: Bagging, decision trees Econometrics: Robust inference, bootstrap, pretesting, forecast evaluation Teaching: Applied Econometrics and Policy Analysis (compulsory MSc Economics course) Advanced Quantitative Methods for Data Analytics (elective MSc Money, Banking and Finance course)

Perline Aline Delle DemangeView profile

Research Fellow

• Behavioral Genetics
• Educational Genetics
• Genetic Epidemiology
• +3 more

Perline Aline Delle Demange is a Postdoctoral Fellow at PROMENTA , University of Oslo, Department of Psychology. Her work focuses on the intergenerational transmission of (dis-)advantage using genetically-informed methodologies. Education : PhD in Biological Psychology (2018-2023, Vrije Universiteit Amsterdam); MSc in Cognitive Sciences (2015-2017), Diplôme de l'ENS (2015-2018, both from École Normale Supérieure Paris); BSc in Cell Biology and Psychology of Organisms (2012-2015, Université de Strasbourg). Research Interests : Mechanisms of intergenerational transmission, Mendelian randomization, genetic epidemiology, non-cognitive skills in education, and causal inference in social stratification. Her recent publications investigate genetic and environmental pathways linking family socioeconomic status to educational outcomes, mental health interactions with educational attainment, and methodological comparisons for indirect genetic effects. She maintains active GitHub repositories for open-science implementations and lectures on behavioral genetics.

• Hand Surgery
• Dermatology
• Clinical Research
• +6 more

Miguel Jansen is a Researcher at Erasmus MC , focusing on Dermatology and Hand Surgery. His work emphasizes patient-reported outcomes and clinical efficacy in musculoskeletal treatments.

Kyle NickodemView profile

Researcher

• Educational and psychological measurement
• Study design for causal inferencing
• Tools for promoting reproducible research
• +1 more

Kyle Nickodem serves as the Director of the Research Methodology Consulting Center at the University of Minnesota-Twin Cities , specializing in quantitative methods in education . He holds a PhD in Quantitative Methods in Education and a BA in Psychology. Degrees : PhD (University of Minnesota-Twin Cities), BA (University of Notre Dame) Research Interests span educational and psychological measurement , causal inferencing study design , and reproducible research tools . His work focuses on positive youth development through collaborations with school districts and educational organizations. Contributions include developing R packages like DIFreport and kfa for methodological rigor. He provides consultations on multilevel modeling , propensity scores , and study design , emphasizing data reproducibility and statistical programming . Current Projects involve analyzing social-emotional learning metrics , gender differences in violence perpetration , and measurement invariance across large-scale educational datasets. Contact: nicko013@umn.edu

• Stroke
• Neurointerventional Radiology
• Endovascular Therapy
• +5 more

James Siegler is an Assistant Professor in the Department of Neurology at the University of Chicago, specializing in stroke and neurointerventional procedures. His research focuses on endovascular therapy, mechanical thrombectomy, and stroke management for medium and distal vessel occlusions. Dr. Siegler's research interests encompass stroke pathophysiology, mechanical thrombectomy outcomes, endovascular therapy for medium vessel occlusions, cerebral venous thrombosis, and cervical artery dissection. His work investigates predictors of treatment success, compares endovascular therapy with medical management, and analyzes optimal patient selection criteria for various stroke interventions. He has made significant contributions to understanding the role of LDL lowering in intracranial atherosclerosis and the management of strokes with large ischemic cores. Analysis of Dr. Siegler's recent publications reveals a strong focus on medium vessel occlusions, particularly in the middle cerebral artery territory. His research employs sophisticated statistical methods including propensity score matching and regression discontinuity analysis to evaluate treatment efficacy. A significant portion of his work examines the comparative effectiveness of mechanical thrombectomy versus medical management across various stroke subtypes and time windows. Dr. Siegler has served as an editor for Neurology journal, contributing numerous 'Editors' Notes' on diverse neurological topics. His editorial work demonstrates broad expertise across multiple neurological subspecialties. As a researcher, Dr. Siegler participates in numerous multicenter international collaborations, contributing to large-scale studies that influence clinical practice guidelines in stroke management. His work bridges clinical practice and research, with direct implications for improving stroke treatment protocols worldwide.

Bao-We-Wal BambeView profile

Researcher

• Public Finance
• Monetary Policy
• Development Economics
• +2 more

Bao-We-Wal Bambe is an economics researcher at the University of Orléans , with a focus on fiscal and monetary policy frameworks in developing countries. He also collaborates with international institutions like the World Bank and the Overseas Development Institute (ODI). Research Interests Fiscal rules and public expenditure efficiency Inflation targeting mechanisms Climate change impacts on corporate R&D Macroeconomic stability and private investment Role of media freedom in governance Publication Trends His recent work (2023-2024) combines empirical analysis (propensity score matching, stochastic frontier methods) and policy evaluation, spanning topics like inflation targeting's effects on investment, fiscal rules' influence on public spending efficiency, and climate change barriers to innovation in developing economies. Scientific Contributions Published in leading journals: Journal of Economic Dynamics and Control , Public Choice , Oxford Economic Papers , Economic Modelling , and Ecological Economics Developed novel public expenditure efficiency indicators Analyzed complementarities between fiscal/monetary reforms and institutional quality

Joseph M. KushView profile

Assistant Professor

• Survey Data
• Educational Assessment
• Academic and Behavioral Interventions
• +4 more

Joseph M. Kush is an Assistant Professor in the Department of Graduate Psychology at James Madison University. He also serves as an assistant assessment specialist at the Center for Assessment and Research Studies and is a faculty member in the Assessment and Measurement Ph.D. program and the Quantitative Psychology concentration of the Psychological Sciences M.A. program. Ph.D. in Educational Psychology - Research, Statistics, and Evaluation (2021), University of Virginia B.S. in Psychology (2016), Syracuse University, with minors in Applied Statistics and Philosophy His research focuses on advancing statistical methods in social sciences, including Multilevel Structural Equation Modeling , Propensity Score Matching/Weighting , and Moderated Nonlinear Factor Analysis . He explores applications in educational assessment, behavioral interventions, and psychological measurement. Recent publications highlight methodological innovations in clustered randomized trials , integrative data analysis , and racial/economic equity in education. He oversees the Program Assessment Support Services (PASS) team, evaluating program-level assessment reports at JMU.

Jamie Lynn ToddView profile

Associate Professor

• Pulmonary Medicine
• Lung Transplantation
• Idiopathic Pulmonary Fibrosis
• +4 more

Jamie Lynn Todd is an Associate Professor of Medicine at Duke University School of Medicine and a member of the Duke Clinical Research Institute, specializing in lung transplantation and advanced lung disease with a focus on idiopathic pulmonary fibrosis (IPF). Her work bridges clinical practice and translational research to address critical challenges in pulmonary medicine. Education: Fellow in Pulmonary Medicine, Duke University (2008-2009) Resident, Duke University, Medicine (2005-2008) M.D., University of Colorado, School of Medicine (2005) Research Focus: Dr. Todd investigates clinical and genetic risk factors for lung fibrosis in transplant rejection and IPF, and examines how airway epithelial injury responses influence repair, fibrosis, and immunity. Her approach integrates molecular biology with clinical data to develop predictive models and biomarkers for disease progression. Publication Trends: Her 2025 publications emphasize biomarker discovery (prostasin, hyaluronan), risk stratification in lung transplantation, and environmental impacts on IPF. Utilizing large registries like IPF-PRO and ILD-PRO, her work prioritizes practical applications for early detection and intervention in fibrotic lung diseases. Grants & Collaboration: As PI on 10 active grants (2019-2029), including NIH K23 and Cystic Fibrosis Foundation awards, she leads the Lung Transplant Clinical Trial Network and Duke PROSPER training program. Her extensive grant portfolio supports translational research and mentorship in pulmonary academia. Research Ecosystem: Embedded in Duke's Department of Medicine and Clinical Research Institute, she collaborates with the Lung Transplant Outcomes Group and IPF-PRO Registry teams, driving multicenter studies that shape clinical guidelines for transplant complications and fibrotic lung diseases.

• Clinical Trials
• Randomized Controlled Trials
• Bayesian Statistics
• +8 more

Dr. Guangyu Tong is an Assistant Professor of Cardiovascular Medicine at the Yale School of Medicine with a secondary appointment as Assistant Professor of Biostatistics at the Yale School of Public Health. He directs the Cardiovascular Medicine Analytics Center (CMAC), providing critical analytic support for Yale's cardiovascular research community. Dr. Tong maintains faculty affiliations with multiple interdisciplinary centers including the Center for Methods of Implementation and Prevention Science (CMIPS), the Data Management and Statistics Core of the Alzheimer's Disease Research Center (ADRC-DMSC), the Center for Interdisciplinary Research on AIDS (CIRA), and the Interdepartmental Foci of Firearm Injury Prevention (FIP). His educational background includes a PhD from Duke University, an MA from Columbia University, and an AB from Peking University. Dr. Tong's research spans three major pillars: methodological innovation in trial design (particularly cluster randomized trials and stepped wedge designs), cardiovascular medicine applications, and firearm injury prevention research. His methodological contributions include advances in sample size estimation, treatment effect heterogeneity analysis, and complex trial designs with unequal cluster sizes. Dr. Tong's publications reveal a strong focus on translating statistical methods into clinical and public health applications. His work bridges Bayesian statistics with causal inference frameworks to address complex real-world problems like treatment effect heterogeneity in pragmatic trials and survivor average causal effects in studies with mortality outcomes. His research has appeared in top journals including Annals of Applied Statistics, Statistics in Medicine, and American Journal of Epidemiology. Faculty Scholar, National Institute on Aging's IMPACT Collaboratory (2023) Statistical Editor of the Journal of the American College of Cardiology (JACC) Associate Editor for BMC Medicine Principal Investigator on multiple NIH-funded trials As a senior statistician for the Great Smoky Mountain Study (GSMS), Dr. Tong examines how childhood exposures influence long-term mental health and firearm-related behaviors, directly informing policy debates around 'red flag' laws and age-based firearm restrictions. His collaborative work spans numerous NIH-funded projects including PULESA-UGANDA, TRANSFORM-HF, TRUE HAVEN, G4H, and iDOVE2.

• Machine Learning
• Medical Imaging
• Natural Language Processing
• +6 more

Jonathan Bates is a Lecturer at the Yale School of Public Health, affiliated with the Yale Center for Medical Informatics. His work focuses on applying machine learning and data science to public health challenges, particularly in HIV care, falls prevention, and medical device safety. His educational background includes: PhD in Biomathematics from Florida State University (2013) Postdoctoral Fellowship in Medical Informatics at VA Connecticut/Yale (2015) Dr. Bates' research spans machine learning and health informatics , with expertise in natural language processing , predictive modeling , and causal inference . His work targets vulnerable populations including older adults with HIV and veterans, addressing critical gaps in fall prevention, fracture risk assessment, and healthcare delivery optimization through advanced analytics. Analysis of his 15 most recent publications (2015-2023) reveals three dominant research thrusts: predictive modeling for geriatric falls and fractures in HIV populations, machine learning applications in medical device surveillance , and social network analysis for care coordination . These studies consistently leverage electronic health records and focus on high-impact public health problems affecting marginalized groups. Dr. Bates maintains active collaborations across Yale University, the Veterans Health Administration, and clinical departments including Cardiology and Emergency Medicine, demonstrating strong interdisciplinary integration of methodological innovation with real-world health challenges.

• Health services research
• analytic methods
• healthcare utilization
• +4 more

Stephen Flaherty is a Research Assistant Professor in the Department of Public Health and Health Sciences at Bouvé College of Health Sciences, Northeastern University, with affiliation at the Roux Institute. Previously, he served as Assistant Professor in Healthcare Administration at Stonehill College and brings over 20 years of healthcare experience from institutions including Beth Israel Deaconess Medical Center, Brigham and Women’s Hospital, and Caltech's Brain Imaging Center. His educational background includes: PhD in Population Health, Northeastern University MBA, University of Redlands BS in Computer Science and Engineering (with honors), University of Colorado Denver Dr. Flaherty's research centers on health services research and advanced analytic methods , specializing in healthcare utilization patterns, cost trend analysis, observational data methodologies, propensity score matching, and predictive modeling. His technical expertise spans SAS, SQL, R, and Python for analyzing large-scale healthcare datasets, particularly insurance claims, to evaluate population health interventions and clinical outcomes. His 2019-2021 publications reveal consistent focus on physician practice dynamics, hospital-physician integration effects, Medicaid expansion financial impacts, and inappropriate imaging utilization. These studies employ sophisticated statistical techniques on administrative databases to address systemic healthcare challenges in policy and delivery. Scientific recognition includes: Edgar C. Hayhow award for best Journal of Healthcare Management article (2019) Academy Health Annual Research Meeting Student Scholarship Award Six-semester Dean's List honors in Engineering at University of Colorado Professional service encompasses Stonehill College committees including Faculty Search, Strategic Planning Technology Workgroup, and Provost's Advisory Committee for Teaching and Learning. His current dual role combines Northeastern faculty duties with health outcomes research at Point32Health insurer, building on prior leadership managing Caltech's Brain Imaging Center and clinical operations at major medical centers.

Ronnie PingelView profile

Senior Lecturer

• Causal Inference
• Biostatistics
• Public Health
• +3 more

Ronnie Pingel serves as a Senior Lecturer at Uppsala University's Department of Statistics within the Faculty of Social Sciences, while also maintaining an associated researcher position at the Department of Public Health and Caring Sciences (General Practice division) in the Faculty of Medicine. With dual appointments spanning statistical methodology and public health applications, Pingel bridges quantitative theory with population health research. Ronnie Pingel's research spans causal inference methodology, biostatistics, and public health applications. Primary interests include propensity score-based estimators for causal effects, mental health epidemiology (particularly adolescent male psychosocial health), cardiovascular outcomes research, and diabetes/cardiovascular disease epidemiology. Recent work demonstrates expertise in longitudinal study designs, registry-based analyses, and methodological validation of statistical approaches in real-world health data. Analysis of Pingel's 15 most recent publications reveals consistent focus on causal inference methods applied to public health questions, with particular emphasis on mental health trajectories, cardiovascular outcomes, and adolescent health behaviors. The work frequently employs Swedish registry data and demonstrates methodological rigor in handling observational data through propensity scoring, decomposition analysis, and structural equation modeling. Pingel maintains active collaborations across Uppsala University's medical and statistical departments, with frequent co-authorship on interdisciplinary projects involving the SWEDEHEART registry, pregnancy cohort studies, and adolescent health surveys. Current research involves ongoing work with the SUPRIM trial (psychological interventions in coronary disease) and PhysCan trial (exercise during cancer treatment).