Find Academic Supervisors

Megan E. VannemanView profile

Associate Professor

• Health Services Research
• Health Policy
• Health Economics
• +7 more

Megan E. Vanneman, PhD, MPH , is an Associate Professor with tenure at the University of Utah School of Medicine , affiliated with the Department of Internal Medicine , Division of Epidemiology, and the Department of Population Health Sciences , Division of Health System Innovation and Research. She is also a Core Investigator at the VA Salt Lake City Health Care System's IDEAS Center and co-leads the ACCENT CORE consortium. Dr. Vanneman earned her PhD in Health Services and Policy Analysis from UC Berkeley MPH in Health Policy and Management from Columbia University BA in Human Biology from Stanford University before completing a postdoctoral fellowship at the VA Palo Alto and Stanford University School of Medicine . Her research focuses on policy impacts on access, quality, and cost in healthcare systems like Medicaid and the Veterans Health Administration (VHA), particularly for mental health and substance use disorders . She employs mixed-methods combining econometrics, biostatistics, and qualitative interviews. Recent publications highlight her work on VA/non-VA care coordination opioid safety in community care mental health service disparities traumatic brain injury outcomes , reflecting a trend toward health equity and systemic policy evaluation . She teaches research methods and health policy , and her lab team includes data scientists and statisticians like Erin Beilstein-Wedel and Warren Pettey . Her work informs national policies on VHA care expansion and integrated healthcare delivery .

Catherine M. SteinView profile

Professor

• Tuberculosis Genetics
• Speech-Sound Disorder Genetics
• Genetic Epidemiology
• +4 more

Dr. Catherine M. Stein serves as Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine, where she directs research on tuberculosis genomics and developmental speech disorders. Her work bridges genetic epidemiology, biostatistics, and clinical translation through international collaborations in Uganda and South Africa. Education Ph.D. in Epidemiology and Biostatistics, Case Western Reserve University (2004) Research Focus : Dr. Stein's program investigates tuberculosis susceptibility through host-pathogen genomic interactions, particularly in HIV-coinfected populations, and speech-sound disorder etiology via cognitive domain modeling and genetic mapping. Her development of strum R software enables advanced structural equation modeling for family-based genetic studies, featured in Nature Medicine and American Journal of Speech-Language Pathology publications. Publication Trends : Recent work demonstrates methodological innovation in genetic epidemiology (40% in top-tier journals) with 54% international collaboration. Key themes include biomarker discovery for TB exposure, longitudinal outcomes of speech disorders, and ethical frameworks for genomic secondary findings. Mentorship & Funding : She has trained 30 Master's students (5 mentored), 5 PhD candidates, and 1 postdoc, with alumni at Eli Lilly, NIOSH, and NYC Public Health. Current grants include NIH R01s on TB resistance in HIV+ subjects (Uganda/South Africa), NIDCD-funded speech disorder genetics, and Gates Foundation research on pediatric infection recovery. Professional Engagement : Dr. Stein serves as Associate Editor for International Journal of Tuberculosis and Lung Disease and Biomed Central Infectious Diseases , with editorial board membership at Genes Immunity . She actively contributes to the American Society of Human Genetics Social Issues Committee.

Mike HorstView profile

Adjunct Professor

• Geographic Information Systems (GIS) in Health Research
• Electronic Medical Records (EMR) Data Analysis
• Health Systems Outcomes Research
• +3 more

Dr. Mike Horst serves as an Adjunct Faculty member with over 25 years of extensive experience in hospital and health system administration and technical roles. He is affiliated with Penn Medicine/Lancaster General Hospital, where he functions as a biostatistician for investigator-initiated research projects, a research administrator, and vice-chair of the Institutional Review Board. His research expertise spans Geographic Information Systems (GIS) applications in health studies, Electronic Medical Record (EMR) data utilization for health systems research, health outcomes analysis, and advanced statistical methodologies. He specializes in data science, big data analytics, predictive modeling, and spatial statistics to address public health challenges. Dr. Horst teaches SPHG 711 – Data Analysis for Public Health, providing biostatistical support and research administration for numerous clinical and health services investigations at Penn Medicine/Lancaster General Hospital.

Christy L. AveryView profile

Professor

• Genomic Epidemiology
• Gene-Environment Interactions
• Metabolomics
• +7 more

Christy L. Avery is a Professor in the Department of Epidemiology at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. She serves as Program Leader for Cardiovascular Disease Epidemiology. Her research focuses on genomic epidemiology, gene-environment interactions, metabolomics, and assessing cardiovascular disease burden in diverse populations across the life course. Her work integrates longitudinal data and multi-level factors (cellular, individual, contextual) to study health risks like hypertension and hypercholesterolemia, as well as behaviors such as smoking, physical activity, and obesity. She investigates genetic diversity in health risks among multi-ethnic U.S. populations and leads studies on metabolomic correlates of health and disease. Research Themes include: Genomic epidemiology of cardiovascular diseases Gene-environment interactions and omic biomarkers Multi-ethnic population studies Epidemiological methods for life-course analysis Metabolomics in disease prevention Recent Publications highlight trends in: Genetic risk scores for cardiovascular disease Multi-ethnic genome-wide association studies (GWAS) Metabolomic biomarkers in lifespan health Pharmacogenomics and drug-gene interactions Environmental health and exposomics Statistical genetics and causal inference She contributes to trans-ethnic genomics , refining genetic loci and improving precision medicine frameworks. Her collaborations span consortia like the Trans-Omics for Precision Medicine (TOPMed) and the Population Architecture Using Genomics and Epidemiology (PAGE) study.

Bryan E. ShepherdView profile

Professor

• Biostatistics
• Causal Inference
• Measurement Error
• +3 more

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

Yili HongView profile

Professor

• Machine Learning
• Reliability Analysis
• Survival Analysis
• +4 more

Yili Hong is a Professor in the Department of Statistics at Virginia Tech's College of Science. Her research focuses on statistical methodologies in reliability analysis, machine learning, survival analysis, and spatial data analysis. She holds a Ph.D. from Iowa State University (2009), with prior degrees from the same institution and the University of Science and Technology of China. Hong has been recognized with awards such as the ASA Physical and Engineering Sciences Award (2017) and the DuPont Young Professor Award (2011). She serves on editorial boards for journals like Technometrics and Journal of Quality Technology, emphasizing contributions to statistical reliability and big data applications. Her work bridges theoretical statistics with practical challenges in engineering and public health, including studies on Lyme disease emergence in Virginia through spatial modeling. Education: Ph.D. in Statistics, Iowa State University, 2009 M.S. in Statistics, Iowa State University, 2005 B.S. in Statistics, University of Science and Technology of China, 2004 Research interests span machine learning robustness, reliability of AI systems, degradation analysis, and statistical computing. Notable achievements include pioneering work on mixture experiments for AI algorithm evaluation and statistical methods for high-performance computing variability. Her recent projects address the reliability of autonomous systems and AI safety through recurrent event data analysis. Awards and recognitions highlight her contributions to statistical methodology and industrial applications. Editorial activities reflect her leadership in advancing statistical practice through peer-reviewed journals.

Tieming LiuView profile

Associate Professor

• Supply Chain Coordination
• Logistics Planning
• Healthcare Data Analytics
• +4 more

Dr. Tieming Liu is an Associate Professor in the School of Industrial Engineering and Management at Oklahoma State University , where he has served since 2005, first as Assistant Professor and then promoted to Associate Professor in 2011. His expertise bridges operations research, supply-chain coordination, healthcare analytics, renewable-energy policy, and production scheduling. Education Ph.D. in Transportation and Logistics, Massachusetts Institute of Technology, 2005 M.S. in Industrial Engineering and Management Science, Northwestern University, 2001 M.S. in Control Theory and Control Engineering, Tsinghua University, 2000 B.S. in Control Theory and Control Engineering, Tsinghua University, 1997 Research Interests Dr. Liu’s scholarship is organized around three pillars: Supply-Chain & Logistics: coordination contracts, inventory bounds, responsive pricing, channel rebates, and production flexibility under uncertainty. Healthcare Analytics: machine-learning models for diabetic retinopathy and sepsis risk prediction, clinical decision-support systems, and handling imbalanced EHR data. Energy & Sustainability: renewable portfolio standards, capacity coordination with renewable energy certificates, and incentive mechanisms for renewable and conventional generators. Recent methodological contributions include hidden Markov models for continuous mortality prediction, tree-augmented Bayesian networks for sepsis risk, and tensor-completion-driven convolutional networks for longitudinal medical data. Scientific Awards & Honors EJOR Reviewer Award, 2019 IEM Faculty Award, 2019 Halliburton Outstanding Faculty Award, OSU, 2014 Merrick Foundation Teaching Award, OSU, 2013 Riata/Koch Faculty Fellow, OSU, 2012 Lockheed Martin Teaching Award, OSU, 2011 Student Organization Faculty Advisor of the Year, OSU, 2010 Student Mentorship & Collaboration Dr. Liu has advised or co-advised a large cohort of doctoral and master’s students whose names appear as first or co-authors on his publications. His collaborative network spans MIT, Northwestern, IBM T. J. Watson Research Center, and multiple departments across OSU, fostering interdisciplinary projects that integrate operations research with real-world healthcare, transportation, and energy challenges. Laboratories & Teams He conducts research within the analytics and optimization laboratories of the School of Industrial Engineering and Management, directing projects funded by federal agencies and industry partners aimed at next-generation decision-support systems for healthcare providers, logistics operators, and energy market regulators.

• Biostatistics
• Missing Data Analysis
• Measurement Error
• +2 more

Yang Zhao is a Professor of Statistics in the Department of Mathematics and Statistics at the University of Regina. His research specializes in biostatistical methods for incomplete data, measurement error correction, and longitudinal analysis. Current work focuses on maximum likelihood estimation and Cox regression models with complex missing data patterns. His methodological contributions include diagnostic checking of imputation models and weighted estimating equations for longitudinal studies. He teaches advanced courses in statistical inference, time series analysis, and generalized linear models.

Fabrizia MealliView profile

Professor

• Causal Inference
• Econometrics
• Statistical Methods
• +3 more

Fabrizia Mealli is a Full-time Professor in the Department of Economics at the European University Institute (EUI), serving as Director of Graduate Studies. She holds a concurrent leave position as Professor of Statistics at the University of Florence's Department of Statistics, Informatics, Applications (DiSIA). Her research focuses on causal inference, econometric methods, Bayesian analysis, and missing data, with applications in social and biomedical sciences. Education & Academic Roles: Professor of Statistics, University of Florence (on leave) Full-time Professor, EUI Department of Economics (since 2023) Visiting positions at Harvard University (2001, 2015, 2017) Research Interests: Her work centers on causal inference methodologies, including principal stratification, interference, and bipartite settings. She develops statistical techniques for observational and experimental studies, Bayesian approaches, and applications in policy evaluation and network effects. Awards & Editorial Roles: Elected Fellow of the American Statistical Association (ASA) President-elect of the Society for Causal Inference (SCI) Associate Editor for Biometrika , Journal of the American Statistical Association , and Annals of Applied Statistics Grants & Collaborations: Her research has been applied to policy evaluation (e.g., hospital readmission programs, immigration policies) and public health interventions (e.g., air pollution effects). Labs & Teams: Leads initiatives on causal inference under interference and network analysis, collaborating with global institutions and researchers in statistics and econometrics.

Jingbo NiuView profile

Associate Professor

• Epidemiology
• Etiology of chronic diseases
• Data analysis of large cohort studies

Jingbo Niu, M.D., Sc.D., is an Associate Professor in the Department of Medicine-Nephrology at Baylor College of Medicine in Houston, TX. Their work focuses on epidemiological studies of chronic diseases, particularly osteoarthritis and its functional impacts, alongside data analysis of large cohort studies. Education: Bachelor of Science (BS) in Medicine from Harbin Medical School (1995) Doctor of Medicine (MD) from Peking Union Medical College & Chinese Academy of Medical Science (1997) Doctor of Science (DSc) in Epidemiology from Boston University School of Public Health (2006) Epidemiology Residency at Peking Union Medical College Hospital (2000) Research Interests: Dr. Niu’s research emphasizes understanding the etiology of chronic diseases, particularly osteoarthritis, through rigorous epidemiological methods. Their work includes analyzing large cohort datasets to evaluate the functional impacts of interventions like knee replacements and studying paradoxical phenomena in osteoarthritis progression. They also investigate how symptomatic osteoarthritis influences mobility decline in aging populations. Publications: Recent work includes studies on knee replacement outcomes, methodological challenges in osteoarthritis research, and longitudinal analyses of gait speed decline. These publications highlight a focus on improving clinical understanding and intervention efficacy in musculoskeletal and geriatric medicine. Awards/Grants: No awards or grants were explicitly listed in the provided text. Labs/Teams: No specific lab or team affiliations are mentioned in the profile.

Marc De BenedettiView profile

Assistant Professor

• Machine Learning
• Medical Data Analysis
• Geophysical Modeling
• +3 more

Marc De Benedetti is an Assistant Professor, Teaching Stream in the Department of Computer Science at the University of Toronto's Mississauga campus within the Mathematical and Computational Sciences school. His work bridges computational methods with healthcare analytics, geophysical modeling, and educational innovation. He specializes in applying machine learning to medical datasets (e.g., SEER cancer data) for predictive modeling and has contributed to CAR T-cell therapy efficacy studies in oncology. His geoscience research focuses on spatial-temporal variability in climate systems, particularly in the Congo Basin and UK wind dynamics. De Benedetti also designs supplementary educational programs to support undergraduate STEM learners. His research emphasizes data-driven approaches to address challenges in healthcare, environmental science, and pedagogy. Research interests include: Machine learning applications in healthcare survival prediction Immunotherapy comparative efficacy analysis Spatial variability in climate and hydrology systems Wind energy potential modeling Innovative undergraduate physics/mathematics tutoring strategies His recent articles analyze treatment outcomes for blood cancers using CAR T-cell therapies, explore model resolution impacts on geophysical data accuracy, and develop predictive tools for cancer prognosis via SEER datasets. No scientific awards are explicitly mentioned in the provided materials. While no formal grants or student advisement records are listed, his work demonstrates strong engagement with both academic research and educational development initiatives. His research locations include the Congo Basin for hydroclimatic studies and the UK for wind energy modeling. Collaborations likely span medical institutions for oncology studies and climatological agencies for environmental data analysis.

Steven GoodmanView profile

Professor

• Epidemiology
• Biostatistics
• Clinical Research Methods
• +4 more

Steven Goodman is a Professor of Epidemiology and Population Health, and Medicine at Stanford University, serving as Associate Dean of Clinical and Translational Research. He leads initiatives like the Stanford Program on Research Rigor and Reproducibility (SPORR) and co-directs the Meta-research Innovation Center at Stanford (METRICS). His research focuses on improving research reproducibility, evidence synthesis, and clinical trial methodology. Goodman holds academic appointments in multiple departments and has served on prestigious committees such as the National Academy of Medicine and the Patient-Centered Outcomes Research Institute (PCORI). Educationally, he earned an AB from Harvard, an MD from NYU, and advanced degrees in Biostatistics and Epidemiology from Johns Hopkins. He has authored over 200 publications, emphasizing Bayesian methods, ethical research practices, and meta-research. His awards include the Abraham Lilienfeld Award and induction into the National Academy of Medicine. Goodman teaches courses on clinical research methods, scientific inference, and diagnostic technologies. Key contributions include advancing open science practices, data-sharing policies, and critical analyses of FDA decision-making. His work bridges statistical rigor with practical applications in healthcare, aiming to enhance the reliability and relevance of biomedical research.

Menggang YuView profile

Professor

• Causal Inference
• Observational Studies
• Risk Prediction
• +7 more

Dr. Menggang Yu is a Professor of Biostatistics at the University of Michigan School of Public Health. He holds academic leadership roles, including former Director of the Biostatistics Core at UW Carbone Cancer Center and Associate Director of the Center for Health Disparities Research. His expertise spans causal inference, clinical biostatistics, and precision health, with a focus on cancer and chronic disease research. Education: PhD in Biostatistics (University of Michigan, 2004), MS in Applied Statistics (Bowling Green State University, 1999), BS in Computational Mathematics (Fudan University, 1996). Research interests emphasize causal inference, risk prediction, and treatment selection, particularly in clinical trials and observational studies. His work addresses methodological challenges in healthcare, including optimal surveillance strategies and subgroup analysis. Key awards include the Distinguished Student Paper Award (ENAR 2023) and ASA Biometrics Section Travel Awards (2017, 2018). He collaborates widely, contributing to high-impact journals like Biometrics , Journal of the American Statistical Association , and Cancer Cell . Grants and lab involvement include leadership roles in cores for cancer research and health disparities. Courses taught include survival analysis, causal inference, and clinical trial biostatistics.

Lihong QiView profile

Professor

• Biostatistics
• Epidemiology
• Genetic Association Studies
• +3 more

Lihong Qi is a Professor in the Department of Public Health Sciences at the University of California, Davis. Her research focuses on biostatistical methods applied to genetic association studies, complex traits (including cancer), survival analysis, and modeling missing data. She has contributed to studies on dietary energy density's role in cancer, environmental pollution's cardiovascular effects, and congenital anomalies like bladder exstrophy. Ph.D. in Biostatistics, University of Washington (2003) M.S. in Biostatistics, University of Washington (2000) M.S. in Statistics, Florida State University (1998) M.S. in Applied Mathematics, Peking University (1996) Her work integrates advanced statistical techniques to address public health challenges, particularly in epidemiology and genetic research. Her publications span journals in biostatistics, molecular medicine, and environmental health, reflecting interdisciplinary collaboration. No scientific awards listed. Her research has been supported by grants focusing on genetic epidemiology and environmental health studies. She has advised on studies involving bladder exstrophy, obesity-related cancers, and air pollution impacts. Her lab focuses on statistical methodologies for healthcare and genetic disorders.

• Biostatistics
• Epidemiology
• Health Services Research
• +12 more

Dr. Lin Liu is an Associate Adjunct Professor in the Department of Family Medicine and Public Health at the Herbert Wertheim School of Public Health & Human Longevity Science, UC San Diego. Her research focuses on biostatistical methodologies, including dose-response analysis, longitudinal HIV data modeling, and colorectal cancer prognostic modeling. She collaborates on studies addressing cancer, cardiovascular disease, hepatitis C, chronic pain, HIV/AIDS, and telemedicine. Dr. Liu’s work spans epidemiology, health services research, and clinical trial design. Her research interests include statistical methods for detecting minimum effective doses in toxicity studies, HIV viral load analysis, and predictive models for cancer recurrence. She has contributed to studies on opioid use, tuberculosis treatment adherence, and yoga interventions for chronic pain. Dr. Liu’s publications reflect her expertise in applying statistical techniques to public health challenges, such as improving cancer screening strategies and evaluating telehealth outcomes for mental health conditions like PTSD. Notable contributions include developing prognostic models for colorectal neoplasia recurrence and investigating the efficacy of videoconferencing psychotherapy. Her interdisciplinary collaborations span infectious disease epidemiology, psychiatric disorders, and biomedical informatics, emphasizing patient-centered care and electronic health record optimization.