جست‌وجوی استاد و پژوهشگر

Daniel Almirallمشاهده پروفایل

عضو هیئت علمی

• Adaptive Interventions
• Dynamic Treatment Regimes
• Sequential Multiple Assignment Randomized Trials (SMART)
• +۵ مورد دیگر

Daniel Almirall is a Research Associate Professor at the University of Michigan's Institute for Social Research (ISR) and holds a courtesy appointment in the Department of Statistics. He co-directs the Data Science for Dynamic Intervention Decision-making Center (d3c), focusing on developing statistical methods for adaptive interventions in healthcare and education. With a Ph.D. in Statistics from the University of Michigan (2007), his career includes roles at Duke University and the Durham VA Center for Health Services Research. His research emphasizes adaptive interventions—dynamic treatment strategies optimized via Sequential Multiple Assignment Randomized Trials (SMARTs)—to address chronic health conditions, mental health (e.g., autism, depression), and substance abuse. Key contributions include methodological frameworks for causal inference, longitudinal data analysis, and implementation science. Notable recognition includes a Top 20 Autism Article (2016) and a US Department of Education-recognized methodology publication (2020). Almirall advises students across statistics, biostatistics, and education, mentoring over a dozen PhD, master’s, and undergraduate researchers. His work bridges theory and practice, collaborating with clinicians and educators to deploy evidence-based interventions. Ongoing efforts focus on scalable strategies for schools and clinics to adopt adaptive interventions, leveraging d3c's collaborative environment.

Thomas M Braunمشاهده پروفایل

استاد

• Adaptive clinical trials
• Bayesian methods
• Longitudinal data
• +۴ مورد دیگر

Thomas M Braun, PhD is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He serves as a Member of the Senate Advisory Committee on University Affairs (SACUA) and teaches graduate-level biostatistics courses. Dr. Braun is an internationally recognized expert in Bayesian adaptive clinical trial design, with primary focus on cancer treatment and prevention research. Dr. Braun's educational background includes: PhD in Biostatistics from the University of Washington, Seattle (1999) MS in Biostatistics from the University of Washington, Seattle (1996) BBA from the University of Wisconsin, Madison (1990) His research expertise spans adaptive clinical trials, Bayesian statistical methods, longitudinal data analysis, oncology, rare diseases, dyadic analyses, and mobile health technology. Dr. Braun has developed novel Bayesian clinical trial designs for discovering safe and effective chemotherapy and immunotherapy regimens for cancer treatment. He is also pioneering methods to incorporate patient preferences into randomized clinical trials and sequential multiple assignment randomized trials (SMARTs). Analysis of Dr. Braun's recent publications (2023-2025) reveals a strong concentration on small n sequential multiple assignment randomized trials (snSMARTs), Bayesian methodology for rare disease research, mobile health interventions for cancer patients and caregivers, and innovative approaches to merging electronic health records with clinical trial data. His work consistently bridges theoretical statistical methodology with practical clinical applications in oncology and transplantation medicine. Dr. Braun has secured significant research funding from: The Food and Drug Administration (FDA) for developing Bayesian clinical trial designs for rare diseases The Patient Centered Outcomes Research Institute (PCORI) for developing Bayesian methods incorporating patient preference in SMARTs He actively collaborates on multiple research projects examining cancer treatment disparities, immunotherapy regimens for melanoma and other cancers, and the Roadmap mobile health platform for improving outcomes in stem cell transplant recipients and their caregivers. His work often focuses on dyadic patient-caregiver relationships and developing innovative statistical methodologies for complex clinical trial designs.

Abdus S. Wahedمشاهده پروفایل

استاد

• Personalized Medicine
• Dynamic Treatment Regimes
• Statistical Methodology
• +۵ مورد دیگر

Abdus S. Wahed is a Professor of Biostatistics and Associate Chair in the Department of Biostatistics and Computational Biology at the University of Rochester. His research focuses on developing statistical methods for dynamic treatment regimes (DTRs), particularly in Sequential Multiple Assignment Randomized Trials (SMARTs), and their applications in hepatitis B, cancer, and obesity. He also works on longitudinal data analysis, survival analysis, and causal inference. Key Roles: Associate Chair, Professor of Biostatistics Affiliations: University of Rochester Medical Center His research interests include: Personalized medicine and adaptive treatment strategies Methodologies for analyzing longitudinal and time-to-event data Clinical collaborations in hepatitis B, liver diseases, and cancer treatment Recent work emphasizes statistical innovations in SMART trials, including interim monitoring and sample size adjustments. His collaborations span virology, oncology, and public health, addressing issues like hepatitis B treatment discontinuation and arsenic exposure effects. Awards: No specific awards listed in provided text. Advising & Grants: Active in mentoring graduate students and leading research grants focused on biostatistical methodologies. Collaborates with organizations like the Hepatitis B Research Network and the Longitudinal Assessment of Bariatric Surgery study. Labs/Teams: Engaged in multidisciplinary teams addressing chronic diseases and statistical trial design.

Yu Chengمشاهده پروفایل

استاد

• Dynamic Treatment Regimes
• SMART (Sequential Multiple Assignment Randomized Trials)
• Causal Inference
• +۶ مورد دیگر

Yu Cheng is a Professor and Chair of the Department of Statistics at the University of Pittsburgh, affiliated with the Dietrich School. She holds secondary appointments in Biostatistics and Psychiatry. Her research focuses on dynamic treatment regimes, SMART trials, causal inference, and multiple endpoints analysis. She leads a PCORI-funded method grant on SMART studies and co-investigates projects on cardiovascular disease and maternal/child health outcomes. Her awards include ASA Fellow (2024) and Pittsburgh Chapter Statistician of the Year (2020). Dr. Cheng earned her PhD in Statistics from the University of Wisconsin-Madison (2006), following MS (National University of Singapore, 2001) and BS (University of Science and Technology of China, 1999) degrees. She has held leadership roles including ASA LiDS Section Treasurer (2021–2023), Department Chair (2014–present), and Director of Graduate Studies (2016). Her research integrates methodological innovation with clinical collaboration, addressing topics like HIV, smoking cessation, lupus, depression, and cystic fibrosis. Her 150+ publications span journals like Biometrics , Journal of the American Statistical Association , and Biostatistics . Teaching includes courses in Survival Analysis, Probability, and Statistical Consulting. She actively mentors students through supervised consulting programs.

Abdus S Wahedمشاهده پروفایل

استاد

• Biostatistics
• Personalized Medicine
• Dynamic Treatment Regimes
• +۳ مورد دیگر

Abdus S Wahed is a Professor in the Department of Biostatistics at the University of Pittsburgh. His research specializes in statistical methods for personalized medicine, particularly dynamic treatment regimes through sequentially randomized designs, multivariate longitudinal data analysis, and survival analysis with misclassified events. His educational background includes: Ph.D., Statistics (2003), North Carolina State University, Raleigh, NC M.A., Mathematical Statistics (2000), Ball State University, Muncie, IN M.Sc., Statistics (1994), University of Dhaka, Bangladesh B.Sc., Statistics (1992, Minor: Economics and Mathematics), University of Dhaka, Bangladesh Dr. Wahed's work focuses on developing adaptive treatment strategies for personalized medicine, including methods for screening viable regimes from observational data and identifying critical variables for dynamic decision-making. His methodological interests extend to analysis of censored survival data, length-biased sampling, and joint modeling of longitudinal-time-to-event data with measurement error. Collaborative projects address depression treatment sequencing, bariatric surgery risk prediction, hepatitis clinical trials, and weight-loss interventions. Recent publications (2021-2022) demonstrate expertise in sequential multiple assignment randomized trials (SMART), prognostic accuracy for ordinal competing risks via ROC surfaces, and evaluation of parametric/nonparametric methods for effect measure modification. These contributions advance biostatistical methodology for adaptive clinical trials, causal inference, and personalized treatment optimization. As a professor, Dr. Wahed teaches graduate courses including Linear Models, Estimation Theory, Likelihood Theory, Longitudinal Data Analysis, and Missing Data in Clinical Studies. His research likely involves mentoring graduate students and securing grants in biostatistical methodology and clinical collaborations, though specific details are not provided in the source text.

Holly Hartmanمشاهده پروفایل

استادیار

• Biostatistics
• Oncology
• Health Disparities
• +۳ مورد دیگر

Holly Hartman, PhD, is an Assistant Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine. Her research focuses on leveraging biostatistical methods to improve clinical trial design and analysis, particularly in oncology. She is a member of the Population and Cancer Prevention Program at the Case Comprehensive Cancer Center. Dr. Hartman holds a PhD in Biostatistics from the University of Michigan (2021), an MS in Biostatistics from the University of Alabama (2016), and a BS in Mathematics and Molecular & Cellular Biology from the University of Puget Sound (2011). Her work emphasizes addressing racial disparities in prostate cancer outcomes and optimizing radiation therapy approaches. Her research interests include clinical trial methodology, health disparities, and the integration of social determinants of health into oncology research. Recent studies have explored PSA screening policies, racial inequities in prostate cancer care, and the efficacy of androgen deprivation therapy in localized prostate cancer treatment. Dr. Hartman is affiliated with the Wolstein Research Building and Robbins Building facilities at Case Western Reserve University. She actively contributes to professional organizations including the American Statistical Association and the International Biometrics Society.

Kristin Linnمشاهده پروفایل

استادیار

• Predictive Modeling
• Causal Inference
• Sequential Multiple Assignment Randomized Trials (SMARTs)
• +۴ مورد دیگر

Kristin Linn, MStat, PhD is an Assistant Professor of Biostatistics at the University of Pennsylvania and Associate Director of PennSIVE, a biomedical imaging group. She is affiliated with CHIBE (Center for Health Incentives and Behavioral Economics) and focuses on methodological and applied research in precision medicine, causal inference, and health equity. Key affiliations: CHIBE, PennSIVE Expertise: Statistical methods for personalized interventions, algorithmic fairness, health policy evaluation Dr. Linn's research bridges biostatistics and behavioral economics to address critical healthcare challenges: Developing statistical methods for sequential multiple assignment randomized trials (SMARTs) to optimize long-term health behaviors Quantifying racial bias in healthcare algorithms (e.g., VA risk scores) Designing machine learning fairness frameworks for health applications Biomedical imaging research involving scanner harmonization and personalized TMS treatments Policy work evaluating Medicaid waivers and primary care payment systems Her methodological rigor has been applied across diverse domains including: Physical activity promotion Gun safety interventions Clinician behavior modification Medicaid policy evaluation Dr. Linn collaborates extensively with interdisciplinary teams across Penn Medicine, including: Kevin Volpp, MD, PhD Amol Navathe, MD, PhD Atheen Venkataramani, PhD, MD, MPhil Ravi Parikh, MD, MPP

Philip Lavoriمشاهده پروفایل

استاد

• Biostatistics
• Clinical Trials Design
• Translational Medicine
• +۳ مورد دیگر

Philip Lavori is a Professor at Stanford University with primary appointments in the Department of Health Research and Policy and Biostatistics, and a courtesy appointment in the Department of Statistics. He earned a PhD in Mathematics from Cornell University in 1974 and has been on Stanford's faculty since 1992, after prior roles at MIT, Harvard, and Brown. Educational Background: PhD in Mathematics, Cornell University (1974) His research focuses on innovative clinical trials design, including seamless phase II-III trials, biomarker-guided treatments, point-of-care randomization for comparative effectiveness research, and sequential multiple assignment randomized trials (SMARTs) for adaptive treatment strategies. He directs the Biostatistics Core of the Stanford Cancer Center, chairs the Department of Health Research and Policy, and co-directs Spectrum, Stanford's Clinical and Translational Sciences Award. Contact: lavori@stanford.edu

• Health Information Technology
• EHR and mHealth
• Patient Engagement
• +۸ مورد دیگر

Bryan Smith Gibson serves as an Assistant Professor in the Department of Biomedical Informatics at the University of Utah, holding an additional adjunct appointment in Physical Therapy & Athletic Training. His academic career builds upon extensive clinical experience including management of the Salt Lake City VA Cardiac Rehabilitation Program (2002-2008) and service on the LDS Hospital Inpatient wound care team. Dr. Gibson's educational trajectory demonstrates deep institutional commitment to the University of Utah: BS in Undergraduate Studies MPT in Graduate Training DPT in Doctoral Training (2006) PhD in Doctoral Training (2012) Postdoctoral Fellowship at Veterans Affairs Medical Center His research program centers on the sociotechnical dimensions of health informatics, with particular emphasis on translating psychological science into health technology design. Core methodologies include Workflow Analysis, Cognitive Task Analysis, and Human Centered Design and Evaluation. Primary application domains encompass Type 2 Diabetes management through technology-based behavioral interventions, EHR optimization, and mHealth solutions for patient engagement. Analysis of Dr. Gibson's publication record reveals consistent thematic focus across 15 recent works: chronic disease management (particularly diabetes) through informatics solutions, EHR redesign for improved clinical workflows, and patient engagement strategies using simulation and behavioral science principles. His work bridges technical health IT development with human factors considerations, frequently employing mixed-methods approaches to evaluate sociotechnical system impacts. Professional activities include significant contributions to tobacco cessation implementation science (QuitSMART Utah) and community-engaged diabetes research with Hispanic populations. While no formal advisees are documented in the provided materials, his collaborative publication pattern indicates active mentorship within multidisciplinary research teams.

Eric Laberمشاهده پروفایل

استاد

• Statistical Reinforcement Learning
• Precision Medicine
• Dynamic Treatment Regimes
• +۳ مورد دیگر

Eric Laber serves as James B. Duke Distinguished Professor of Statistical Science at Duke University, with cross-appointments as Professor of Biostatistics & Bioinformatics in the School of Medicine and Research Professor of Global Health. He co-directs the Duke Computing Initiative and maintains active roles in the Department of Statistical Science. His educational background includes: Ph.D. in Statistics, University of Michigan, Ann Arbor (2011) M.A. in Statistics, University of Michigan, Ann Arbor (2007) Laber's research pioneers statistical reinforcement learning for dynamic decision-making in complex health environments. He specializes in developing methodologies for Sequential Multiple Assignment Randomized Trials (SMARTs) to optimize personalized treatment sequences in precision medicine. His work bridges machine learning with clinical applications, focusing on cancer therapeutics, mental health interventions, and resource-constrained global health settings. Recent innovations address network interference in policy learning and risk-sensitive optimization for digital health platforms. Analysis of his 15 most recent publications reveals strong trends in adaptive clinical trial design, with increasing emphasis on mHealth applications (40% of recent work), PTSD management in cancer survivors (25%), and network-based reinforcement learning (20%). Key methodological threads include Thompson sampling variants (33%), functional data analysis for truncated outcomes (15%), and causal inference under interference (25%). His scientific recognition includes: James B. Duke Distinguished Professorship (premier faculty honor at Duke) Laber secures substantial grant funding for high-impact health research, currently leading 10 active projects totaling over $15M. Major awards include NIH/NCI funding for next-generation SMARTs in cancer therapeutics (2023-2028), NSF support for risk-sensitive statistical learning (2024-2027), and multiple NIH trials for digital mental health interventions. His collaborative approach spans oncology, psychiatry, and global health teams, with special focus on vulnerable populations including cancer survivors and adolescents. As Co-Director of the Duke Computing Initiative, he leads interdisciplinary teams developing scalable computational frameworks for precision medicine. Current efforts integrate reinforcement learning with electronic health records systems to create real-time clinical decision support tools, particularly for resource-limited settings in infectious disease management and chronic pain treatment.

Anne Fernandezمشاهده پروفایل

دانشیار

• Substance Use Disorders
• Alcohol Use Disorder
• Health Psychology
• +۷ مورد دیگر

Anne Fernandez, Ph.D. is an Associate Professor in the Department of Psychiatry at the University of Michigan Medical School. She serves as Director of Clinical Programming for both the U-M Addiction Treatment Services and the U-M Multidisciplinary Alcohol-Related Liver Disease Clinic. Additionally, she is the Director of Clinical Research for MI-ACRE (Michigan Integrated Center for Health Analytics and Reasoning Engine) and works as a practicing Clinical Psychologist with U-M Addiction Treatment Services. Dr. Fernandez received her doctoral degree in clinical psychology from University of Rhode Island with a focus in health psychology. Her training includes a clinical psychology residency in Behavioral Medicine at Yale New Haven Hospital and a post-doctoral fellowship at the Center for Alcohol and Addiction Studies at Brown University. Her research focuses on two primary areas: surgical optimization for patients with alcohol use disorders and prevention of opioid misuse among patients prescribed opioids for pain management after surgery. The overarching goal of this work is to improve surgical outcomes through early pre-operative intervention that addresses addiction and other behavioral health risk factors. She employs innovative methodologies including machine learning and natural language processing to identify patient cohorts in need of addiction prevention and intervention at key time points in clinical care. Dr. Fernandez's recent publications demonstrate a strong focus on alcohol use disorders, alcohol-associated liver disease, and perioperative substance use. Her work spans clinical trial design, diagnostic methodologies, risk prediction, and implementation of behavioral interventions in medical settings. She has made significant contributions to understanding gender differences in alcohol-related conditions and developing frameworks for psychosocial evaluation in liver transplantation. National Institute of Alcohol Abuse and Alcoholism career development grant University of Michigan Precision Health Award Dr. Fernandez leads several research initiatives funded by prestigious grants, with a particular focus on integrating behavioral health interventions into surgical care pathways. Her work bridges clinical psychology, hepatology, and surgical specialties to address critical gaps in care for patients with substance use disorders. She is actively involved in developing and testing interventions that leverage technology to improve identification and treatment of substance use issues in medical settings. As Director of Clinical Programming for the U-M Multidisciplinary Alcohol-Related Liver Disease Clinic, Dr. Fernandez oversees a team that provides integrated care for patients with alcohol-associated liver conditions. Her leadership in this clinic exemplifies her commitment to creating innovative care models that address the complex interplay between addiction and medical conditions.

Deborah Tateمشاهده پروفایل

استاد

• Obesity Prevention and Treatment
• Digital Health Interventions
• Behavioral Science
• +۳ مورد دیگر

Deborah Tate is a Professor in the Department of Nutrition at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. She holds a joint appointment in the Department of Health Behavior and serves as Director of the Communication for Health Applications and Interventions (CHAI) Core, a shared resource for the Nutrition Obesity Research Center and Lineberger Comprehensive Cancer Center. Dr. Tate is a behavioral scientist with a Ph.D. in Clinical Psychology from Virginia Tech, known for pioneering research in digital health interventions for obesity prevention. Education : PhD in Clinical Psychology (Virginia Tech, 1999) Appointments : Department of Nutrition (Primary), Department of Health Behavior (Joint), Nutrition Research Institute (Faculty Appointment) Her research focuses on two major areas: Optimizing digital and wearable technology-based interventions for weight regulation and disease prevention Advancing precision public health through adaptive trial designs and individualized intervention strategies Developing scalable behavioral treatments using Multiphase Optimization Strategy (MOST) and Sequential Multiple Assignment Randomized Trial (SMART) frameworks Dr. Tate's work has been continuously funded by the National Institutes of Health since 2000, with publications in top journals including: JAMA (Journal of the American Medical Association) New England Journal of Medicine Obesity American Journal of Clinical Nutrition Health Psychology She teaches the graduate-level course mHealth for Behavior Change and serves in leadership roles including: Director of UNC Weight Research Program Director of CHAI Core Member of Lineberger Comprehensive Cancer Center Internal Advisory Board Member, Nutrition Obesity Research Center

• HIV/AIDS Research
• Integrative Medicine
• Chronic Disease Management
• +۳ مورد دیگر

Frederick Hecht, MD, is a Professor at the University of California, San Francisco (UCSF), specializing in Internal Medicine and clinical research. He serves as Director of Research at the UCSF Osher Center for Integrative Medicine, leading a globally recognized research program focused on integrative medicine methodologies, particularly mindfulness-based interventions. His work bridges HIV/AIDS research and chronic disease management, emphasizing non-pharmacological approaches for conditions like diabetes, obesity, and mental health issues. He has pioneered studies on HIV viral dynamics and post-treatment control, alongside innovative research designs for evaluating lifestyle interventions. Dr. Hecht holds advanced training in clinical epidemiology and has authored over 300 publications. His leadership roles include Board Chair of the Academic Consortium for Integrative Medicine and Health. With 12 NIH K awardees mentored and over 30 grants secured (19 NIH-funded), he has directed three Program Project grants. His research integrates factorial trials and Sequential Multiple Assignment Randomized Trials (SMARTs) to enhance intervention rigor. Key areas of focus include HIV reservoir dynamics, mindfulness in chronic pain management, and wearable device data for health monitoring. He chairs the TRIM fellowship program, fostering the next generation of integrative medicine researchers. His work emphasizes translational research, bridging basic science and clinical practice to address pressing public health challenges.

Kelley M. Kidwellمشاهده پروفایل

استاد

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

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

Min Qianمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Sequential Decision Making
• Statistical Inference
• +۶ مورد دیگر

Min Qian is an Associate Professor of Biostatistics at Columbia University Mailman School of Public Health, specializing in data analytic methods for health decision-making. She is affiliated with the Foundations of Data Science, Health Analytics, and Education centers. Current role: Associate Professor of Biostatistics Institution: Columbia University Mailman School of Public Health Email: mq2158@cumc.columbia.edu Research Focus Dr. Qian develops advanced statistical and machine learning techniques for: Reinforcement learning in sequential decision-making Statistical inference for non-regular parameters Sequential Multiple-Assignment Randomized Trials (SMART) Mobile health applications Her work addresses health outcomes in cardiovascular disease, cognitive impairment, and psychiatric conditions, leveraging longitudinal and microbiome data. Publication Trends Her recent articles emphasize: Survival analysis for cancer outcomes Microbiome-based prediction models Cognitive intervention trials for aging populations Machine learning in cardiovascular risk stratification Statistical methods for individualized treatment regimes Affiliations Foundations of Data Science Health Analytics Education Center