Yaesoubi-Headshot

Reza Yaesoubi, PhD, is an Associate Professor at the Philip R. Lee Institute for Health Policy Studies and the Department of Epidemiology and Biostatistics at the University of California, San Francisco, and a core faculty member of the UCSF & UC Berkeley Computational Precision Health (CPH) program. He is a health decision scientist focused on developing and applying analytical methods to inform data-driven, value-based decisions in clinical care, public health, and global health. His primary area of expertise lies in guiding decisions when evidence and data evolve too rapidly for static policies or guidelines to suffice; for example, during outbreaks of novel pathogens or the spread of antimicrobial resistance. He is also interested in advancing the theoretical and methodological foundations of adaptive decision-making and health care resource allocation.

Dr. Yaesoubi serves as an Associate Editor for Health Care Management Science and as an Editorial Board Member for Medical Decision Making. He was the Scientific Review Co-Chair for the Society for Medical Decision Making's 2024 and 2025 annual meetings.

Mariana-Raniere

Mariana Raniere, PhD
Post-Doctoral Associate

Mariana is a Postdoctoral Associate at the Yale School of Public Health, with previous experience as a Senior Mathematical Modeler at the UK Department of Health and Social Care. She earned her PhD in Computer Science from Queen Mary University of London, where her research focused on using Bayesian Network models to support healthcare decision-making. She also holds bachelor’s and master’s degrees in Statistics, and her expertise spans machine learning, decision science, and statistical modeling. She work at the intersection of machine learning and decision science, developing models that drive evidence-based decision-making in healthcare. Her interests center on applying data-driven approaches to inform policy and clinical practice, with a focus on building tools that are accurate, actionable, and advance health equity and social impact.

Tima Mikdashi

Fatima (Tima) Mikdashi, MS
PhD Student

Tima is a PhD student at Yale’s Department of Health Policy and Management (Economics Track). She holds a bachelor’s degree in bioengineering from the University of Maryland, College Park and a MPhil in Population Health Sciences from the University of Cambridge. She previously worked as a data consultant in the Office of the Secretary at the Department of Health and Human Services (HHS) during the COVID-19 pandemic. She is interested in decision science, policy modeling, and health equity.

Xavier Guaracha

Xavier Guaracha, BS
PhD Student

Xavier is a PhD student in the UCSF & UC Berkeley Computational Precision Health (CPH). He earned a B.S. in Statistics and Data Science from Yale University in 2025. His current work compares the cost-effectiveness of time-based and adaptive non-pharmaceutical intervention policies during emerging pandemics. His previous work focused on developing a disease-agnostic simulation-to-real framework to predict hospital capacity strain three weeks in advance. He is interested in using deep reinforcement learning, decision science, and simulation modeling to identify optimal treatment and intervention policies at the individual and population levels.
Ze Wang

Ze Wang, BA, BS
Junior Specialist

Ze is a Junior Specialist at the Philip R. Lee Institute for Health Policy Studies. He holds a Bachelor of Arts in Economics from Brandeis University and a Bachelor of Science in Applied Mathematics from Columbia University. His undergraduate research at Columbia University Medical Center focused on pulse wave inverse problems, where he first developed deep learning models to approximate solutions and later applied analytical and numerical methods to solve the governing partial differential equations. His research interests include decision science, mathematical modeling, and simulation, with a focus on applying quantitative methods to inform health policy and clinical decision-making

Yashleen Sharma

Yashleen Sharma, MS
Junior Specialist

Yashleen is a Junior Research Specialist at the Philip R. Lee Institute for Health Policy Studies. She specializes in mathematical modeling, algorithm design, and computational methods to study infectious disease dynamics, integrating compartmental models (SIR) and agent-based models with social determinants of health. Her work highlights how structural inequities and stigma shape disparities in infection outcomes and supports evidence-based health policy. She aims to further bridge epidemiological modeling with biomedical sciences to strengthen early detection, forecasting, and interventions for emerging diseases. She holds a B.S. in Applied Mathematics with an emphasis in Computer Science and a minor in Physics, as well as an M.S. in Mathematics, where her research focused on infectious disease modeling.

Ritvik

Ritviksiddha Penchala, BS
Junior Specialist

Ritvik is a Junior Specialist at the Philip R. Lee Institute for Health Policy Studies. He earned his B.S. in Computer Science with a specialization in Bioinformatics from the University of California, San Diego (UCSD) in 2024. As a Research Assistant at UCSD, he developed automated proteomic pipelines to analyze unannotated microbiome samples for predicting immune responses and applied machine learning techniques to large-scale genomic datasets to study psychiatric outcomes. His current interests focus on leveraging innovative data science methodologies to drive clinically meaningful advances in health and medicine.

Former Lab Members

  • Post-Doctoral Associates
  • Post-Graduate Associates/Junior Specialists
    • Shiying You (2020 – 2021)
    • Qin Xi (2022)
    • Jingwen Li (2022 – 2023)
    • Fatima (Tima) Mikdashi (2023 – 2024)
    • Tianfang Shao (2024-2025)
    • Xavier Guaracha (2024-2026)
  • MPH, MS, and Medical Students
    • Michelle Guo (2016-2017)
    • Ava Yap (2016-2018)
    • Maya Mahin (2016-2017)
    • Zongbo Li (2018-2019)
    • Sydney Pryor (2019-2020)
    • Jingyi Meng (2020-2021)