Sidian Lin

Contact

:linkedin_logo_initials: www.linkedin.com/in/sidianlin

Operations Research | Econometrics | Causal Inference | Reinforcement Learning | Healthcare Operations Management

I am a fifth-year Ph.D. student at Harvard University, working at the intersection of operations research, econometrics, and machine learning, advised by Prof. Soroush Saghafian in the PIAS-Lab. My research focuses on sequential decision-making in complex systems, with applications in healthcare operations, resource allocation, and policy design. I develop predictive and prescriptive models that integrate machine learning (reinforcement learning), causal inference, and optimization to support high-stakes decisions where experimentation is limited.

My recent projects include personalized treatment recommendations using machine learning/reinforcement learning, analyzing national liver transplantation data to study allocation efficiency, and combining predictive tools and optimization models to reduce hospital readmissions. I am broadly interested in how quantitative modeling can improve decision-making in large systems, from healthcare to technology and international development.

In Summer 2026, I will join Google (Kirkland, WA) as a Data Scientist Research Intern.

*https://drive.google.com/file/d/1kv7PdNEgtyPB-8ZAtzlGvvbdujXIsz5y/view?usp=drive_link*

Education

Skills

R Python Stata

C++ Matlab

Languages

🇺🇸 English (fluent)

🇨🇳 Mandarin Chinese (native)

🇰🇷 Korean (advanced)

Publications

<aside>

A Multi-Agent Reinforcement Learning Algorithm for Personalized Recommendations in Bipolar Disorder

Sidian Lin, Soroush Saghafian, Jessica M. Lipschitz, Katherine E. Burdick

PNAS Nexus (2025)

</aside>

<aside>

Digital Phenotyping in Bipolar Disorder: Using Longitudinal Fitbit Data and Personalized Machine Learning to Predict Mood Symptomatology

Jessica M. Lipschitz, Sidian Lin, Soroush Saghafian, Chelsea K. Pike, Katherine E. Burdick

Acta Psychiatrica Scandinavica (2025)

</aside>

<aside>

Technician Routing and Scheduling with Employees’ Learning through an Implicit Cross-training Strategy

Xi Chen, Kaiwen Li, Sidian Lin, Xiaosong Ding

International Journal of Production Economics (2024)

</aside>

Working Papers

<aside>

Allocation Out Of Sequence (AOOS) in Liver Transplantation: Insights into the Who, What, and the Why

with David Lee, Jackson Fulk-logon, Nicholas DeStefino, and Soroush Saghafian

</aside>

<aside>

Transparent and Efficient Liver Transplantation Allocation via Reinforcement Learning

with David Lee and Soroush Saghafian

</aside>

<aside>

Leveraging Predictive Analytics for Reducing Readmission Rates in Healthcare Facilities

with Pooyan Kazemian and Soroush Saghafian

</aside>

Selected Conference Presentations

Conference Presentations

Teaching @ Harvard

Teaching

<aside>

</aside>