Alexander Volfovsky

Alexander Volfovsky is an Associate Professor of Statistical Science at Duke University, affiliated with the Trinity College of Arts & Sciences. He holds a Ph.D. in Statistics from the University of Washington (2013), and a B.Sc. (Hons) and M.S. in Mathematics from the University of Chicago (2009). His research focuses on network analysis, causal inference, and computational-statistical tradeoffs, with applications in social sciences, epidemiology, and public health.

**Education:**

• Ph.D., University of Washington, 2013
• B.Sc. (Hons) and M.S., University of Chicago, 2009

**Research Interests:**
Volfovsky explores network dynamics, causal inference under complex dependencies, and statistical methods for large-scale data. Key areas include:
- Political polarization and social media experiments - Epidemic modeling on dynamic networks - Interpretable machine learning for causal inference - Design of randomized experiments in networked systems - Applications in public health and policy evaluation

**Grants & Funding:**
Principal investigator for NIH-funded studies on anti-seizure treatment efficacy and NSF-funded projects on interpretable AI in healthcare. Collaborates with institutions like Massachusetts General Hospital and the John Templeton Foundation.

**Awards:**

• Thomas Langford Award, 2023

**Labs & Initiatives:**
Co-Director of the Polarization Lab (studying partisan dialogue) and Almost Matching Exactly Lab (interpretable causal inference tools). Past President of the New Researchers Group at the Institute of Mathematical Statistics.