OSDF project

Illinois_Park

PI: Chan Park · University of Illinois Urbana-Champaign

Mathematics and statistics

My research broadly focuses on (a) causal inference under interference and non-i.i.d. settings, (b) causal inference under unmeasured confounding, and (c) optimal treatment regimes and policy learning. A common theme in my research is to use non/semiparametric theory and optimization methods to develop efficient and robust estimators of causal quantities in (a)-(c). Website: https://www.chanpark.net/

959 TB

Data delivered over the OSDF

2,420,885

Jobs

1.1M

Files via OSDF

1.4M

CPU hours

0

GPU hours

Cumulative usage · Jul 2, 2025 – Jul 2, 2026

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