STATISTICS LAB FOR CAUSAL & ROBUST MACHINE LEARNING
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STATISTICS LAB FOR
​CAUSAL AND ROBUST MACHINE LEARNING
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Jelena Bradic

Professor of Statistics

Jelena Bradic holds a joint appointment in the Department of Mathematics and Halicioglu Data Science Institute (HDSI).  

She holds a Ph.D. in Statistics from Princeton University's Operation's Research and Financial Engineering Department, where she studied under Jianqing Fan. Previously she obtained B.S. and M.S. in Mathematics from University of Belgrade, Serbia.

RESEARCH INTEREST

CAUSAL INFERENCE


ATE     optimal inferential methods under minimal conditions
Dynamics     challenges of multiple treatment assignments

Policy     learning who/when to treat  

​ROBUSTNESS


(C)ATE     allow model-misspecification and/or allow sparsity misspecification
Machine Learning     define new ML methods and new robustness paradigms 

Missing Data     methods that formally address missing data: semi-supervised, supervised, censored...

HIGH-DIMENSIONAL STATISTICS


Detection     hypothesis testing in models with many more parameters than samples 
Learning     optimal prediction error bounds for complex methods
Censored    
survival analysis, lifetime data, right-censored data ...

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