STATISTICS LAB FOR CAUSAL & ROBUST MACHINE LEARNING
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The  Statistics Lab for Causal & Robust Machine Learning  at UCSD is led by Professor Jelena Bradic of the Department of Mathematics and Halicioglu Data Science Institute. The Lab  designs new models and methods that can more efficiently  assist in signal discovery, detection, estimation or testing  compared to the current methods for AI and machine learning. Lab expertise is in causality, robustness,  machine learning,  optimization,  statistics,  efficiency and model selection.  

The Lab is always looking for highly qualified and motivated PhD students and postdocs. Email: jbradic@ucsd.edu if interested.

Current Lab Members

 
Jelena Bradic, PI
Weijie Ji, PhD, 2019-
Zijin (Jason) Lin, PhD, 2019-
Denise Rava, PhD, 2017 - , co-advised with Ronghui Xu
Davide Vivano, PhD, co-advised, 2017
Yuqian Zhang, PhD, 2017-

Past Lab Members

Jiaqi Guo, PhD, 2013-2017, now Research Scientist at Amazon
Jue (Marquis) Hou, PhD 2014-2019, co-advised with Ronghui Xu, now postdoc at Harvard Biostatistics Department
Alexander Hanbo Li, PhD, 2013-2017, now Applied Research Scientist at Amazon AI Lab, before at Amazon Alexa.
Yinchu Zhu, PhD, 2014-2017, co-advised with Allan Timmermann, now Assistant Professor of Economics at Brandeis University; before Assistant Professor at University of Oregon, Lindquist College of Business
Christopher Campos, Undergraduate, 2013-2014, next PhD student at UC Berkeley Economics
Colleen Chan, Undergraduate, 2017-2018, next PhD student at Yale Statistics
Wenjing Yin, Undergraduate, 2013-2015, next PhD student at UIUC Statistics
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