Research

After a wonderful 4 years of PhD studies at UCSD's Artificial Intelligence Group, I have joined Carnegie Mellon University (CMU) as Assistant Professor in the Tepper School of Business, and affiliate faculty in the Machine Learning Department (MLD) and Heinz school of public policy. My research interests span core ML methods as well as critical applications and the social impacts of ML. Right now I'm focused on the robustness of ML systems under distribution shift (e.g., our recent ICML paper on label shift), applications of ML to healthcare (e.g. Learning to Diagnose), data-efficient deep learning (see recent work on active learning for NER, learning from partial labels, and ML+crowdsourcing), and questions regarding the fairness and interpretability of ML systems.

I value clear, understandable scientific prose and to this end have authored / co-authored two reviews of the literature and one interactive book. Deep Learning - the Straight Dope teaches deep learning through exposition, math and code in a fully-interactive textbook written with Jupyter. The latest review explains recurrent neural networks for sequence learning tasks and can be found on the arXiv and previously, I coauthored a review on differential privacy and machine learning. Occasionally, I write for more popular media, including a feature for IEEE Spectrum magazine (February 2016). Additionally, I have been a Contributing Editor at the KDnuggets data science website. In Fall, 2016, I launched Approximately Correct, a blog aimed at bridging technical and social perspectives on machine learning. We have had some success calling out AI hype and misconceptions but the problem has only intensified.

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