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(Track2) Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities Q&A
Himabindu Lakkaraju · Julius Adebayo · Sameer Singh

Wed Dec 09 03:00 AM -- 03:50 AM (PST) @ None

Author Information

Himabindu Lakkaraju (Harvard)

Hima Lakkaraju is an Assistant Professor at Harvard University focusing on explainability, fairness, and robustness of machine learning models. She has also been working with various domain experts in criminal justice and healthcare to understand the real world implications of explainable and fair ML. Hima has recently been named one of the 35 innovators under 35 by MIT Tech Review, and has received best paper awards at SIAM International Conference on Data Mining (SDM) and INFORMS. She has given invited workshop talks at ICML, NeurIPS, AAAI, and CVPR, and her research has also been covered by various popular media outlets including the New York Times, MIT Tech Review, TIME, and Forbes. For more information, please visit: https://himalakkaraju.github.io/

Julius Adebayo (MIT)

Julius Adebayo is a Ph.D. student at MIT working on developing and understanding approaches that seek to make machine learning-based systems reliable when deployed. More broadly, he is interested in rigorous approaches to help develop models that are robust to spurious associations, distribution shifts, and align with 'human' values. Website: https://juliusadebayo.com/

Sameer Singh (University of California, Irvine)

Sameer Singh is an Assistant Professor at UC Irvine working on robustness and interpretability of machine learning. Sameer has presented tutorials and invited workshop talks at EMNLP, Neurips, NAACL, WSDM, ICLR, ACL, and AAAI, and received paper awards at KDD 2016, ACL 2018, EMNLP 2019, AKBC 2020, and ACL 2020. Website: http://sameersingh.org/

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