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Author Information
Alexander Feldman (Xerox PARC)
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/
More from the Same Authors
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2022 : A Human-Centric Take on Model Monitoring »
Murtuza Shergadwala · Himabindu Lakkaraju · Krishnaram Kenthapadi -
2022 : Invited talk (Dr Hima Lakkaraju) - "A Brief History of Explainable AI: From Simple Rules to Large Pretrained Models" »
Himabindu Lakkaraju -
2021 : Panel II: Machine decisions »
Anca Dragan · Karen Levy · Himabindu Lakkaraju · Ariel Rosenfeld · Maithra Raghu · Irene Y Chen -
2021 : [IT3] Towards Reliable and Robust Model Explanations »
Himabindu Lakkaraju -
2021 : Speaker Introduction »
Alexander Feldman -
2021 Workshop: eXplainable AI approaches for debugging and diagnosis »
Roberto Capobianco · Biagio La Rosa · Leilani Gilpin · Wen Sun · Alice Xiang · Alexander Feldman -
2021 : Invited Talk: Towards Reliable and Robust Model Explanations »
Himabindu Lakkaraju -
2020 Poster: Incorporating Interpretable Output Constraints in Bayesian Neural Networks »
Wanqian Yang · Lars Lorch · Moritz Graule · Himabindu Lakkaraju · Finale Doshi-Velez -
2020 Spotlight: Incorporating Interpretable Output Constraints in Bayesian Neural Networks »
Wanqian Yang · Lars Lorch · Moritz Graule · Himabindu Lakkaraju · Finale Doshi-Velez -
2020 Tutorial: (Track2) Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities Q&A »
Himabindu Lakkaraju · Julius Adebayo · Sameer Singh -
2020 Poster: Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses »
Kaivalya Rawal · Himabindu Lakkaraju -
2020 Tutorial: (Track2) Explaining Machine Learning Predictions: State-of-the-art, Challenges, and Opportunities »
Himabindu Lakkaraju · Julius Adebayo · Sameer Singh