Himabindu Lakkaraju

Himabindu "Hima" Lakkaraju is an Indian-American computer scientist who works on machine learning, artificial intelligence, algorithmic bias, and AI accountability. She is currently an Assistant Professor at the Harvard Business School and is also affiliated with the Department of Computer Science at Harvard University. Lakkaraju is known for her work on explainable machine learning. More broadly, her research focuses on developing machine learning models and algorithms that are interpretable, transparent, fair, and reliable. She also investigates the practical and ethical implications of deploying machine learning models in domains involving high-stakes decisions such as healthcare, criminal justice, business, and education. Lakkaraju was named as one of the world's top Innovators Under 35 by both Vanity Fair and the MIT Technology Review. She is also known for her efforts to make the field of machine learning more accessible to the general public. Lakkaraju co-founded the Trustworthy ML Initiative (TrustML) to lower entry barriers and promote research on interpretability, fairness, privacy, and robustness of machine learning models. She has also developed several tutorials and a full-fledged course on the topic of explainable machine learning. Early life and education Lakkaraju obtained a masters degree in computer science from the Indian Institute of Science in Bangalore. As part of her masters thesis, she worked on probabilistic graphical models and developed semi-supervised topic models which can be used to automatically extract sentiment and concepts from customer reviews. This work was published at the SIAM International Conference on Data Mining, and won the Best Research Paper Award at the conference. She then spent two years as a research engineer at IBM Research, India in Bangalore before moving to Stanford University to pursue her PhD in computer science. Her doctoral thesis was advised by Jure Leskovec. She also collaborated with Jon Kleinberg, Cynthia Rudin, and Sendhil Mullainathan during her PhD. Her doctoral research focused on developing interpretable and fair machine learning models that can complement human decision making in domains such as healthcare, criminal justice, and education. This work was awarded the Microsoft Research Dissertation Grant and the INFORMS Best Data Mining Paper prize.