LEILANI GILPIN

Leilani H. Gilpin is a PhD candidate in Electrical Engineering and Computer Science at MIT, supervised by Prof. Gerald Jay Sussman and funded by the Toyota Research Institute. She works on enabling autonomous vehicles, and other autonomous machines, to explain themselves. Before MIT, Leilani worked as a research engineer at Palo Alto Research Center (PARC) focusing on anomaly detection in healthcare. Leilani earned a M.S. in Computational Mathematical and Engineering from Stanford University in 2013, and a B.S. in Mathematics (with honors), B.S. in Computer Science (with highest honors), and a music minor from UC San Diego in 2011. Leilani is passionate about mentoring students from underrepresented minorities through her work as a calculus instructor at SMASH Stanford, through the Xerox mentoring program, and the UCSD Alumni board. Leilani hopes to continue mentoring students of all backgrounds in academia.

• Leilani H. Gilpin. Reconciling system-wide errors with symbolic explanations. To appear in the Proceedings of the IJCAI Workshop on AI for Anomaly Detection, 2020.
• Leilani H. Gilpin. System-wide monitoring for anomaly detection. Advances in Cognitive Systems, 2020.
• Leilani H. Gilpin. Explaining possible futures for robust autonomous decision- making. Proceedings of the AAAI Fall Symposium on Anticipatory Thinking, 2019.
• Leilani H. Gilpin. Monitoring opaque learning systems. ICLR 2019 Debugging ML Models Workshop, 2019.
• Leilani H. Gilpin, Tianye Chen, and Lalana Kagal. Learning from explanations for robust autonomous driving. In ICML Workshop on AI for Autonomous Driving, 2019.
• Leilani H. Gilpin and Lalana Kagal. An adaptable self-monitoring framework for opaque machines. In Proceedings of the 18th International Conference on Au- tonomous Agents and MultiAgent Systems, pages 1982–1984. International Foun- dation for Autonomous Agents and Multiagent Systems, 2019.
• Leilani H. Gilpin. Reasonableness monitors. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
• Leilani H. Gilpin, David Bau, Ben Z Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal. Explaining explanations: An overview of interpretability of machine learning. In 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA), pages 80–89. IEEE, 2018.

• ACM FAccT Travel Award, 2020
• Nokia Bell Labs Prize Finalist, 2018
• AAAI Doctoral Consortium Travel Award
• Nokia Bell Labs Prize Semi-finalist, 2017
• USENIX Security Student Travel Award, 2016