Machine Learning and Instrument Autonomy Group


Office: 158-244H

Mail Stop: 158-242
4800 Oak Grove Drive
Pasadena, CA 91109


Computer Science (BS, MS) - Columbia University

Research Interests

Machine Learning
Deep Learning
Computer Vision
Interpretable/Explainable ML
Robust ML


Paper Icon Kiri Wagstaff, Steven Lu, Emily Dunkel, Kevin Grimes, Brandon Zhao, Jesse Cai, Shoshanna B. Cole, Gary Doran, Raymond Francis, Jake Lee, and Lukas Mandrake. Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances. Proceedings of the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence, 2021. (Link to PDF) (CL#20-6521) IAAI Deployed Application Award

Paper Icon David E. Stillman, Katie M. Primm, Brian Bue, Kiri L. Wagstaff, Jake H. Lee, and Adnan Ansar. RSL Geostatistics Show Slopes Above the Angle of Repose and Significant Enhancement After Mars Year 34 Dust Storm. 52nd Lunar and Planetary Science Conference, Abstract #1578, 2021. (Link to publisher's site)

Paper Icon Jake H. Lee and Kiri L. Wagstaff. Visualizing Image Content to Explain Novel Image Discovery. Data Mining and Knowledge Discovery, 34(6), p. 1777-1804, 2020. (Link to publisher's site) (CL#20-2863) See also: Paper website for code, data, etc.

Paper Icon Steven Lu, Kiri L. Wagstaff, Jesse Cai, Gary Doran, Kevin Grimes, Jake Lee, Lukas Mandrake, and Yisong Yue. Improved Content-Based Image Classifiers for the PDS Image Atlas. Fourth Planetary Data Workshop, Abstract #7028, 2019. (Link to PDF) (CL#19-1913)

Paper Icon Kiri L. Wagstaff and Jake Lee. Interpretable Discovery in Large Image Data Sets. Proceedings of the Workshop on Human Interpretability in Machine Learning (WHI), 2018. (Link to PDF) (CL#18-3125) See also: Paper website for code, data, etc.

Jake Lee