Machine Learning and Instrument Autonomy Group

Dr. Umaa Rebbapragada is a Data Scientist and the Technical Group Supervisor of the Machine Learning and Instrument Autonomy (MLIA) group. During her tenure in MLIA, Umaa has developed a portfolio of cross-cutting capabilities and product lines that are applied to problems in mission operations, Earth science, planetary science and astrophysics. She is the Principal Investigator for the Time Series Forecasting Evaluation and Deployment (Time-FED) machine learning system that has been applied to use cases at the Deep Space Network and in Global Navigation Satellite System (GNSS) datasets. She is a co-investigator for the Domain-agnostic Outlier Ranking Algorithms (DORA) toolbox and has served as the technical lead on the development and deployment of real-time machine learning systems at the intermediate Palomar Transient Factory and Zwicky Transient Facility which are wide-field time domain astronomical surveys. Her collaborations with members of the JPL science community, Caltech and external organizations have resulted in numerous funded projects where she serves as either a principal investigator or task lead. In addition to her project leadership, Umaa has served as Asian American Council Chair and multiple service positions in support of diversity, equity and inclusion (DEI). Umaa received her Ph.D. in Computer Science from Tufts University and B.A. in Mathematics from the University of California, Berkeley.

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Email: umaa.d.rebbapragada[at]
Mail Stop: 158-242
4800 Oak Grove Drive
Pasadena, CA 91109


  • Computer Science (PhD) - Tufts University
  • Computer Science (MS) - Tufts University
  • Mathmatics (BS) - University of California Berkeley
Dr. Umaa Rebbapragada