Virisha Timmaraju is a Data Scientist at NASA’s Jet Propulsion Laboratory, where she develops machine learning methods to enable the detection and characterization of Earth-like exoplanets and support future flagship missions such as the Habitable Worlds Observatory. Her work focuses on separating weak planetary signals from dominant stellar and instrumental noise, advancing the methodological foundations of extreme precision radial velocity (EPRV) science. She has served as a Principal Investigator and technical lead on NASA-funded efforts applying machine learning to exoplanet detection and characterization.

Beyond EPRV, Virisha develops science-aware machine learning systems that improve the reliability and scalability of astrophysics pipelines. Her broader contributions include learning from sparse or incomplete data (such as Arctic sea surface height reconstruction from SWOT observations), autonomous science prioritization for planetary rovers, anomaly detection in radio SETI experiments, and the NASA ExEP Technosignatures GapList study guiding NASA’s technosignature research priorities. She contributes to community efforts shaping the role of AI in astronomy as a steering committee member for the Extreme Precision Radial Velocity Research Coordination Network and the Habitable Worlds Observatory AI/ML Working Group. Virisha holds an M.S. in Electrical Engineering from Texas A&M University and is passionate about using AI to make scientific discovery more adaptive and interpretable, while fostering community collaboration through initiatives such as SUDS Ask a Data Scientist.

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Education

• M.S., Electrical Engineering, Texas A&M University, College Station, TX (2018)
• B.Tech, Electronics and Communication Engineering, GITAM University, Hyderabad, India (2016)

Research Interests

• Exoplanet Detection and Characterization
• Radio Astronomy and SETI
• Instrument Systematics and Calibration
• Physics-Constrained Machine Learning