Vishal Gajjar

Breakthrough Listen is humanity’s largest and most comprehensive initiative dedicated to the search for technologically advanced extraterrestrial life. As a Project Scientist for Breakthrough Listen, I collaborate with multiple leading radio observatories around the globe to facilitate technosignature searches. These include the Sardinia Radio Telescope (Italy), LOFAR-SE station (Sweden), LOFAR-IE station (Ireland), FAST telescope (China), and the Giant Metrewave Radio Telescope (India). 

In collaboration with the SETI Institute and with support from Amateur Radio and Digital Communication (ARDC), the ARISE curriculum brings hands-on, skills-focused learning to classrooms. Designed for courses in astronomy, digital communication, signal processing, and electronics, ARISE uses the search for extraterrestrial intelligence (SETI) as a gateway to explore real-world applications with GNU Radio and data from world-class telescopes like the Allen Telescope Array. This project-based approach equips students to problem-solve, collaborate, and think critically while engaging with authentic scientific data. By bridging theory and practice, ARISE inspires curiosity and prepares students with job-ready skills.

Narrowband technosignatures passing through an exoplanetary plasma environment (Exo-IPM) can suffer spectral broadening and scintillation, reducing their detectability. While interstellar scattering is well studied, the effects of stellar winds, flares, and CMEs remain poorly constrained. Using turbulence scaling laws extended to active M-dwarfs, in this project we show that Exo-IPM broadening can reach 10–100 Hz at low frequencies, severely impacting narrowband SETI searches. Survival-function analysis across stellar and orbital parameters suggests these effects may contribute to the “Great Silence,” underscoring the need for mitigation strategies in future surveys such as SKA-Low.

Machine learning (ML) is transforming exoplanet discovery, moving beyond traditional transit methods to identify anomalies in stellar light curves. One exciting possibility is detecting alien megastructures—large, irregular constructs whose transits would leave distinct, non-spherical signatures. This project will develop an ML tool to infer object shapes directly from light curves and rank candidates based on deviations from circular forms. Applied to Kepler and TESS data, the approach will flag highly irregular shapes as potential megastructures. By systematically exploring these anomalies, the work broadens technosignature searches and contributes to answering the fundamental question: Are we alone in the universe?