(Jay) Digvijay Wadekar

I'm an assistant professor in the physics department at UT Austin, in the Weinberg Institute for Theoretical Physics and the Center for Gravitational Physics. My group broadly focuses on developing agentic AI systems for scientific discovery, gravitational-wave astrophysics, physics-informed ML, cosmology and particle phenomenology. I also co-chair the NASA AI/ML Science and Technology Interest Group and I collaborate with people in the NSF Cosmic AI institute at UT Austin.

I was previously a postdoctoral fellow at Johns Hopkins University (JHU), and before that a member at the Institute for Advanced Study (IAS). I completed my PhD at the Center for Cosmology and Particle Physics (CCPP) at New York University in 2021, working closely with the CCA cosmology group, and did my bachelors in Engineering Physics at IIT Bombay.

I grew up in Nanded in India. If you don't find me at my office, I may be backpacking, running or practicing latin and ballroom dancing.

Research directions

Verification-based agentic AI for scientific discovery

I am interested in agentic AI systems that have to justify their outputs against data and established physics rather than simply assert them, and in how you measure whether they can. This spans agents for gravitational-wave research, agentic equation discovery, and benchmarks for evaluating physics reasoning.

Gravitational-wave searches and astrophysics

I work on searching gravitational-wave data with richer waveform models than standard pipelines use, so that mergers those searches would miss can be recovered, and on what the resulting catalogs say about the astrophysics of compact objects. Related interests span formation channels, waveform modelling, parameter estimation, and population inference.

Machine learning for astrophysics

I am also interested in interpretable and robust machine learning: obtaining physically meaningful surrogates for expensive simulations rather than black boxes, and using symbolic regression to uncover universal relations in simulation and observational data.

Topics

Terms weighted by how often they appear across the titles, abstracts and keywords of my papers.

• 21 cm
• Axion
• Baryonic feedback
• Binary black hole
• Black hole
• Cosmological parameter
• Covariance matrix
• Dark matter
• Dwarf galaxy
• Galaxy cluster
• Galaxy survey
• Gravitational wave
• Halo occupation
• Higher harmonics
• Large-scale structure
• Machine learning
• Mass function
• Neural network
• Parameter estimation
• Population inference
• Power spectrum
• Search pipeline
• Symbolic regression
• Waveform model

Software

• gwIAS-HM: gravitational-wave search pipeline with higher harmonics. Repository
• CovaPT: analytic covariance of the galaxy power spectrum multipoles. Repository
• TIER: trigger inference using extended strain representations, to augment GW searches. Repository
• flywheel: low-latency parameter estimation for neutron star–black hole mergers via mode-by-mode filtering. Repository
• Gas_rich_dwarfs: dark matter constraints from observations of gas-rich dwarf galaxies. Repository

News

• Aug 2026 New preprint on unified remnant models for aligned-spin, precessing and eccentric black hole mergers (arXiv:2608.00934).
• Jan 2026 Joined UT Austin as an assistant professor in the Center for Gravitational Physics.
• Jan 2026 Physical Review Letters paper identifying the progenitor of the recoiling supermassive black hole RBH-1 using HST and JWST imaging (arXiv:2601.18986).
• Fall 2025 Became co-chair of the NASA Cosmic Origins AI/ML Science and Technology Interest Group.