I am an astrophysics researcher focused on the electromagnetic counterparts of compact-object mergers, including binary black holes embedded in AGN disks and kilonovae from neutron star–black hole and neutron star–neutron star mergers. My work combines theoretical modeling, machine learning, and simulation-based inference to understand the most energetic transients in the Universe. I also develop physics-informed open-source tools for the multi-messenger astronomy community, enabling rapid candidate classification, ranking, and follow-up of newly discovered Multimessenger events.
An open-source framework for scoring optical transient candidates against a kilonova model grid in real time, deployed into TROVE (Tool for Rapid Object Vetting and Examination) — a community coordination platform for multimessenger follow-up. Uses ABC diagnostics and inverse-variance weighting to reject supernova contaminants within 3–4 days of a GW trigger under LSST ToO strategies. Validated on AT 2017gfo and SN 2025ulz.
Building amortized inference pipelines — Amortized Neural Posterior Estimation, Truncated Sequential NPE, Neural Likelihood Estimation — that reduce kilonova parameter inference from hours of MCMC to seconds. Validated on AT 2017gfo and applied to GRB 230307A multi-wavelength data. Symbolic regression methods have extracted analytic scaling laws from BNS merger simulations (NeurIPS 2025).
Leading multi-epoch optical and spectroscopic campaigns for LIGO/Virgo/KAGRA BBH merger candidates, testing whether remnant interactions with AGN accretion disks produce detectable EM emission. Published in Physical Review D (2025) and arXiv (2026), these are among the most comprehensive optical datasets yet for BBH EM counterpart searches.
As part of the CBPF LAB-IA group, developing the Astronomical Data Smart System — AI-powered autonomous agents for real-time LSST alert triage (up to 10 million alerts/night), LLM-assisted astrophysical reasoning, and fast SBI physical modeling targeting kilonovae, TDEs, FBOTs, ultra-stripped supernovae, and orphan GRB afterglows.
Member of COIN — a worldwide interdisciplinary network building data-driven solutions across astrophysics. Current COIN contribution: the SAGUI framework for SED-based segmentation of multi-band galaxy images, applied to JWST/JADES data in GOODS-South (arXiv 2026). COIN projects span statistical methodology, machine learning, and open-source tool development for the global astronomy community.
I am currently a Ph.D. candidate at CBPF, actively seeking postdoctoral opportunities in astrophysics, multi-messenger astronomy, or machine learning applied to physical sciences. I welcome opportunities to collaborate on challenging open problems at the intersection of AI and astrophysics.