With the launch of a flagship observatory, new parameter spaces in the populations of galaxies and active galactic nuclei are almost always uncovered — regions that push beyond the limits that existing models can address. Expanding these boundaries by building new models and tools to interpret the data is the central theme of my research program.

I have pioneered a method that directly encodes data-driven constraints on galaxy evolution into the inference process (Prospector-β), which has proven to be instrumental in maximizing the scientific returns from JWST surveys. Building on this, I have identified some of the most distant galaxies known to date. More recently, I've been focused on characterizing the population of compact, red sources known as ``little red dots" (LRD) -- a discovery and a puzzle in the JWST era. I led some of the earliest spectroscopic studies, including an unusually bright LRD lacking hot dust emission, and first NIRSpec observations of high-z LRDs that were initially intrepreted as massive galaxies.

In parallel, I have worked on and share a deep interest in machine-learning-empowered inference techniques and population models for star formation timescales.

My Ph.D. thesis centers on the escape of ionizing radiation and galactic outflows (slides from my AAS dissertation talk). The former concerns the processes in early galaxies that enable a substantial fraction of their Lyman-continuum radiation to escape the dense interstellar medium, driving the cosmic reionization. The latter is on the physical processes governing starburst-driven outflows, which are often invoked as the principal feedback mechanism in models of galaxy formation and evolution.

I have also collaborated on projects on early universe cosmology, such as understanding large-angle anomalies in the Cosmic Microwave Background and developing analysis pipelines for the Cosmology Large Angular Scale Surveyor.

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