About Me

I work at the intersection of data-intensive astronomy, AI, and simulation-driven discovery. My research combines large-survey data analysis, physics-based modeling, and machine learning to uncover hidden phenomena in the cosmos and prepare for the Rubin Observatory Legacy Survey of Space and Time (LSST).

My work focuses on understanding how light from stars interacts with interstellar dust, reconstructing the history of cosmic events through light echoes, and developing AI-powered detection pipelines to automatically identify rare and novel transients in vast datasets. I also develop open-source software tools and dashboards for photometric and spectral analysis, survey visualization, and light-echo detection, enabling the astronomical community to explore the dynamic sky more efficiently.

Beyond research, I’m passionate about science outreach and education. I enjoy mentoring students, hosting tutorials, and giving talks to share the excitement of astronomy and AI-driven discovery with both the public and the research community. I also explore creative expressions inspired by the cosmos, turning stellar phenomena and interstellar structures into visual art that bridges science and imagination.

I received my Ph.D. in Physics from the University of Delaware, where my dissertation focused on automating the discovery of rare transients in the era of AI and Rubin LSST. My academic journey combines a strong foundation in physics and astronomy with expertise in machine learning, data analysis, and software development, all aimed at pushing the boundaries of how we explore the universe.

When I’m not analyzing survey data or developing AI models, I enjoy exploring creative projects, connecting with the community through workshops and seminars, and seeking new ways to merge science, art, and technology.