Xin Liu

Xin Liu is an Associate Professor in the Department of Astronomy at the University of Illinois Urbana-Champaign and holds a joint appointment at the National Center for Supercomputing Applications (NCSA). Her research bridges astronomy and data science, focusing on multi-messenger astrophysics, time-domain phenomena, and AI-driven scientific discovery through large-scale astronomical surveys.

Her educational background includes:

• Ph.D. in Astrophysical Sciences, Princeton University (2010)
• M.A. in Astrophysical Sciences, Princeton University (2008)
• M.S. in Physics, Tsinghua University (2006)
• B.S. in Physics, Tsinghua University (2004)

Liu's research centers on three interconnected domains: physics-informed machine learning for astronomical data, statistical learning with probabilistic frameworks for uncertainty quantification, and transparent AI models for scientific interpretation. She pioneers methodologies where machine learning integrates with principled astrophysical analysis rather than serving as black-box tools, addressing challenges in big data astronomy through innovative computational approaches.

Her recent publications reveal a strong emphasis on dual supermassive black hole systems, active galactic nuclei variability, and time-domain astrophysics. Key themes include leveraging JWST and multi-wavelength observations to identify dual quasars, developing machine learning techniques for survey data analysis, and exploring the connection between black hole growth and galaxy evolution during cosmic noon. Her work frequently combines large datasets from SDSS, Rubin Observatory, and space telescopes with advanced computational methods.

Her awards and honors include:

• Norman P. Jones Professorial Scholar (2023-2026)
• Excellent Teacher Ranked by Students (2023)
• NCSA Faculty Fellow (2020, 2023)

Liu has secured prestigious fellowships including the NASA Einstein Fellowship and Hubble Fellowship. She teaches advanced courses like ASTR 596 (AI and Big Data in Astronomy) and has received recognition for her teaching excellence. Her research receives support through institutional appointments and competitive fellowships enabling high-impact work in data-intensive astrophysics.

As an NCSA Faculty Fellow, Liu leverages world-class supercomputing resources to develop and apply machine learning frameworks for astronomical datasets. Her group fosters interdisciplinary collaboration between astronomy, computer science, and statistics, focusing on creating interpretable AI tools that advance fundamental astrophysical understanding while pushing computational boundaries.