1002 W. Green St.
Prof. Xin Liu received her B.A. in Physics from Tsinghua University in 2004 and her Ph.D in Astrophysical Sciences from Princeton University in 2010 under the guidance of Prof. Michael A. Strauss. Before joining UIUC in 2015, she was a NASA Einstein Fellow at Harvard and a Hubble Fellow at UCLA.
Astronomical survey and data science
Multi-messenger and time-domain astrophysics
Machine learning applications
Exploring the cosmos in the multi-messenger and time domains is a key priority in astronomy and astrophysics for the coming decade. With LIGO fulfilling the long-anticipated promise of gravitational wave astronomy, pulsar timing and the space-based interferometer LISA will open other windows on the gravitational wave spectrum, revealing unknown sources and surprises, much as the first X-ray and radio telescopes did. To realize the full discovery potential in the multi-messenger and time domains, efficient electromagnetic observations and data analysis tools are needed. Capitalizing on the big data transformation and new survey capabilities on the horizon, Prof. Liu's current research is focused on two thematic areas: (i). new windows on the dynamic Universe and multi-messenger astrophysics, and (ii). machine learning applications and data science.
Ph.D., Astrophysical Sciences, Princeton University, 2010
M.A., Astrophysical Sciences, Princeton University, 2008
M.S., Physics, Tsinghua University, 2006
B.S., Physics, Tsinghua University, 2004
Additional Campus Affiliations
Associate Professor, National Center for Supercomputing Applications (NCSA)
Honors & Awards
Norman P. Jones Professorial Scholar, 2023-2026
NCSA Faculty Fellow, 2020 & 2023
Lincoln Excellence for Assistant Professor, 2019
Chen, Y., Liu, X., Foord, A., Shen, Y., Oguri, M., Chen, N., Di Matteo, T., Holgado, M., Hwang, H., & Zakamska, N. (2023). A close quasar pair in a disk–disk galaxy merger at z = 2.17. Nature, 616(7955), 45-49. https://doi.org/10.1038/s41586-023-05766-6
Lin, J. Y. Y., Pandya, S., Pratap, D., Liu, X., Kind, M. C., & Kindratenko, V. (2023). AGNet: Weighing Black Holes with Deep Learning. Monthly Notices of the Royal Astronomical Society, 518(4), 4921-4929. https://doi.org/10.1093/mnras/stac3339
Wang, Z. F., Burke, C. J., Liu, X., & Shen, Y. (2023). Dwarf AGNs from Variability for the Origins of Seeds (DAVOS): Optical Variability of Broad-line Dwarf AGNs from the Zwicky Transient Facility. Monthly Notices of the Royal Astronomical Society, [stad532]. https://doi.org/10.1093/mnras/stad532
Chen, Y. C., Hwang, H. C., Shen, Y., Liu, X., Zakamska, N. L., Yang, Q., & Li, J. I. (2022). Varstrometry for Off-nucleus and Dual Subkiloparsec AGN (VODKA): Hubble Space Telescope Discovers Double Quasars. Astrophysical Journal, 925(2), . https://doi.org/10.3847/1538-4357/ac401b
Foord, A., Liu, X., Gültekin, K., Whitley, K., Shi, F., & Chen, Y. C. (2022). Investigating the Accretion Nature of Binary Supermassive Black Hole Candidate SDSS J025214.67-002813.7. Astrophysical Journal, 927(1), . https://doi.org/10.3847/1538-4357/ac4af1