1002 W. Green St.
Professor Liu received her B.A. and M.S. in Physics from Tsinghua University in 2004 and 2006 and her M.A. and Ph.D in Astrophysical Sciences from Princeton University in 2008 and 2010. After that she was a NASA Einstein fellow at Harvard University and then a Hubble fellow at the University of California, Los Angeles before joining UIUC as an assistant professor in 2015. Her research interests include astronomical surveys and data science, origin and cosmic evolution of galaxies and galactic nuclei, and the nature of black holes and gravity.
Astronomical survey and data science
Origin and cosmic evolution of galaxies and galactic nuclei
The nature of black holes and gravity
Machine learning applications in Astronomy
Professor Liu studies supermassive black holes that live in the hearts of galaxies across the universe. Understanding how black holes formed and evolved into their present forms has been a central theme in modern astronomy. The next decade will see an array of new facilities and large surveys aimed at addressing fundamental questions centered around this theme. The Liu group develops new observational tools and data analysis techniques in the context of large astronomical surveys. The goal is to understand black holes' origins, growth, how they may have contributed to the evolution of galaxies, and how we can use them as astrophysical probes for fundamental physics.
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
Awards and Honors
Additional Campus Affiliations
Assistant Professor, National Center for Supercomputing Applications (NCSA)
Guo, H., Liu, X., Tayyaba, Z., & Liao, W. T. (2020). Spectral energy distributions of candidate periodically variable quasars: Testing the binary black hole hypothesis. Monthly Notices of the Royal Astronomical Society, 492(2), 2910-2923. https://doi.org/10.1093/mnras/stz3566
Guo, H., Shen, Y., He, Z., Wang, T., Liu, X., Wang, S., Sun, M., Yang, Q., Kong, M., & Sheng, Z. (2020). Understanding Broad Mg II Variability in Quasars with Photoionization: Implications for Reverberation Mapping and Changing-look Quasars. Astrophysical Journal, 888(2), . https://doi.org/10.3847/1538-4357/ab5db0
Hwang, H. C., Shen, Y., Zakamska, N., & Liu, X. (2020). Varstrometry for Off-nucleus and Dual Subkiloparsec AGN (VODKA): Methodology and Initial Results with Gaia DR2. Astrophysical Journal, 888(2), . https://doi.org/10.3847/1538-4357/ab5c1a
Allen, G., Andreoni, I., Bachelet, E., Berriman, G. B., Bianco, F. B., Biswas, R., Kind, M. C., Chard, K., Cho, M., Cowperthwaite, P. S., Etienne, Z. B., George, D., Gibbs, T., Graham, M., Gropp, W., Gupta, A., Haas, R., Huerta, E. A., Jennings, E., ... Zhao, Z. (2019). Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era.
Burke, C. J., Aleo, P. D., Chen, Y. C., Liu, X., Peterson, J. R., Sembroski, G. H., & Lin, J. Y. Y. (2019). Deblending and classifying astronomical sources with Mask R-CNN deep learning. Monthly Notices of the Royal Astronomical Society, 490(3), 3952-3965. https://doi.org/10.1093/mnras/stz2845