Jialin Liu
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Jialin Liu

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Assistant Professor (AI Initiative @ UCF)
Department of Statistics & Data Science
University of Central Florida (UCF), Orlando, FL
Email: jialin.liu@ucf.edu
Google Scholar Page    Github Page

Educational and Working Experiences

  • Department of Statistics and Data Science, UCF (Aug. 2024 - Present)
    Assistant Professor

  • Decision Intelligence Lab, Alibaba DAMO Academy (Jul. 2020 - Aug. 2024)
    Senior Algorithm Engineer

  • Department of Mathematics, UCLA (Aug. 2015 - Jun. 2020)
    Ph.D in Applied Math

  • Department of Automation, Tsinghua University (Aug. 2011 - July, 2015)
    B.E. in Automation

Awards & Honors

  • UCLA Mathematics Graduate Research Presentation Prize, 2020.

  • Top rated paper (2/1579 submissions), International Conference on Learning Representations (ICLR) 2019.

  • Best Student Paper Award, IEEE International Conference on Image Processing (ICIP) 2017.

Research Interests and Selected Papers

My research focuses on the intersection of mathematics and artificial intelligence (AI), with a particular emphasis on applying AI to computational mathematical problems such as optimization, differential equations, and numerical linear algebra. While AI and data science have shown significant potential in these areas, a systematic and fundamental understanding of such approaches is still lacking. There is an urgent need to develop stable, safe, and explainable data-driven methods for mathematical applications. The long-term goal of my research is to establish systematic and reliable methodologies, along with the necessary theoretical foundations, for integrating AI into mathematics and science.

Some selected papers:

  • (With X. Chen, Z. Wang, W. Yin, and H. Cai). “Towards Constituting Mathematical Structures for Learning to Optimize.” International Conference on Machine Learning (ICML), 2023. (pdf)

  • (With Z. Chen, X. Wang, J. Lu, and W. Yin). “On Representing Linear Programs by Graph Neural Networks.” International Conference on Learning Representations (ICLR), 2023. (pdf, software, Spotlight paper)

  • (With Z. Chen, X. Wang, J. Lu, and W. Yin). “On Representing Mixed-Integer Linear Programs by Graph Neural Networks.” International Conference on Learning Representations (ICLR), 2023. (pdf, software)

  • (With X. Chen, Z. Wang, and W. Yin). “ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA.” International Conference on Learning Representations (ICLR), 2019. (pdf, poster, software, Top-rated paper)