折 闪 电

Shandian Zhe

Assistant Professor

School of Computing, University of Utah

Office: Room 3466, MEB building

Address: 50 Central Campus Dr #3190, School of Computing, University of Utah, Salt Lake City, UT 84112

I obtained the Ph.D. degree from the Computer Science Department of Purdue University in 2017.

My research interests mainly lie in probabilistic machine learning. That is, using probabilistic frameworks to formulate learning problems and to infer/estimate model parameters. My research include but are not limited to probabilistic graphical models, Bayesian deep learning, Bayesian nonparametric, physics informed machine learning, approximate inference, sparse learning, large-scale machine learning and kernel methods. From application side, I have developed probabilistic methods for microarray data analysis, association study for Alzheimer's disease, functional magnetic resonance imaging (fMRI) data analysis, and click-through-rate prediction for online advertising.

Email: zhe at cs.utah.edu

Curriculum Vitae

Post-Doc Position

I am looking for a postdoc, expected to start soon. The research will be mainly on physics informed machine learning. Machine learning and data science background are preferred. The posting is Here. Feel free to contact me if you are interested.

Graduate students

I am looking for highly motivated students with solid background in probability, linear algebra and/or optimization, and good programming skills. If you are interested in joining my group, welcome to contact me via the email address above. Please attach your CV in the email. In particular, if you have a strong math and/or physics background (e.g., with a BS degree in math or physics) and also have coding experiences, e.g., matlab and python), feel free to schedule a meeting with me.


Group

  • Undergraduate students: Raheem Nimnicht, Yiming Zheng, David L Randall
  • Master students: Yishuai Du
  • Ph.D. students:
  • Postdoctoral fellow: Wei Xing (coadvised with Prof. Mike Kirby )
  • Group Seminar

    Teaching

  • CS 6190 Probabilistic Machine Learning, Spring 2024
  • CS 6190 Probabilistic Modeling, Fall 2019, Spring 2022, 2023
  • CS 5350/6350 Machine Learning, Spring 2018, 2019, 2020, 2021, Fall 2021, 2022, 2023
  • CS 3190 Foundation of Data Analysis, Fall 2018
  • CS 7941 Data Group Seminar, Fall 2018, 2019
  • News

    Year 2023

    Year 2022

    Grants

    Publications

    Alumini

    Source Code

    Professional Serverices

    Past Award

  • AAAI/SIGAI 2017 Doctoral Consortium Scholarship
  • Google PhD Fellowship for Machine Learning 2016
  • AAAI 2015 Outstanding Student Paper Honorable Mention
  • Pacific Symposium on Biocomputing 2014 travel award