Sheng Li

Associate Professor, School of Data Science

178 Elson, University of Virginia, Charlottesville, VA 22903
Email: shengli [AT]

Research Interests: Trustworthy Representation Learning (e.g., Robustness, Fairness, Causality, Transferability); Computer Vision; Interdisciplinary Applications (Bioinformatics, Biomedical Informatics, Public Health, Ecology, Education, etc.)

Openings: I am continuously looking for highly-motivated Ph.D. students to work on AI, ML, CV, and causal inference. Please send me your CV if interested.

Short Bio: Sheng Li is an Associate Professor of Data Science at the University of Virginia (UVA). He was an Assistant Professor of Data Science at UVA from 2022 to 2023, an Assistant Professor of Computer Science at the University of Georgia from 2018 to 2022, and a Data Scientist at Adobe Research from 2017 to 2018. He received his PhD degree in Computer Engineering from Northeastern University in 2017 under the supervision of Prof. Yun Raymond Fu. He received his Master degree and Bachelor degree from School of Computer Science at Nanjing University of Posts and Telecommunications in 2012 and 2010, respectively. His recent research interests include trustworthy representation learning, graph neural networks, visual intelligence, and causal inference. He has published over 150 papers, and has received over 10 research awards, such as the INNS Aharon Katzir Young Investigator Award, Fred C. Davidson Early Career Scholar Award, Adobe Data Science Research Award, Cisco Faculty Research Award, and SDM Best Paper Award. He has served as Associate Editor for seven international journals such as IEEE Trans. Neural Networks and Learning Systems (TNNLS) and IEEE Trans. Circuits and Systems for Video Technology (TCSVT), and has served as an Area Chair for IJCAI, NeurIPS, ICML, and ICLR.

News [more...]

  • 10/2023, One paper on semi-supervised single domain generalization is accepted by TMLR.
  • 09/2023, One paper on fish identification is accepted at AJCAI 2023. More information about our project can be found in this news article.
  • 08/2023, Received an NSF Grant to support our research on physics-guided graph neural networks.
  • 08/2023, One paper on the calibration of graph neural networks is accepted at ACM CIKM 2023.
  • 07/2023, One paper is accepted by Medical Physics; One paper is accepted by ACS Nano.
  • 06/2023, I am promoted to Associate Professor with Tenure (effective 08/2023). Many thanks to my students, collaborators, colleagues, and mentors!
  • 06/2023, We gave a tutorial on Recent Advances in Visual Domain Adaptation and Generalization at CVPR 2023.
  • 06/2023, One paper is accepted to The 2023 ICML Workshop on Computational Biology.
  • 05/2023, One paper is accepted by IEEE Transactions on Cognitive and Developmental Systems (TCDS).
  • 04/2023, I am selected to participate in DARPA AI Forward.
  • 04/2023, Two papers on graph-based local clustering and time series forecasting are accepted at IJCAI 2023.
  • 04/2023, We will organize The 11th IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG) at ICCV 2023.
  • 04/2023, I accepted the invitation to serve as Area Chair for NeurIPS 2023.
  • 03/2023, One paper on trustworthy representation learning is accepted to the Blue Sky Idea Track of SDM 2023.
  • 02/2023, One paper on video recognition is accepted at CVPR 2023.
  • 01/2023, Three papers on fair graph learning, continual federated learning, and few-shot transfer learning are accepted at ICLR 2023.
  • 01/2023, One paper is accepted by Bioinformatics; One paper is accepted by JMIR AI.
  • 12/2022, Seven papers are accepted at ICDE 2023, SDM 2023, Briefings in Bioinformatics, AAAI-23 Bridge Program, and ICAART 2023.
  • 12/2022, I accepted the invitation to serve as Area Chair for ICML 2023.
  • 11/2022, I accepted the invitation to serve as Area Chair for IJCAI 2023.
  • 10/2022, One paper on scene graph generation is accepted at WACV 2023; Two papers on visual learning are accepted at IEEE BigData 2022.
  • 09/2022, I will co-organize a tutorial (Machine Learning for Causal Inference) and a workshop (AI for Web Ads) at AAAI 2023.
  • 09/2022, Received the Adobe Data Science Research Award.
  • 09/2022, One paper on causality guided model interpretation is accepted by IEEE Trans. Knowledge and Data Engineering (TKDE).
  • 08/2022, Received a HHS R01 Grant (as Co-I; PI: Prof. Andrea Sikora) to develop AI based health IT tools to assist critical care pharmacists.
  • 08/2022, I accepted the invitations to serve as Tutorial Co-Chair and Senior PC member for SDM 2023, and Area Chair for ICLR 2023.
  • 08/2022, Two papers are accepted at ACM CIKM 2022; One paper is accepted at MICCAI-MLMI 2022.
  • 07/2022, Received an NIH U01 Grant (as Co-I; PI: Prof. Natarajan Kannan) to annotate dark ion-channel functions by language modeling.
  • 06/2022, Received an NIH R01 Supplement Grant (as Co-I; PI: Prof. Donglan Zhang) to examine disparities in ADRD using machine learning.

Awards and Honors

  • Georgia CTSA Team Science Award, 2023
  • Fred C. Davidson Early Career Scholar Award, 2022
  • Best Associate Editor Award, IEEE TCSVT, 2022
  • CS Faculty Teaching Excellence Award, 2021
  • INNS Aharon Katzir Young Investigator Award, 2020
  • CS Faculty Research Excellence Award, 2020
  • M. G. Michael Award, 2020
  • Adobe Data Science Research Award, 2019
  • Baidu Research Fellowship, 2016-2017
  • Chinese Government Award for Outstanding Self-Financed Students Abroad, 2015-2016
  • NEU Outstanding Graduate Student Award, 2014-2015
  • Best Paper Award, SDM 2014
  • Best Paper Award Candidate, ICME 2014
  • Best Student Paper Honorable Mention Award, FG 2013


  • Panelist: NSF (III, OAC, NAIRI, CRII, GRFP), CDC
  • Associate Editor: IEEE Trans. Neural Networks and Learning Systems (2022 - ), IEEE Transactions on Circuits and Systems for Video Technology (2021 - ), IEEE Computational Intelligence Magazine (2019 - ), Neurocomputing (2017 - 2022), Journal of Electronic Imaging (2018 - 2022), IET Image Processing (2017 - 2020)
  • Editorial Board Member: Frontiers in Signal Processing (2021 - ), Neural Computing and Applications (2017 - )
  • Program Chair: CVPR-AMFG (2021), IJCAI-Tusion (2020), IJCAI-Tusion (2019), CVPR-AMFG (2019)
  • Tutorial Co-Chair: SDM (2023)
  • Publicity Chair: ICMLA (2016), TCMFTL (2016), AMFG (2015-2017)
  • Area Chair: ICML (2023), IJCAI (2023), NeurIPS (2022-2023), ICLR (2022-2024), ICPR (2020-2022), VCIP (2017)
  • SPC Member: IJCAI (2020-2021), AAAI (2019-2022), SDM (2023)