Sheng Li's Homepage

Research Interests: Trustworthy Representation Learning (e.g., Robustness, Fairness, Causality, Transferability); Computer Vision; Interdisciplinary Applications (Educational Measurement, Biomedical Informatics, Public Health, 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. Due to a large volume of inquiries, I may not have a chance to reply to every email.

Short Bio: Sheng Li is a Quantitative Foundation Associate Professor of Data Science and an Associate Professor of Computer Science (by courtesy) 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 AI, Causal Inference, Large Foundation Models, and Vision-Language Modeling. He has published over 170 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 currently serves as Associate Editor for six journals such as Transactions on Machine Learning Research (TMLR) and IEEE Trans. Neural Networks and Learning Systems (TNNLS), and serves as an Area Chair for IJCAI, NeurIPS, ICML, and ICLR.

11/2024, Received a DOD DURIP Grant to support our research projects.
11/2024, I accepted the invitations to serve as Action Editor for the Transactions on Machine Learning Research (TMLR) and Area Chair for IJCAI 2025.
11/2024, One paper on individual-level continuous treatment effect estimation is accepted by TMLR.
10/2024, Our article on AI for K-12 education is published at The Virginian-Pilot.
09/2024, One paper on expressive representation learning with vision-language models is accepted at NeurIPS 2024.
09/2024, One paper on tag-grounded visual instruction tuning is accepted at EMNLP 2024.
08/2024, Received an ONR Grant to support our research on causal inference.
08/2024, We are organizing the First Workshop on Large Foundation Models for Educational Assessment at NeurIPS 2024.
08/2024, One paper on mixup virtual adversarial training for robust vision transformers is accepted at IEEE Transactions on Big Data.
07/2024, Received an NSF Grant to support our research on AI for controlled-environment agriculture.
07/2024, One paper on text-guided augmentation for domain generalization is accepted at ECCV 2024.
06/2024, One paper on class-imbalanced domain adaptation has been accepted by IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT).
06/2024, One paper on large language models for food applications is accepted by IEEE Transactions on Big Data.
05/2024, One paper on dual-windowed vision transformer with angular self-attention is accepted by TMLR.
05/2024, Received an NSF I-Corps Grant to explore generative AI for educational assessment.
05/2024, My student Zhongliang Zhou received the Best PhD Dissertation Award from UGA School of Computing. Zhongliang has recently joined Merck as a Senior Scientist.
05/2024, One paper on class-incremental graph learning is accepted by TMLR.
04/2024, I'm invited to write a preview paper on deep learning for causal inference for the Patterns journal.
03/2024, One survey paper on trustworthy representation learning across domains is accepted by ACM TKDD; One demo paper is accepted at SIGIR 2024.
02/2024, I'm appointed as a Quantitative Foundation Associate Professor of Data Science at UVA.
01/2024, One paper on explainable machine learning is accepted by Bioinformatics.
12/2023, Three papers (Open-Set Learning, Causal Inference, and LLM for Recommendation) and one student abstract (BadSAM) are accepted at AAAI 2024; One paper on video anomaly detection is accepted at SDM 2024.
11/2023, Our new book on Machine Learning for Causal Inference has been published by Springer. Many thanks to the contributors of this book!
11/2023, I accepted the invitation to serve as Associate Editor of IEEE Trans. Cognitive and Developmental Systems (T-CDS), as Area Chair for IJCAI 2024 and ICML 2024, and as SPC for PAKDD 2024.
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

Services

Panelist: NSF (III, RI, OAC, NAIRI, CRII, GRFP), CDC
Associate Editor: Transactions on Machine Learning Research (2024 - ), IEEE Trans. Neural Networks and Learning Systems (2022 - ), IEEE Trans. Cognitive and Developmental Systems (2024 - ), IEEE Trans. on Circuits and Systems for Video Technology (2021 - ), IEEE Trans. Consumer Electronics (2024 - ), 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 - 2023), Neural Computing and Applications (2017 - 2023)
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 (2023-2024), IJCAI (2023-2025), NeurIPS (2022-2024), ICLR (2022-2025), ICPR (2020-2022), VCIP (2017)
SPC Member: IJCAI (2020-2021), AAAI (2019-2025), SDM (2023-2025)

Sheng Li

Awards and Honors

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