Di Wang

Email: di.wang@kaust.edu.sa
Website: [中文主页] [PRADA Lab][Google Scholar]
CV: CV (last updated in January, 2022)
Al Khawarizmi Building 1, Room 4243
Division of CEMSE
King Abdullah University of Science and Technology
Thuwal, Saudi Arabia, 23955-6900


I am currently an Assistant Professor of Computer Science and an affiliated faculty of Statistics in the Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) at the King Abdullah University of Science and Technology (KAUST), start from Spring 2021. I am also the PI of Provable Responsible AI and Data Analytics (PRADA) Lab, a member of Computational Bioscience Research Center (CBRC), and an affiliated faculty with the SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence (SDAIA-KAUST AI).

Before that, I got my Ph.D degree in Computer Science at the State University of New York (SUNY) at Buffalo in 2020 under supervision of Dr. Jinhui Xu. Before my Ph.D study I took my Master degree in Mathematics at University of Western Ontario in 2015, and I received my Bachelor degree in Mathematics and Applied Mathematics at Shandong University in 2014.


Opening Positions : I am always looking for visiting scholars, Postdocs, PhD students, (remote) interns, and visiting students. If you are interested in working with me, please send me your CV and transcripts before applying.

News (March 2024) : Our paper "Persistent Local Homology in Graph Learning" has been accepted at Transactions on Machine Learning Research (TMLR)!

News (March 2024) : Our paper "Privacy Amplification via Shuffling: Unified, Simplified, and Tightened" has been accepted at International Conference on Very Large Data Bases (VLDB 2024)!

News (March 2024) : Our paper "Preserving Node-level Privacy in Graph Neural Networks" has been accepted at The 45th IEEE Symposium on Security and Privacy (IEEE S&P 2024)!

News (March 2024) : Our paper "A Multi-classification Division-aggregation Framework for Fake News Detection" has been accepted at IEEE Transactions on Big Data!

News (January 2024) : Two papers have been accepted at The 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL)!

News (January 2024) : Four papers have been accepted at The 12th International Conference on Learning Representations (ICLR)!

News (December 2023) : Our paper "PPML-Omics: a Privacy-Preserving federated Machine Learning System Protects Patients’ Privacy from Omic Data" has been accepted at Science Advances!

News (December 2023) : Our paper "Personalized and Privacy-preserving Federated Heterogeneous Medical Image Analysis with PPPML-HMI" has been accepted at Computers in Biology and Medicine!

News (December 2023) : Our paper "Communication Efficient and Provable Federated Unlearning" has been accepted at International Conference on Very Large Data Bases (VLDB 2024)!

News (November 2023) : Our paper "PAC Learning Halfspaces in Non-interactive Local Differential Privacy Model with Public Unlabeled Data " has been accepted at Journal of Computer and System Sciences (JCSS)!

News (October 2023) : Our paper "Quantizing Heavy-tailed Data in Statistical Estimation:(Near) Minimax Rates, Covariate Quantization, and Uniform Recovery " has been accepted at IEEE Transactions on Information Theory (TIT)!

News (October 2023) : Our paper "Nearly Optimal Rates of Privacy-preserving Sparse Generalized Eigenvalue Problem" has been accepted at IEEE Transactions on Knowledge and Data Engineering (TKDE)!

News (October 2023) : Our paper "Gradient Complexity and Non-stationary Views of Differentially Private Empirical Risk Minimization" has been accepted at Theoretical Computer Science (TCS)!

News (October 2023) : Our paper "GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings" has been accepted at The First Arabic Natural Language Processing Conference (ArabicNLP 2023)!

News (October 2023) : Two papers have been accepeted at The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)!

Research Areas

I am interested in trustworthy machine learning, large language models and AI for Science. Please see my Publications and Research for details.

  • Responsible Computing: privacy, fairness, robustness, truthfulness, explainability, reproducibility, faithfulness

  • Large Language Models: inference acceleration, knowledge editing, alignment

  • AI for Science: optics, Optical Neural Networks, biomedicine, chemistry

  • Awards

  • Best Paper Award, Asian Conference on Machine Learning 2022

  • Invited to The ACM Transactions on Database Systems special issue on Best of PODS 2022

  • CSE Best Doctoral Dissertation Award in 2020, SUNY at Buffalo

  • SEAS Dean's Graduate Achievement Award in 2019, SUNY at Buffalo

  • Best CSE Graduate Research Award in 2018, SUNY at Buffalo

  • Top