Shuai Zhang
Shuai Zhang
Assistant Professor, Data Science
2117 Guttenberg Information Technologies Center (GITC)
About Me
I am an assistant professor in the Department of Data Science, New Jersey Institute of Technology (NJIT), starting at Fall 2023. I earned my Ph.D. in Electrical and Computer Engineering from Rensselaer Polytechnic Institute under the supervision of Dr. Meng Wang. Previously, I was fortunately to work with IBM Thomas J. Watson Research Center and MIT-IBM Watson AI Lab.
Education
Ph.D.; Rensselaer Polytechnic Institute; Electrical and Computer Engineering; 2021
B.E.; University of Science and Technology of China; ; 2016
B.E.; University of Science and Technology of China; ; 2016
2024 Fall Courses
DS 726 - INDEPENDENT STUDY II
DS 725 - INDEPENDENT STUDY I
CS 732 - ADVANCED MACHINE LEARNING
DS 790A - DOCT DISSERTATION & RES
DS 792B - PRE-DOCTORAL RESEARCH
DS 725 - INDEPENDENT STUDY I
CS 732 - ADVANCED MACHINE LEARNING
DS 790A - DOCT DISSERTATION & RES
DS 792B - PRE-DOCTORAL RESEARCH
Past Courses
CS 732: ADVANCED MACHINE LEARNING
DS 675: MACHINE LEARNING
DS 675: MACHINE LEARNING
Research Interests
My research has been focused on the theoretical foundations of deep learning and the design of principled and fast algorithms for better, safer, and more efficient AI applications. My current research focuses on the theoretical foundation of foundation models and parameter-efficient transfer learning.
Conference Paper
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
ICLR, January (1st Quarter/Winter) 2023
How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
ICLR, 2022
Why lottery ticket wins? a theoretical perspective of sample complexity on sparse neural networks
Advances in Neural Information Processing Systems (NeurIPS), 2021
Fast learning of graph neural networks with guaranteed generalizability: one-hidden-layer case
International Conference on Machine Learning (ICML), 2020
ICLR, January (1st Quarter/Winter) 2023
How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
ICLR, 2022
Why lottery ticket wins? a theoretical perspective of sample complexity on sparse neural networks
Advances in Neural Information Processing Systems (NeurIPS), 2021
Fast learning of graph neural networks with guaranteed generalizability: one-hidden-layer case
International Conference on Machine Learning (ICML), 2020