Shuai Zhang

Assistant Professor, Data Science
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
Website
https://inchs708.github.io/shuaizhang.github.io/index.html
2026 Fall Courses
DS 488 - INDEPENDENT STUDY IN DS
DS 726 - INDEPENDENT STUDY II
DS 675 - MACHINE LEARNING
DS 700B - MASTER'S PROJECT
DS 725 - INDEPENDENT STUDY I
CS 700B - MASTER'S PROJECT
CS 701B - MASTER'S THESIS
DS 701C - MASTER'S THESIS
DS 790A - DOCT DISSERTATION & RES
CS 488 - INDEPENDENT STUDY IN CS
CS 790A - DOCT DISSERTATION & RES
DS 792B - PRE-DOCTORAL RESEARCH
CS 489 - COMPUTER SCIENCE RESEARCH PROJ
CS 726 - INDEPENDENT STUDY II
CS 792 - PRE-DOCTORAL RESEARCH
DS 400 - FOUNDATION OF ML
CS 725 - INDEPENDENT STUDY I
DS 701B - MASTER'S THESIS
Past Courses
CS 732: ADVANCED MACHINE LEARNING
DS 400: SCIENTIFIC FOUNDATION OF MACHINE LEARNING
DS 675: MACHINE LEARNING
Research Interests
My research focuses on the learning theory of emerging artificial intelligence technologies and the structural properties of modern models, with an emphasis on understanding their implications for training dynamics, optimization, and generalization. My research aims to develop principled theoretical frameworks that explain how architectural structure, optimization geometry, and data properties interact to shape the performance, efficiency, and reliability of AI systems.
Conference Paper
"A Theoretical Analysis of Mamba’s Training Dynamics: Filtering Relevant Features for Generalization in State Space Models"
ICLR 2026.
"Theoretical Analysis of Contrastive Learning under Imbalanced Data: From Training Dynamics to a Pruning Solution"
ICLR 2026.
"Theoretical Guarantees and Training Dynamics of Contrastive Learning: How Misaligned Data Influence Feature Purity"
2025.
"When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers"
2025.
"Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning"
ICML 2024, 2024.
"On the Convergence and Sample Complexity Analysis of Deep Q-Networks with $$\backslash$epsilon $-Greedy Exploration"
NeurIPS, 2023, 2023.
Journal Article
Shusen Jing, Anlan Yu, Shuai Zhang, Songyang Zhang. 2024. "FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-iid Data." ICML, 2024.
Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, Meng Wang. 2024. "SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning." ICML, 2024.
Shuai Zhang, Meng Wang. 2019. "Correction of corrupted columns through fast robust Hankel matrix completion." IEEE Transactions on Signal Processing, vol. 67, no. 10, pp. 2580--2594.
Shuai Zhang, Yingshuai Hao, Meng Wang, Joe H Chow. 2018. "Multichannel Hankel matrix completion through nonconvex optimization." IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 4, pp. 617--632.