Shuai Zhang
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Assistant Professor |
About me
I joined NJIT from Rensselaer Polytechnic Institute (RPI), where I was a postdoctoral researcher focused on theoretical and algorithmic foundations of deep learning. I received my Ph.D. from the Department of Electrical, Computer, and Systems Engineering (ECSE) at Rensselaer Polytechnic Institute (RPI) in 2021, supervised by Prof. Meng Wang. I received my Bachelor's degree in Electrical Engineering (EE) at the University of Science and Technology of China (USTC) in 2016. My interests span deep learning, optimization, data science, and signal processing, with a particular emphasis on learning theory – the design of machine learning algorithms – as well as the development of efficient and trustworthy AI.
I am always looking for self-motivated students (PhD with RA/TA support, Masters, undergrads) with interests in data science, machine/deep learning, and signal processing.
Interested candidates are strongly encouraged to contact me by sz457 at njit.edu, together with the resume and transcripts.
Research
As AI continues to advance, it is imperative to address several challenges to ensure not only the widespread adoption but also the safe and efficient implementation of this technology. These challenges include the high cost of computation, the limited availability of high-quality labeled data, generalizability across domains, and the lack of transparency – specifically, the absence of explanations for how AI works. My long-term research objective is to study the theoretical foundations of artificial intelligence and design more principled and efficient algorithms for better, safer, and more efficient AI applications. As part of my research, my interests cover the following areas:
Machine/Deep Learning
Learning Theory
High-dimensional Data Analysis
Spatio-temporal Data Analysis
Deep Reinforcement Learning
Probability and Statistical Inference
My current research interests lie in parameter-efficient transfer learning with foundation models and sparse learning, especially for applications involving spatial-temporal data.
Recent news
Sep. 2023: Our paper "On the Convergence and Sample Complexity Analysis of Deep Q-Networks with -Greedy Exploration" is accepted to the NeurIPS 2023!
Aug. 2023: I joined NJIT as an Assistant Professor.
Apr. 2023: One paper has been accepted at ICML 2023.
Jan. 2023: Our paper on Joint Sparse Learning for Graph Neural Networks is accepted to the International Conference on Learning Representations (ICLR) 2023!
Nov. 2022: Invited talk for the Electrical, and Computer Engineering Seminar at Iowa State University.
Mar. 2022: Paper with Hongkang "Learning and generalization of one-hidden-layer neural networks, going beyond standard Gaussian data" is accepted to 56th Annual Conference on Information Sciences and Systems (CISS), 2022.
Jan. 2022: Our paper "How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis" is accepted to the 10th International Conference on Learning Representations (ICLR) 2022!
Dec. 2021: Shuai successfully defended his thesis. Thanks to Drs. Meng Wang, Ali Tajer , John E. Mitchell , and Birsen Yazıcı for serving as the commitee members.
Sep. 2021: Our paper "Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks" is accepted to the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) 2021!
May 2021: Shuai won the Allen B. DuMont Prize from the Department of ECSE at RPI (The award is presented to outstanding doctoral graduates, and two students for the Department of ECSE at 2021).
Jun. 2020: Our paper "Improved Linear Convergence of Training CNNs with Generalizability Guarantees: A One-hidden-layer Case" is accepted to IEEE Transactions on Neural Networks and Learning Systems (TNNLS).
Jun. 2020: Our paper "Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case" is accepted to International Conference on Machine Learning (ICML) 2020.
Sep. 2019: Shuai received Rensselaer's Founders Award of Excellence.
Feb. 2019: Our paper "Correction of Corrupted Columns in Robust Matrix Completion by Exploiting the Hankel Structure" is accepted to IEEE Transactions on Signal Processing (TSP).
Mar. 2018: Our paper "Correction of Simultaneous Bad Measurements by Exploiting the Low-rank Hankel Structure" is accepted to IEEE International Symposium on Information Theory (ISIT) 2018.
Mar. 2018: Paper with Yingshuai "Multi-Channel Hankel Matrix Completion through Nonconvex Optimization" is accepted to IEEE Journal of Selected Topics in Signal Processing (JSTSP)!
Sep. 2017: Paper with Yingshuai "Multi-Channel Missing Data Recovery by Exploiting the Low-rank Hankel Structures" is accepted to IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) 2017.