Research Interest

My research focuses on adaptive artificial intelligence, aiming to build intelligent systems that can continuously learn, remain safe and trustworthy, and autonomously adapt in dynamic, open-ended environments. My work centers on neural network training under data-scarce and non-stationary distribution conditions, with an emphasis on continual learning, generalization enhancement, and anti-forgetting mechanisms. I also explore efficient training frameworks grounded in uncertainty modeling and adaptive learning to improve the generalization, robustness, safety, and reliability of AI models in complex, dynamic settings.

Furthermore, I have long been dedicated to trustworthy intelligence in foundation models. My research investigates the safety, reliability, and adaptive reasoning of large language models (LLMs), multimodal LLMs, and diffusion language models. I have proposed efficient algorithms addressing critical challenges such as model hallucination, adaptive decoding, efficient inference, and model safety, providing theoretical foundations and technical support for building the next generation of trustworthy, efficient, and adaptive foundation models.

Additionally, I have applied these theoretical advances to EEG signal analysis, effectively tackling key challenges in the field, including insufficient robustness against interference, a severe lack of annotated data, and non-stationary data distributions. My research has been published in over 40 papers in top-tier computer science conferences and journals, including NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, TMLR, and TPAMI.

Education

State University of New York at Buffalo, USA September 2018 - June 2023

PhD in computer science and engineering

Western University, Canada September 2016 - May 2018

M.Sc. in Computer Science

Northeastern University, China  2011  2015

B. E. Digital Media Technology

Work Experience

Tenure Track Assistant Professor in Computer Science, University of Central Florida, USA

August 2025 – June 2026

Postdoctoral Associate, University of Maryland, College Park, USA

Supervisor: Prof. Heng Huang August 2023 – August 2025

Meta Reality lab (Pittsburgh), research scientist intern August 2022 – December 2022

Amazon (Seattle, Washington), applied scientist research intern September 2020 – December 2020.

JD.com AI lab (Mountain View, CA), NLP research intern June 2020 – August 2020

Tencent AI lab (Seattle, Washington), NLP research intern June 2019 – August 2019

Research Grant

2025, Science Fund Program for Excellent Young Scientists (Overseas), PI

Publication

(* indicates equal contribution, # indicates corresponding author)

1. CoRe: Context-Robust Remasking for Diffusion Language Models

Kevin Zhai, Sabbir Mollah, Zhenyi Wang, Mubarak Shah

International Conference on Machine Learning (ICML), 2026

2. AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective

Zhenyi Wang, Siyu Luan

Transactions on Machine Learning Research (TMLR), 2026

3. Pixel-Perfect Puppetry: Precision-Guided Enhancement for Face Image and Video Editing

Yan Li, Zhenyi Wang, Guanghao Li, Wei Xue, Yike Guo, Wenhan Luo

International Conference on Learning Representations (ICLR), 2026

4. Adaptive Defense against Harmful Fine-Tuning via Bayesian Data Scheduler (Spotlight)

Zixuan Hu, Li Shen, Zhenyi Wang, Yongxian Wei, Dacheng Tao

Conference on Neural Information Processing Systems (NeurIPS), 2025

5. Dynamic Neural Fortresses: An Adaptive Shield for Model Extraction Defense

Siyu Luan∗, Zhenyi Wang∗#, Li Shen, Zonghua Gu, Chao Wu, Dacheng Tao

International Conference on Learning Representations (ICLR), 2025

6. Open-Vocabulary Customization from CLIP via Data-Free Knowledge Distillation (Oral)

Yongxian Wei, Zixuan Hu, Li Shen, Zhenyi Wang, Chun Yuan, Dacheng Tao

International Conference on Learning Representations (ICLR), 2025

7. Defense against Model Extraction Attack by Bayesian Active Watermarking

Zhenyi Wang, Yihan Wu, Heng Huang

International Conference on Machine Learning (ICML), 2024

8. Representation Surgery for Multi-Task Model Merging

Enneng Yang, Li Shen, Zhenyi Wang, Guibing Guo, Xiaojun Chen, Xingwei Wang, Dacheng

Tao

International Conference on Machine Learning (ICML), 2024

9. A Unified and General Framework for Continual Learning

Zhenyi Wang, Yan Li, Li Shen, Heng Huang

International Conference on Learning Representations (ICLR), 2024

10. Improving Non-Transferable Representation Learning by Harnessing Content and Style (Spotlight)

Ziming Hong, Zhenyi Wang, Li Shen, Yu Yao, Zhuo Huang, Shiming Chen, Chuanwu Yang,

Mingming Gong, Tongliang Liu

International Conference on Learning Representations (ICLR), 2024

11. AdaMerging: Adaptive Model Merging for Multi-Task Learning

Enneng Yang, Zhenyi Wang, Li Shen, Shiwei Liu, Guibing Guo, Xingwei Wang, Dacheng Tao

International Conference on Learning Representations (ICLR), 2024

12. Defending against Data-Free Model Extraction by Distributionally Robust Defensive Training

Zhenyi Wang, Li Shen, Tongliang Liu, Tiehang Duan, Yanjun Zhu, Donglin Zhan, David Doer-

mann, Mingchen Gao

Conference on Neural Information Processing Systems (NeurIPS), 2023

13. Distributionally Robust Memory Evolution with Generalized Divergence for Continual Learning

Zhenyi Wang, Li Shen, Tiehang Duan, Qiuling Suo, Le Fang, Wei Liu, Mingchen Gao

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023

14. Improving Task-free Continual Learning by Distributionally Robust Memory Evolution

Zhenyi Wang, Li Shen, Le Fang, Qiuling Suo, Tiehang Duan, Mingchen Gao

International Conference on Machine Learning (ICML), 2022

15. Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints

Zhenyi Wang, Xiaoyang Wang, Bang An, Dong Yu, Changyou Chen

Annual Conference of the Association for Computational Linguistics (ACL), 2020