Zhenyi Wang

I’m Zhenyi Wang. I am currently an Assistant Professor of Computer Science at the University of Central Florida (UCF), also a member of UCF’s Artificial Intelligence Initiative. Previously, I was a Postdoctoral Associate at University of Maryland, College Park, working with Prof. Heng Huang. I received my PhD from Department of Computer Science and Engineering at University at Buffalo in 2023, working with Prof. Mingchen Gao.

Research Summary: My long-term research goal is to develop artificial general intelligence (AGI) that is data-efficient, capable of continual learning and trustworthy. Specifically, I have developed algorithms across the following research areas: (1) Biologically and Neuroscience-Inspired Continual Learning. (2) AI safety for responsible AGI, focusing on protecting model intellectual property and ensuring appropriate usage through applicability authorization mechanisms. (3) Data-efficient learning, including limited labeled data learning and data-free multi-task model merge.

Specifically:

• Biologically and Neuroscience-Inspired Continual Learning: (1) Memorization and Generalization Trade-Off [ICML2022], (2) Generalized Distributionally Robust Memory Evolution [TPAMI2023], (3) Low-Rank Memory Parameterization [NeurIPS2023], (4) Beneficial Forgetting: A Biology-Inspired Approach to Continual Learning [ICLR2024], (5) Homeostatic Synaptic Scaling: Old Task and New Task Trade-Off [NeurIPS2024]
• AI Safety and Trustworthy AI: Deep Model Intellectual Property Protection: (1) Model Extraction Aggregation [ICML2023], (2) Sparse Model Inversion [ICML2024], (3) Applicability/Usage Authorization [ICLR2024], (4) Efficient Model Extraction Defense [NeurIPS2023, ICML2024]

• Data-Efficient Learning:

  [1] Limited Labeled Data Learning: (1) Bayesian Meta Sampling for Few-Shot Learning Uncertainty Adaptation [ICLR2020], (2) Neural Tangent Kernel for Inner-Loop-Free Few-Shot Learning [ICLR2021], (3) Few-Shot Learning in Evolving Domains [ICCV2021, CVPR2022]

  [2] Model Merge and Reuse: (1) Adaptive Model Merging [ICLR2024], (2) Representation Surgery [ICML2024], (3) Model Grouping [ICML2024]

Please refer to Google Scholar for more papers

I am looking for Postdocs/PhD/Master/Undergraduate/Intern students with strong self-motivation starting in 2026 Spring or Fall. Please email me your CV and transcripts if you are interested.

News

• November 2024 One paper has been accepted by TPAMI.

• September 2024 One paper has been accepted by NeurIPS 2024.

• September 2024 One paper has been accepted by TPAMI.

• July 2024 Two papers accepted at ECCV 2024.

• May 2024 Four papers accepted at ICML 2024.

• February 2024 One paper accepted at CVPR 2024.

• January 2024 Three papers accepted at ICLR 2024.

• September 2023 Two papers has been accepted by NeurIPS 2023.

• September 2023 One paper has been accepted by TPAMI.