• Ph.D. in Computer Engineering, Clemson University
• M.S. in Computer and Information Sciences, University of Delaware
• B.S. in Software Engineering, Wuhan Institute of Technology

Gen Li is an Assistant Professor in the Department of Computer Science at the University of Houston. He received his Ph.D. in Electrical and Computer Engineering from Clemson University in 2026. His research focuses on efficient and trustworthy machine learning, with particular interests in efficient training and inference for large-scale models, model compression and sparsity, secure and robust AI systems, and safe generative models. His work develops algorithmic and system-level innovations that make modern machine learning models more computationally scalable, resilient to real-world challenges, and aligned with safety and ethical principles. His research has been published in leading conferences, including CVPR, NeurIPS, ICLR, ICML, ECCV, ICCV, ACM CCS, and IEEE S&P.

• CVPR 2023 Highlight Paper, selected among the top 2.5% of accepted papers
  Spotlight Paper Award, ICLR 2023 Sparsity in Neural Networks (SNN) Workshop

• “Sculpting Memory: Multi-Concept Forgetting in Diffusion Models via Dynamic Mask and Concept-Aware Optimization.” IEEE/CVF International Conference on Computer Vision (ICCV), 2025.
• “Adversarial Robust ViT-based Automatic Modulation Recognition in Practical Deep Learning-based Wireless Systems.” IEEE Symposium on Security and Privacy (S&P), 2025.
• “Outlier Weighed Layerwise Sparsity: A Missing Secret Sauce for Pruning LLMs to High Sparsity.” International Conference on Machine Learning (ICML), 2024.
• “NeurRev: Train Better Sparse Neural Network Practically via Neuron Revitalization.” International Conference on Learning Representations (ICLR), 2024.
• “Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting.” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. Highlight Paper.