Shangqian Gao

I am an Assistant Professor in the Department of Computer Science at Florida State University. Before joining FSU, I worked as a Research Scientist at Samsung Research America, where I focused on enhancing the efficiency of Large Language Models. I earned my Ph.D. in Electrical and Computer Engineering from the Swanson School of Engineering at the University of Pittsburgh in 2024, under the supervision of Prof. Heng Huang. Before that, I obtained my M.S. degree in Computer Systems Engineering from Northeastern University in 2017 and my B.S. degree in Electronic Engineering from Xidian University in 2015.

Multiple PhD openings are available in AI/ML at Florida State University. I am actively looking for highly self-motivated PhD students to work with me in areas related to improving the training and/or inference efficiency for large language models, diffusion models, and other models. Master/undergraduate students and remote interns are also welcome. To apply, please send me an email with your C.V. and transcripts to sgao@cs.fsu.edu.

My research interests includes:

• Efficient Machine Learning: Model compression for Deep Neural Networks (DNNs) and Large Language Models (LLMs). Differentiable Neural Architecture Search. [CVPR20, CVPR21, ICCV21, ECCV22, ICCV23, AAAI23]
• Efficient and Safe Cross-Modal Learning: Adversarial attack and defense on cross-modal datas. Efficient vision and language transformers and cross-modal DNNs. [CVPR19, NeurIPS19, ICCV21, ICLR23]
• Policy Gradient Methods for Reinforcement Learning: Variance-reduced policy gradient methods based on momentum techniques and mirror descent. [ICML20, ICLR22]
• Zeroth-order optimization methods and other topics for optimization. [JMLR, TPAMI, NeurIPS22] Fairness in deep learning and interpretation guided models. [ECCV22a, ECCV22b]