Shangqian Gao

I am now a final year Ph.D. candidate in ECE, Swanson School of Engineering, University of Pittsburgh, supervised by Prof. Heng Huang. I am also a Research Scientist at Samsung Research America. Prior to that, I obtained my M.S. degree in Computer System Engineering at Northeastern University in 2017, and I obtained my B.S. degree in Electronic Engineering at 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 shg84@pitt.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]