Shengjie Wang (王圣杰)

Shengjie Wang is an assistant professor at NYU Shanghai since Fall 2023. He received his Ph.D. in Computer Science and Engineering from the University of Washington, where he was advised by Jeffrey Bilmes. He earned his bachelor's degree from the University of Illinois at Urbana-Champaign. His research interests include machine learning, encompassing areas such as deep learning, AI for science, submodular optimization, computer vision, and NLP.

Personal website: https://sheng-jie-wang.github.io/

Select Publications

• Machine Learning Force Fields with Data Cost Aware Training. A. Bukharin, T. Liu, S. Wang, S. Zuo, W. Gao, W. Yan, T. Zhao. ICML23

• Constrained Robust Submodular Partitioning. S Wang*, T Zhou*, C Lavania, J Bilmes. NeurIPS21

• Robust Curriculum Learning: from clean label detection to noisy label self-correction.T Zhou*, S Wang*, J Bilmes. ICLR21

• Bias also matters: Bias attribution for deep neural network explanation. S Wang*, T Zhou*, J Bilmes. ICML19

• Analysis of deep neural networks with extended data Jacobian matrix. S Wang, A Mohamed, R Caruana, J Bilmes, M Plilipose, M Richardson, K Geras, G Urban, O Aslan. ICML16