- Senior Area Chair, Conference on Neural Information Processing Systems (NeurIPS), 2023-present
- Senior Area Chair, International Conference on Machine Learning (ICML), 2024-present
- Action Editor, Transactions on Machine Learning Research, 2024-present
Biography
Prof. Yu Cheng joins the Chinese University of Hong Kong in 2024. From 2018-2023, he was a Principal Researcher at Microsoft Research Redmond. Before that, He was a Research Staff Member at MIT-IBM Watson AI Lab. Prof. Cheng’s research interests specialize in model compression & efficiency, deep generative models, and large multimodal/language models. From 2021 to 2023, he led several teams to productize these techniques for Microsoft-OpenAI core models (Copilot, DALL-E-2, ChatGPT, GPT-4). Prof. Cheng serves as a Senior Area Chair for NeurIPS and ICML and as an Area Chair for CVPR, ICLR, ACL, NAACL, and EMNLP. He is also the Action Editor for Transactions on Machine Learning Research (TMLR). His papers have won the Cybersecurity Best Paper 2024, the Outstanding Paper Award in NeurIPS 2023, the Best Student Paper Honorable Mention in WACV 2021, and the Best Paper Finalist in SDM 2015. He is an affiliate professor/faculty at Tsinghua University, Shanghai Jiao Tong University, Fudan University, Zhejiang University, University of Science and Technology of China, and Tongji University.
- Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, Jingjing Liu: Uniter: Universal Image-text Representation Learning. European Conference on Computer Vision (ECCV), 2022
- Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang: EnlightenGAN: Deep Light Enhancement without Paired Supervision. IEEE Transactions on Image Processing (TIP), 2021
- Siqi Sun, Yu Cheng, Zhe Gan, Jingjing Liu: Patient Knowledge Distillation for BERT Model Compression. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019
- Yu Cheng, Duo Wang, Pan Zhou, Tao Zhang: Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges. IEEE Signal Processing Magazine, 2018
- Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, Barnabás Póczos: MMD GAN: Towards Deeper Understanding of Moment Matching Network. Conference on Neural Information Processing Systems (NeurIPS), 2017
- Best Machine Learning and Security Paper in Cybersecurity Award, 2024.
- Outstanding Paper Award in NeurIPS, 2023.
- Best Student Paper Honourable Mention in WACV, 2021.
- Best Paper Candidate Award of SDM, 2015.