Professor Luo, Ping

PhD CUHK
Assistant Director (Outreach and Advancement) of CDS;
Associate Director (Innovation and outreach) of Musketers Foundation Institute of Data Science; Associate Professor

Tel: (+852) 2859 2190
Fax: (+852) 2559 8447
Email: pluo@cs.hku.hk
Homepage: http://luoping.me
 

Google Scholar

I am recruiting a number of Postdocs, PhDs, and RAs. Please drop me an email via pluo.lhi@gmail.com. See details.

Ping Luo's researches aim at 1) developing Differentiable/ Meta/ Reinforcement Learning algorithms that endow machines and devices to solve complex tasks with larger autonomy, 2) understanding foundations of deep learning algorithms, and 3) enabling applications in Computer Vision and Artificial Intelligence.

Ping Luo received his PhD degree in 2014 in Information Engineering, the Chinese University of Hong Kong (CUHK), supervised by Prof. Xiaoou Tang (founder of SenseTime Group Ltd.) and Prof. Xiaogang Wang. He was a Research Director in SenseTime Research. He has published 70+ peer-reviewed articles (including 20 first author papers) in top-tier conferences and journals such as TPAMI, IJCV, ICML, ICLR, NeurIPS and CVPR. He has won a number of competitions and awards such as the first runner up in 2014 ImageNet ILSVRC Challenge, the first place in 2017 DAVIS Challenge on Video Object Segmentation, Gold medal in 2017 Youtube‐8M Video Classification Challenge, the first place in 2018 Drivable Area Segmentation Challenge for Autonomous Driving, 2011 HK PhD Fellow Award, and 2013 Microsoft Research Fellow Award (ten PhDs in Asia).

Research Interests

Computer Vision, Machine Learning, Deep Learning

Selected Journal Articles:

• P. Luo, J. Ren, Z. Peng, R. Zhang, J. Li, “Differentiable Switchable Normalization for learning-to-normalize deep representation”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019 
• Z. Liu, X. Li, P. Luo*, C. C. Loy, X. Tang, “Deep Learning Markov Random Field for Semantic Segmentation ”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018 (*corresponding author)
• Z. Zhang, P. Luo*, C. C. Loy, and X. Tang, “Learning Deep Representation for Face Alignment with Auxiliary Attributes”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), No. 05 - May (vol. 38), 2016 (*corresponding author)
• S. Yang, P. Luo, C. C. Loy, and X. Tang, “Faceness-Net: Face Detection through Deep Facial Part Responses”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018
• Z. Zhang, P. Luo, C. C. Loy, and X. Tang, “From Facial Expression Recognition to Interpersonal Relation Prediction”, International Journal of Computer Vision (IJCV), 2018
• W. Ouyang, X. Zeng, X. Wang, S. Qiu, P. Luo et al., “DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), No. 07 - July (vol. 39), 2017

Selected Conferences:

• P. Luo, Z. Peng, W. Shao, R. Zhang, J. Ren, and L. Wu, “Differentiable Dynamic Normalization for Learning Deep Representation”, International Conference on Machine Learning (ICML), 2019, Oral
• P. Luo, W. Shao, X. Wang, Z. Peng, “Towards Understanding Regularization in Batch Normalization”, International Conference on Learning Representation (ICLR), 2019
• P. Luo, J. Ren, Z. Peng, R. Zhang, J. Li, “Differentiable learning-to-normalize via switchable normalization”, International Conference on Learning Representation (ICLR), 2019
• P. Luo, G. Wang, L. Lin, X. Wang, “Deep Dual Learning for Semantic Image Segmentation”, IEEE International Conference on Computer Vision (ICCV), 2017
• P. Luo, “Learning Deep Architectures via Generalized Whitened Neural Networks”, Thirty-fourth International Conference on Machine Learning (ICML), 2017, Oral
• P. Luo, “EigenNet: Towards Fast and Structural Learning of Deep Neural Networks”, International Joint Conference on Artificial Intelligence (IJCAI), 2017, Oral