Academic Profile

Dr Yu, Han is a Nanyang Assistant Professor (NAP) in the School of Computer Science and Engineering (SCSE), Nanyang Technological University (NTU), Singapore. He has been a Visiting Scholar at the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST) from 2017 to 2018. Between 2015 and 2018, he held the prestigious Lee Kuan Yew Post-Doctoral Fellowship (LKY PDF) at the Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY). Before joining NTU, he worked as an Embedded Software Engineer at Hewlett-Packard (HP) Pte Ltd, Singapore. He obtained his PhD from the School of Computer Science and Engineering, NTU in 2014. During his PhD study, he held the prestigious Singapore Millennium Foundation (SMF) PhD Scholarship. His research focuses on online convex optimization, ethical AI, federated machine learning and their applications in complex collaborative systems such as crowdsourcing. He has published over 150 research papers in book chapters, leading international conferences and journals including AAAI, IJCAI, CVPR, SIGIR, ASE, ACM MM, CIKM, AAMAS, NAACL, and ACM/IEEE Transactions. He co-authored the book "Federated Learning" - the first monograph on the topic of federated learning. His research work has been recognized with 24 awards from conferences and journals. He is a founding Co-PI of the Trustworthy Federated Ubiquitous Learning (TrustFUL) Research Lab (https://trustful.federated-learning.org/).
han.yu_1_2.JPG picture
Asst Prof Yu Han
Nanyang Assistant Professor, School of Computer Science and Engineering

Crowdsourcing and Human Computation
Ethical Artificial Intelligence (AI)
Federated Learning
 
  • ADL+: A Digital Toolkit For Cognitive Assessment And Intervention

  • AI-Powered Crowd-computing

  • Next-Generation Brain-Computer-Brain Platform – A Holistic Solution for the Restoration & Enhancement of Brain Functions (NOURISH)

  • TrustFUL: Trustworthy Federated Ubiquitous Learning

  • TrustFUL: Trustworthy Federated Ubiquitous Learning (SCSE)

  • TrustFUL: Trustworthy Federated Ubiquitous Learning (WeBank)
 
  • Zelei Liu, Yuanyuan Chen, Yansong Zhao, Han Yu, Yang Liu, Renyi Bao, Jinpeng Jiang, Zaiqing Nie, Qian Xu & Qiang Yang. (2022). CAreFL: Contribution-Aware Federated Learning for Smart Healthcare. In Proceedings of the 34th Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-22). (Innovative Applications of AI Award)

  • Zelei Liu, Yuanyuan Chen, Han Yu, Yang Liu & Lizhen Cui. (2022). GTG-Shapley: Efficient and accurate participant contribution evaluation in federated learning. ACM Transactions on Intelligent Systems and Technology.

  • Qiang Yang, Lixin Fan & Han Yu. (Ed.). (2020). Federated Learning: Privacy and Incentive. Springer International Publishing.

  • Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin, Han Yu & Kee Siong Ng. (2020). Towards fair and privacy-preserving federated deep models. IEEE Transactions on Parallel and Distributed Systems, 31(11), 2524–2541.

  • Qiang Yang, Yang Liu, Yong Cheng, Yan, Kang, Tianjian Chen & Han Yu. (2019). Federated Learning. Morgan & Claypool Publishers.