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 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, ASE, ACM MM, AAMAS, CIKM, ACM/IEEE Transactions, Proceedings of the IEEE, as well as Nature Research journals – npj Science of Learning, Scientific Data and Scientific Reports. His research work has been recognized with multiple scientific awards.
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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)
 
  • Qiang Yang, Lixin Fan & Han Yu. (Ed.). (2020). Federated Learning: Privacy and IncentiveSwitzerland: Springer International Publishing.

  • Kang Loon Ng, Zichen Chen, Zelei Liu, Han Yu, Yang Liu & Qiang Yang. (2020). A Multi-player Game for Studying Federated Learning Incentive Schemes. the 29th International Joint Conference on Artificial Intelligence (IJCAI’20).

  • 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.

  • Yang Liu, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Han Yu & Qiang Yang. (2020). In Proceedings of the 32nd AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-20): FedVision: An Online Visual Object Detection Platform powered by Federated Learning. (pp. 13172–13179)New York, USA: AAAI Press.

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