Academic Profile : Faculty

ascymiao_1_2.JPG picture
Prof Miao Chun Yan
Associate Vice President (Capability Building) and Chair, School of Computer Science and Engineering
President’s Chair in Computer Science
Professor, School of Computer Science and Engineering
Co-Director, Others - Please update the Remarks field
External Links
Dr Chunyan Miao is a President’s Chair Professor and Chair of the School of Computer Science and Engineering at Nanyang Technological University, Singapore. Under her leadership, SCSE has contributed greatly to NTU’s top world rankings in CS, CE and AI. SCSE has been ranked as the top university for AI Research Citation Impact by The Nikkei Review. Forbes has listed SCSE’s Data Science and Artificial Intelligence (DSAI) programme in ‘The 10 Best AI and Data Science Undergraduate Courses for 2021’.

Dr Miao is an expert on Human-Centered AI and is well-known for her impactful AI research in health, ageing, education, and digital industry. She has received over 20 Best Paper and AI awards including the NRF Investigatorship Award 2019; and the Innovative Applications of AI Award, AAAI 2018 and 2020. has been elevated as IEEE Fellow in 2022; was recognized as 'Professional of the Year' at the IT Leaders Awards 2021 and was named in the “Singapore 100 Women in Tech” list by IMDA/SCS/CNA in 2020, for her significant contributions to Singapore’s tech sector.

Dr Miao was bestowed the 2023 National Day Awards - Public Administration Medal (Silver) in recognition of her contributions to education in Singapore and dedicated service to the University.

Dr Miao initiated the large-scale interdisciplinary collaboration on AI Ethics and Governance and the world’s first Flexi-Master program to address ethical issues related to AI adoption in Singapore.

In recent years, she has received over S$250M in large-scale research grants and initiated over S$200M worth of educational programmes for the EDB-Industrial Postgraduate Programme (IPP) PhD training over 5 years.

Dr Miao is the Founding Director and Lead PI of the Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), the first AI-empowered interdisciplinary ageing research centre in Singapore. In collaboration with NHG and TTSH, LILY’s AI for ageing technologies have reached over 10,000 senior citizens in Singapore. LILY technologies have also been showcased during Singapore President’s state visits and ministerial mission trips to UK, China and Japan.

Dr Miao is the Founding Director and Lead PI of the S$50M Alibaba-NTU Singapore Joint Research Institute, Alibaba’s first and largest joint research institute outside China. She is also the Founding Director and Lead PI of the $60M NTU-WeBank Joint Research Centre on FinTech.

Dr Miao has served as Council Member, Singapore Computer Society (SCS) since 2006. She is Chair of the AI Body of Knowledge Review Panel which has played an instrumental role in positioning Singapore as a world leader in AI Ethics and Governance. She has served as Editor of several international journals and as Chair/Co-chair/Technical Program Committee member of many leading AI international conferences. She was the general chair of KDD 2021, the world’s premier DSAI conference.

Dr Miao received her PhD Degree in Computer Engineering from NTU and pursued her postdoctoral studies at Simon Fraser University (SFU), Canada. She was a founding faculty member and Associate Professor at the Centre for Digital Media, jointly established by UBC and SFU. She was also a Tan Chin Tuan Fellow for academic and research collaboration in AI for education at Harvard University and MIT. She is a Fellow of SCS, the Academy of Engineering Singapore (SAEng) and the Institution of Engineers Singapore (IES).
Human Centered Artificial Intelligence (HAI), Human AI Interaction, AI Ethics, HAI for health, ageing, sustainability and education.
  • AI for Person Re-Identification “Skeleton-Based Person Re-Identification, Self-Supervised/Unsupervised Person Re-Identification, Domain Generalized Person Re-Identification”
  • Alibaba-NTU Singapore Joint Research Institute
  • CyberSG R&D Programme Office
  • Joint NTU-Alibaba Research Institute
  • Joint NTU-WeBank Research Centre on Fintech
  • Joint SDU-NTU Centre for Artificial Intelligence Research(C-FAIR) - Smart Community Research and Talent Programme
  • Monetary Academic Resources for Research
  • NTU-Tencent Joint Research Collaboration Laboratory (Aceville)
  • Senior-friendly Persuasive AI Companions for an Aging Population
  • TradeMaster: Reinforcement Learning-based Quantitative Trading Toolkit
  • TradeMaster: Reinforcement Learning-based Quantitative Trading Toolkit (SMU)
  • TrustFUL: Trustworthy Federated Ubiquitous Learning
  • TrustFUL: Trustworthy Federated Ubiquitous Learning (SCSE)
  • WeBank-NTU Joint Research Institute on Fintech
US 2016/0379296 A1: Method And Apparatus For Algorithmic Control Of The Acceptance Of Orders By An E-Commerce Enterprise (2021)
Abstract: This invention proposes an autonomous interaction decision support apparatus to provide the operator of an e-commerce business with a recommendation of which received orders to perform. The apparatus autonomously tracks situational information comprising the existing level of work of the e-business, for each of multiple products and/or services offered by the business, and also a desired level of work. In this way, the recommendation protects both the reputation of the business and achieves work-life balance for the business owner.

US 2015/0278676 A1: A Curosity-Based Emotion Modelling Method And System For Virtual Companions (2019)
Abstract: The invention proposes that a software agent for assisting a user in a virtual environment defined by a first concept map, maintains a record of its experiences in the form of a second concept map. The concept maps are compared to obtain a measure of stimulation. The measure of stimulation is used to derive a comparison value indicative an emotional state of the user, and the comparison value is used in a reward function to guide the behavior of the software agent.

US 2018/0012261 A1: Computerized Method And System For Personalized Storytelling (2019)
Abstract: A method and system are proposed for presenting information relating to a product to a potential customer for the product, upon the user scanning a 2D barcode using a mobile device. The information is presented to the customer as a story generated by an agent-based storytelling system. The story is personalized to the customer using online multimedia. It can be used to conduct mobile branding and advertisement, and is able thereby to augment offline shopping with online shopping experience.

US 2017/0140409 A1: Computerized Method and System for Automating Rewards to Customers (2019)
Abstract: A computerized reward mechanism system is proposed which makes use both of presently collected data relating to the customer, and a database of historical data relating to the customer. Using this information, the system performs a customer preference analysis, which results in selecting rewards which are adapted to the customers' personal needs. In one form, the system is positioned at a shopping center, and includes cameras for recognising past customers by image processing. This may be done without requiring customers to participate in a process of signing up to use the system. The presently collected data include responses to questions put to the customer relating to his present shopping experience. Customer honesty is considered in the step of determining which rewards to offer the consumer, to encourage the consumers to express their feelings more clearly.

US 2015/0278688 A1: A Episodic And Semantic Memory Based Remembrance Agent Modeling Method And System For Virtual Companions (2018)
Abstract: A remembrance agent is proposed, to be hosted on a wearable computer. The remembrance agent employs a first database (WK) of knowledge about the world in the form of concept maps. Events (“episodes”) experienced by a user are each classified as relating to one or more of the concepts in WK. The episodes are used to produce a second database (episodic memory, EM), and the classification is also used to update a third database (semantic memory, SM) organised using the concepts. The semantic memory thus summarizes the user's interaction with the concepts during the episodes. A current situation of the user is classified according to the concepts, and the classification is used, with the EM and SM, to provide to the user information relevant to the current situation.