Machine Learning, Artificial Intelligence, Recommender Systems, Internet of Things, Brain Computer Interface, Human Machine Interactions
Fields of Research (FoR)Reinforcement learning, Deep learning, Data mining and knowledge discovery, Semi- and unsupervised learning, Recommender systems, Stream and sensor data, Neural networks, Data engineering and data science
A/Prof. Lina Yao is Scientia Associate Professor and Acting Associate Head of School (Research) in the School of Computer Science and Engineering. She is leading the research group Data Dynamics Lab (D2 Lab) founded in 2016. We strive for developing generalizable and explainable data-efficient data mining, machine learning and deep learning algorithms—as well as designing systems and interfaces—to enable novel ways of human-machine...view more
A/Prof. Lina Yao is Scientia Associate Professor and Acting Associate Head of School (Research) in the School of Computer Science and Engineering. She is leading the research group Data Dynamics Lab (D2 Lab) founded in 2016. We strive for developing generalizable and explainable data-efficient data mining, machine learning and deep learning algorithms—as well as designing systems and interfaces—to enable novel ways of human-machine interactions, including an improved understanding of challenges such as robustness, trust, explainability and resilience that improve human-autonomy partnership.
Her research area is in Few-Shot Learning, Zero-Shot Learning, Deep Reinforcement Learning, Meta-Learning, Neural Process, Self-supervised Learning, Graph Neural Networks and their applications in a broad range of applications in Recommender Systems, Computer Vision, Brain-Computer Interface, Biomedical Image Analysis, Intelligent Transportation System, and Internet of Things. She maintains a strong international research collaboration with world-leading universities such as Stanford University, Tsinghua University, UCSD, TU Wien etc. She is serving as Associate Editor for ACM Transactions on Sensor Networks (ACM TOSN), Knowledge-based Systems (KNOSYS) and Section of Recommender Systems of Frontiers in Big Data. She has over 200 peer-reviewed publications both in international leading conferences and journals including NeurIPS, SIGKDD, WWW, SIGIR, ICDM, CVPR, UbiComp, AAAI, IJCAI etc.
Personal website - https://www.linayao.com/
Australia Category-1 Grants:
- ACARP, Chief Investigator, Risk Based Model for Forecasting Longwall Face Cavity Development, 2022 - 2023
- ARC Discovery Project, Lead CI, Robust Preference Inference from Spatial-Temporal Interaction Networks, 2021 - 2023
- ARC Linkage Project, Lead CI, Context and Activity Recognition for Personalised Behaviour Recommendation, 2020 - 2022
- ARC LIEF, CI, "A Large-Scale Distributed Experimental Facility for the Internet of Things", Australian Research Council Linkage Infrastructure, Equipment and Facilities Project, 2018
- ARC DECRA Award, Sole CI, "Effective Recommendation for Web of Things", Australian Research Council Discovery Early Career Researcher Award, 2016-2018
- EpiWatch – Artificial Intelligence Early-Warning System for Epidemics, CI, Medical Research Future Fund 2021 - 2022
- Machine learning techniques for self-aware computing, Sole CI, Defence Science and Technology Group, 2021 - 2025
- Deep learning for objective measure of tinnitus, CI, British Tinnitus Association 2021 - 2024
- Exploration of Hierarchy of Learning Models in Relation to Goals, Defence Science and Technology Group, 2020-2023
- Long Range/BAA Project, Sole CI, "Context-aware Intent Prediction for Human-Machine Cooperation Improvement", Office of Naval Research (US Department of Navy), 2019 - 2021
- Multi-faceted Adaptive Trust Management in Federated/Distributed Data and AI Systems, Lead CI, Collaborative Research Project, Data61, 2019 - 2021
PhD - University of Adelaide. 2014
- Educational Excellence and Innovation Group Award, UNSW Faculty of Engineering Excellence Awards 2021
- Winner of 2021 Australia and New Zealand Women in AI Awards in the category of AI in Cybersecurity, 2021
- Academic Excellence, UNSW Faculty of Engineering Excellence Awards, 2020
- Vice-Chancellor’s Award for Excellence in Higher Degree Research Supervision in the Emerging Supervisor Category 2020, UNSW
- Listed among the World’ Top 2% Scientists in the 2020 list compiled by Stanford University and published at PLOS in the single year category.
- Inaugural Vice Chancellor's Women's Research Excellence Award, The University of Adelaide 2015
- Dean's Commendation for Doctoral Thesis Excellence, The University of Adelaide 2014
- Google PhD Prize, Google 2012
My Research Supervision
Areas of supervision
Key areas: Machine learning and data mining.
I'm always looking for self-motivated PhD students who are interested in machine learning and data mining area.
More information can be found on my personal website
Please check my personal website or Data Dynamics website for more information.
- TechVouchers Project - Succession Plus Australia, Co-CI, Automatic and Integrative Business Solution for Valuation Data Warehousing, Department of Industry, New South Wales 2020
- Cooperative Research Centres Projects, CI, DeepIoT – A New Hybrid Wireless IoT Platform for Underground Mines, 2020 - 2023
- TechVouchers Project - iKnow Pty Ltd., Sole CI, Customter-centric Interactive Decision Making System, Department of Industry, New South Wales, 2019
- UNSW-Tsinghua University Collaborative Research Fund, CI, "iAgeWell: A Unified Computational Mobile Solution for Personalised Wellness management in Senior Citizens", 2019 -2020
- TechVoucher Project, Co-CI, Inkerz Smartpen, Department of Industry, New South Wales, 2019
- TechVouchers Project - Raiz Pty Ltd., Sole CI, "Collaborative Deep Learning based Personalized Product Recommendation Model", Department of Industry, New South Wales, 2018
- UNSW-Tsinghua University Collaborative Research Fund, Lead CI, "City Pulse: A Unified Computational Framework for Data-driven, People-centric Smarter City Applications", 2018 - 2019
COMP9727 - Recommender Systems
COMP9321 - Data Service Engineering
UNSW CSE Honour thesis and Master's research project students
Vertically Integrated Project - Data Dynamics