Brian LIM Youliang - NUS Computing

COM2-03-49

660 11084

www.comp.nus.edu.sg/~brianlim

Brian LIM

Associate Professor

B.S. (Engineering Physics, Minor: Computer Science, Cornell University)
Ph.D. (Human-Computer Interaction, Carnegie Mellon University)
M.S. (Human-Computer Interaction, Carnegie Mellon University)

Brian Lim is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS). He received a B.S. degree in engineering physics from Cornell University, Ithaca, New York, U.S.A., in 2006, and a Ph.D. degree in human-computer interaction from Carnegie Mellon University, Pittsburgh, U.S.A., in 2012. He leads the NUS Ubicomp Lab which focuses on research on explainable AI, human-computer interaction, ubiquitous computing, and applied machine learning for user-centric and trustworthy systems. His research focuses on designing, developing and evaluating AI-driven technologies to address societal challenges in healthcare, wellness and urban sustainability.

RESEARCH AREAS

Media

Human-Computer Interaction
Ubiquitous Computing
Visualisation

RESEARCH INTERESTS

Explainable Artificial Intelligence
Human-Computer Interaction
Ubiquitous Computing
Healthcare Analytics and Apps
Context-Aware Computing

RESEARCH PROJECTS

RESEARCH GROUPS

TEACHING INNOVATIONS

SELECTED PUBLICATIONS

Wencan Zhang and Brian Y. Lim. 2022. Towards Relatable Explainable AI with the Perceptual Process. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI '22). ACM, New York, NY, USA, 1–16. Best Paper Award (Top 1%).
Yunlong Wang, Priyadarshini Venkatesh, and Brian Y. Lim. 2022. Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI '22). ACM, New York, NY, USA, 1–16.
Wencan Zhang, Mariella Dimiccoli, and Brian Y. Lim. 2022. Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI '22). ACM, New York, NY, USA, 1–16.
Xuejun Zhao, Wencan Zhang, Xiaokui Xiao, and Brian Y. Lim. 2021. Exploiting Explanations for Model Inversion Attacks. IEEE/CVF International Conference on Computer Vision (ICCV ’21), pp. 662-672.
Lim, B. Y., Dey, A. K., and Avrahami, D. 2009. Why and why not explanations improve the intelligibility of context-aware intelligent systems. In Proceedings of the 27th international Conference on Human Factors in Computing Systems Boston, MA, USA, April 04 - 09, 2009. CHI '09. ACM, New York, NY, 2119-2128. Best long paper nomination.
Sam Rhys Cox, Yunlong Wang, Ashraf Abdul, Christian von der Weth, and Brian Y. Lim. 2021. Directed Diversity: Leveraging Language Embedding Distances for Collective Creativity in Crowd Ideation. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). ACM, New York, NY, USA, Article 393, 1–35.
Yan Lyu, Fan Gao, I-Shuen Wu and Brian Y. Lim. Imma Sort by Two or More Attributes With Interpretable Monotonic Multi-Attribute Sorting. IEEE Transactions on Visualization and Computer Graphics (TVCG), vol. 27, no. 4, pp. 2369-2384.
Danding Wang, Wencan Zhang, and Brian Y. Lim. 2021. Show or suppress? Managing input uncertainty in machine learning model explanations. Artificial Intelligence, 294, 103456.
Ashraf Abdul, Christian von der Weth, Mohan Kankanhalli, and Brian Y. Lim. 2020. COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). ACM, New York, NY, USA, 1–14.
Yan Lyu, Xu Liu, Hanyi Chen, Arpan Mangal, Kai Liu, Chao Chen, and Brian Y. Lim. 2020. OD Morphing: Balancing Simplicity with Faithfulness for OD Bundling. IEEE Transactions on Visualization and Computer Graphics (TVCG), vol. 26, no. 1, pp. 811-821.
Brian Y. Lim, Judy Kay, and Weilong Liu. 2019. How does a nation walk? Interpreting large-scale step count activity with weekly streak patterns. Proceedings of the ACM Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Volume 3, Issue 2, Article 57 (June 2019), 46 pages. Distinguished Paper Award (Top 6 out of 166 accepted papers).
Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019. Designing Theory-Driven User-Centric Explainable AI. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). Association for Computing Machinery, New York, NY, USA, Paper 601, 1–15.
Ashraf Mohammed, Jo Vermeulen, Danding Wang, and Brian Y. Lim. 2018. Towards Explainable, Accountable and Intelligible Systems: An HCI Research Agenda. Accepted to CHI 2018 Conference on Human Factors in Computing Systems.
Lim, B. Y. and Dey, A. K. 2011. Investigating Intelligibility for Uncertain Context-Aware Applications. In Proceedings of the 13th international conference on Ubiquitous computing UbiComp '11. ACM, New York, NY, USA, 415-424.
Lim, B. Y. and Dey, A. K. 2010. Toolkit to Support Intelligibility in Context-Aware Applications. In Proceedings of the 12th ACM international Conference on Ubiquitous Computing Copenhagen, Denmark, September 26 - 29, 2010. Ubicomp '10. ACM, New York, NY, 13-22.
Lim, B. Y. and Dey, A. K. 2009. Assessing Demand for Intelligibility in Context-Aware Applications. In Proceedings of the 11th international Conference on Ubiquitous Computing Orlando, Florida, USA, September 30 - October 03, 2009. Ubicomp '09. ACM, New York, NY, 195-204.

AWARDS & HONOURS

2022: CHI'22 Best Paper Award (Top 1%)
2020: IMWUT Distinguished Paper Award (Top 6 out of 166 accepted papers in 2019)
2016: MOE Science Mentorship Programme - Outstanding Mentor Award
2009: CHI'09 Best Long Paper Nomination (Top 5%)
2007: A*STAR NSS(Ph.D.) Scholarship
2003: A*STAR NSS(BS) Scholarship
2000: International Physics Olympiad - Honorary Mention
1999: Singapore Physics Oympiad - Honourable Mention