NUS Ubicomp Lab

About Us

The NUS Ubicomp Lab researches and develops Explainable AI-driven analytics and apps to improve people’s lives. We are interested in combining Machine Learning and Human-Computer Interaction to improve health, wellness and livability in smart cities with interpretable predictive analytics and mobile apps for automated self-tracking.

AI + HCI

User-Centric Explainable AI

We investigate user requirements and explanation algorithms to help users to understand and trust the increasingly ubiquitous Artificial Intelligence.

Apps for Health Behavior Change

We investigate socio-technical context-aware applications to sense behavior and promote pro-health and sustainability behaviors with ubiquitous mobile apps and wearables.

User Behavior Analytics and Visualizations

We apply data mining and visualization techniques to understand user, population, and urban behaviors.

Health

Wellness

Cities

Some of our works

Highlighted Projects

Reasoned Framework for Human-Centric XAI

Explainable AI / Intelligibility / Medical

AI-HCI Research Landscape for Explanable Systems

Explainable AI / Intelligibility

Mobile Food Logging and Recommendation

Behavior Change / Food / Health Tracking / Wellness

Explainable AI for Medical Data Analytics

Data Analytics / Explainable AI / Medical

Population Analytics for Public Health

Data Analytics / Health Tracking / Wellness

Interactive Visualization for Urban Big Data

Data Analytics / Explainable AI / Visualization

FRESH Smart Home Platform

Energy / Internet-of-Things / Sensors / Smart Home

Intelligible Context-Aware Applications

Context-Awareness / Intelligibility

Hiring

We are actively looking for highly motivated and talented postdoc research fellows, PhD, Masters, Undergraduate students to work in the areas of explainable AI, applications of deep learning, human-computer interaction, ubiquitous / pervasive computing, internet-of-things and sensors, data analytics and data visualization.
If you are a prospective PhD student, please check out details of the NUS Computer Science PhD programme and apply online. If you are interested in working with our lab, please email your CV and transcript!

Interactive Explainable AI – Postdoc, PhD Student

The prevalence and ubiquity of deep learning and AI in society is driving the need for their responsible use. To make AI more trustworthy, it needs to be explainable, privacy-preserving, and human-centered. While much recent research on Explainable AI (XAI) has provided many explanation techniques, they remain unusable for end users and domain experts. Therefore, this project aims to develop novel explainable AI algorithms and evaluation methods to improve the usability and usefulness of AI.

We are looking for talented candidates to join our multidisciplinary team. The project is investigating computer vision, artificial intelligence, data visualization and human-computer interaction to develop effective human-AI collaboration and explainable AI.

Expected Skills:

For PhD candidates: Masters or Bachelors in Computer Science, Electrical Engineering or related disciplines
For Postdoc candidates: PhD in Computer Science, Electrical Engineering or related disciplines with a background in human-computer interaction and cyber-physical systems
Expertise in computer vision, machine learning, human-computer interaction, machine learning, and/or data visualization is highly desirable
Competency in developing and implementing algorithms, and programming
Excellent writing and presentation skills
Ability to work independently (50%) and team projects (50%)

To apply, please send your research statement, CV and names of 3 referees (name, institution, email) to Dr. Brian LIM (brianlim@comp.nus.edu.sg). Only shortlisted candidates will be contacted.

More job descriptions.

2022

Wencan Zhang and Brian Y. Lim. 2022. Towards Relatable Explainable AI with the Perceptual Process. In Proceedings of the international Conference on Human Factors in Computing Systems. CHI ’22 . Best Paper Award (Top 1%).

Wencan Zhang, Mariella Dimiccoli and Brian Y. Lim. 2022. Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning. In Proceedings of the international Conference on Human Factors in Computing Systems. CHI ’22.

Yunlong Wang, Priyadarshini Venkatesh, Brian Y. Lim. 2022. Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation. In Proceedings of the international Conference on Human Factors in Computing Systems. CHI ’22.

2020

Yan Lyu, Fan Gao, I-Shuen Wu, and Brian Y. Lim. 2020. Imma Sort by two or more attributes with Interpretable Monotonic Multi-Attribute Sorting. IEEE Transactions on Visualization and Computer Graphics (TVCG).

Guang Jiang, Mengzhen Shi, Pengcheng An, Ying Su, Yunlong Wang, and Brian Y Lim. 2020. NaMemo: Enhancing Lecturers’ Interpersonal Competence of Remembering Students’ Names. In Companion Publication of the 2020 ACM on Designing Interactive Systems Conference. DIS Workshop ’20.

Leye Wang, Daqing Zhang, Dingqi Yang, Brian Y Lim, Xiao Han, Xiaojuan Ma. 2020. Sparse Mobile Crowdsensing With Differential and Distortion Location Privacy. In IEEE Transactions on Information Forensics and Security.

Abdul, A., von der Weth, C., Kankanhalli, M., and Lim, B. Y. 2020. COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations. In Proceedings of the international Conference on Human Factors in Computing Systems. CHI ’20.

2019

Yan Lyu, Victor CS Lee, Joseph Kee-Yin Ng, Brian Y Lim, Kai Liu, Chao Chen. 2019. Flexi-Sharing: A Flexible and Personalized Taxi-Sharing System. In IEEE Transactions on Vehicular Technology.

Heidi Fuchs, Arman Shehabi, Mohan Ganeshalingam, Louis-Benoit Desroches, Brian Lim, Kurt Roth, Allen Tsao. 2019. Comparing datasets of volume servers to illuminate their energy use in data centers. Energy Efficiency, 1-14.

Lyu, Y., Liu, X., Chen, H., Mangal, A., Liu, K., Chen, C., and Lim, B. Y. 2019. OD Morphing: balancing simplicity with faithfulness for OD bundling. In IEEE Transactions on Visualization and Computer Graphics (TVCG).

Lim, B. Y., Kay, J., and Liu, W. 2019. How does a nation walk? Interpreting large-scale step count activity with weekly streak patterns. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). (IMWUT Vol. 3 Distinguished Paper Award (Top 6 of 166)).

Vermeulen, J., Lim, B. Y., Avdic, M., Wang, D., and Abdul., A. 2019. The Curious Case of Providing Intelligibility for Smart Speakers. In CHI 2019 Workshop on Where is the Human? Bridging the Gap Between AI and HCI.

Park, H., Bharadhwaj, H., and Lim, B. Y. 2019. Hierarchical Multi-Task Learning for Healthy Drink Classification. In Proceedings of the International Joint Conference on Neural Networks (IJCNN).

Lim, B. Y., Yang, Q., Abdul, A. and Wang, D. 2019. Why these Explanations? Selecting Intelligibility Types for Explanation Goals. In IUI 2019 Second Workshop on Explainable Smart Systems (ExSS 2019).

Wang, D., Yang, Q., Abdul, A., Lim, B. Y. 2019. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the international Conference on Human Factors in Computing Systems. CHI ’19.

Yang, Z., Tan, E.H., Li, Y., Lim, B., Metz, M., Loh, T.P. 2019. Relative criticalness of common laboratory tests for critical value reporting. In Journal of Clinical Pathology.

2018

Wang, J., Wang, F., Wang, Y., Zhang D., Lim, B., and Wang, L. 2018. Allocating Heterogeneous Tasks in Participatory Sensing with Diverse Participant-Side Factors. In IEEE Transactions on Mobile Computing (TMC).

Lukoff, K., Li, T., Zhuang, Y., & Lim, B. Y. 2018. TableChat: Mobile Food Journaling to Facilitate Family Support for Healthy Eating. In Proceedings of the ACM Conference on Computer Supported Cooperative Work. CSCW ’18.

Bharadhwaj, H., Park, H., Lim, B. Y. 2018. RecGAN: Recurrent Generative Adversarial Networks for Recommendation Systems. In Proceedings of the ACM Conference on Recommender Systems. RecSys ’18.

Fuchs, H., Shehabi, A., Ganeshalingam, M., Desroches, L., Lim, B., Roth, K., Tsao A. 2018. Characteristics and Energy Use of Volume Servers in the U.S. In ACEEE Summer Study 2018.

Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y. (corresponding author), Kankanhalli, M. 2018. Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda. In Proceedings of the international Conference on Human Factors in Computing Systems. CHI ’18.

Lim, B. Y., Wang, D., Loh, T. P., and Ngiam, K. Y. 2018. Interpreting Intelligibility under Uncertain Data Imputation. In ACM IUI 2018 Workshop on Explainable Smart Systems (ExSS 2018).

Lim, B. Y., Smith, A., Stumpf, S. 2018. ExSS 2018: Workshop on Explainable Smart Systems. In Workshop at ACM IUI 2018.

Park, H., Wang, Z., Kashyap, A., Lim, B. Y.. 2018. Biases in Food Photo Taking Behavior. ACM CHI 2018 Workshop on Designing Recipes for Digital Food Lifestyles.

2017

2015

Urban, B., Shmakova, V., Lim, B. Y., Roth, K. 2015. Residential Consumer Electronics Energy Consumption in the United States. In Energy Efficiency in Domestic Appliances and Lighting 2015.

Urban, B., Shmakova, V., Lim, B. Y., Roth, K. 2015. Energy Consumption of Consumer Electronics in U.S. Homes in 2013. Final Report to the Consumer Electronics Association (CEA) by Fraunhofer USA.

2013

Lim, B. Y., Dey, A. K. 2013. Evaluating Intelligibility Usage and Usefulness in a Context-Aware Application. In Human-Computer Interaction. Towards Intelligent and Implicit Interaction. Springer Berlin Heidelberg, 2013. 92-101.

Lim, B. Y., Roth, K., Nambiar, S., Rayakota, H. 2013. FRESH: The Fraunhofer Experimental Smart Home Research Platform for Home Energy Management Applications. In MIT Energy Night 2013.

2011

Lim, B. Y., 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. DOI=10.1145/2030112.2030168 .

Lim, B. Y., Dey, A. K. 2011. Design of an Intelligible Mobile Context-Aware Application. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI ’11). ACM, New York, NY, USA, 157-166. DOI=10.1145/2037373.2037399

Lim, B. Y., Shick, A., Harrison, C., Hudson, S. E. 2011. Pediluma: Motivating Physical Activity Through Contextual Information and Social Influence. In Proceedings of the fifth international conference on Tangible, embedded, and embodied interaction (TEI ’11). ACM, New York, NY, USA, 173-180.

Vermeulen, J., Lim, B. Y., Kawsar, F. 2011. Pervasive Intelligibility: Workshop on Intelligibility and Control in Pervasive Computing. Pervasive 2011 Workshop on Intelligibility and Control in Pervasive Computing.

2010

Lim, B. Y., Brdiczka, O., Bellotti, V. 2010. Show Me a Good Time: Using Content to Provide Activity Awareness to Collaborators with ActivitySpotter. In Proceedings of the 16th ACM international conference on Supporting group work (GROUP ’10). ACM, New York, NY, USA, 263-272.

Lim, B. Y., 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.

2009

Lim, B. Y., 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.

Lim, B. Y., Dey, A. K., 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 Paper Honourable Mention (Top 5%).

Harrison, C., Lim, B. Y., Shick, A., Hudson, S. E. 2009. Where to Locate Wearable Displays? Reaction Time Performance of Visual Alerts from Tip to Toe. 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, 941-944.

Diamant, E. I., Lim, B. Y., Echenique, A., Leshed, G., and Fussell, S. R. 2009. Supporting intercultural collaboration with dynamic feedback systems: preliminary evidence from a creative design task. In Proceedings of the 27th international Conference Extended Abstracts on Human Factors in Computing Systems (Boston, MA, USA, April 04 – 09, 2009). CHI EA ’09. ACM, New York, NY, 3997-4002.

About Us

AI + HCI

User-Centric Explainable AI

Apps for Health Behavior Change

User Behavior Analytics and Visualizations

Health

Wellness

Cities

Some of our works

Highlighted Projects

Reasoned Framework for Human-Centric XAI

AI-HCI Research Landscape for Explanable Systems

Mobile Food Logging and Recommendation

Explainable AI for Medical Data Analytics

Population Analytics for Public Health

Interactive Visualization for Urban Big Data

FRESH Smart Home Platform

Intelligible Context-Aware Applications

Hiring

Interactive Explainable AI – Postdoc, PhD Student

Our Team

Our Alumni

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