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.