Trustworthy Machine Learning Systems
Research on robust learning, adversarial resilience, and dependable AI deployment for real-world intelligent systems.
This seed project page defines the structure that TAS Lab will use for future project documentation.
Current themes include:
- adversarial robustness and secure learning
- trustworthy perception for intelligent systems
- dependable deployment of machine learning models
Project repositories, demos, and linked publications can be added as the project portfolio grows.
Lab Members
Patrick Chan
Associate Professor, Vice Dean
Patrick Chan works on machine learning, deep learning, image processing, adversarial learning, and secure machine learning.