I am an Assistant Professor in the Computer Science Department at University of Illinois at Urbana-Champaign. My research focuses on machine learning, security, privacy, and game theory. Specifically, much of our work aims at exploring vulnerabilities of machine learning systems to various adversarial attacks, and endeavors to develop real-world robust learning systems.
The long-term goal for our group, Secure learning lab (SL2), is to make machine learning algorithms safer, more efficient, and more explainable. We have worked on exploring different types of adversarial attacks including evasion and poisoning attacks in physical world with semantically meaningful constraints. We have developed and will continue to explore robust learning algorithms based on game theory, prior knowledge of data distribution, as well as properties of learning tasks. Our work directly benefits applications such as computer vision, question-answering, audio recognition, and privacy preserving medical records analysis.
- [08/19] Our generated physical adversarial Stop Sign used in our CVPR’18 is on display at Science Museum in London.
- [08/19]“Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms” is accepted in VLDB 2019.
- [08/19]“AdvIt: Adversarial Frames Identifier Based on Temporal Consistency in Videos” is accepted in ICCV 2019.
- [06/19] Our paper: "Adversarial Objects Against LiDAR-Based Autonomous Driving Systems" is reported by JiQiZhiXin QbitAI and is discussed at Reddit , .
- [06/19] Our paper: "SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing" is reported by JiQiZhiXin .
- [05/19] Workshop "Security and Privacy of Machine Learning" in ICML 2019. Please submit your papers here and win the best paper award!
- [05/19] Workshop "Adversarial Machine Learning in Real-World Computer Vision Systems" in CVPR 2019. Please submit your papers here!
- [05/19] Our paper "Realistic Adversarial Examples in 3D Meshes" is accepted in CVPR 2019 as oral presentation! Congratulations to Chaowei and Dawei!
- [05/19] Our paper "Generating 3D Adversarial Point Clouds" is accepted in CVPR 2019!
- [02/19] Our paper "How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning" got accepted in AAMAS 2019 as oral presentation!
- [01/19] Our paper "Towards Efficient Data Valuation Based on the Shapley Value" got accepted in AISTATS 2019! Check it out if you want to know which data contribute more to your model!
- [04/19] Our paper "Characterizing Audio Adversarial Examples Using Temporal Dependency" got accepted in ICLR 2019.