Can Liu

Staff Machine Learning Scientist  

Team: AI Prototyping 

Research areas: Real Time Payments, User Authentication, System Security

Portrait of Can Liu

Biography

Dr. Can Liu joined Visa Research as a Machine Learning Scientist in June 2021. He received his Ph.D. in Electrical and Computer Engineering from Rutgers University under the supervision of Prof. Janne Lindqvist. Prior to joining Visa, he was a research intern at Snap Research.  

As a member of the AI prototyping team, Can’s research focuses on fraud detection, user authentication, and system security. He regularly publishes papers in premier academic conferences and journals, including the ACM CHI Conference on Human Factors in Computing Systems (CHI), the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), the Network and Distributed System Security Symposium (NDSS), and USENIX Security. He also serves as a reviewer for several premier conferences and journals. 


Publications

  1. Shridatt Sugrim, Can Liu, Janne Lindqvist, “Recruit until it fails: Exploring performance limits for identification systems”, the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2019. 
  2. Shridatt Sugrim, Can Liu, Meghan McLean, Janne Lindqvist, “Robust Performance Metrics for Authentication Systems”, The Network and Distributed System Security Symposium (NDSS), 2019.
  3. Xianyi Gao, Yulong Yang, Can Liu, Christos Mitropoulos, Janne Lindqvist, Antti Oulasvirta, “Forgetting of Passwords: Ecological Theory and Data”, USENIX Security Symposium, 2018.
  4. Can Liu, Gradeigh D Clark, Janne Lindqvist, “Where usability and security go hand-in-hand: Robust gesture-based authentication for mobile systems”, Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017. 
  5. Can Liu, Gradeigh D Clark, Janne Lindqvist, “Guessing Attacks on User-Generated Gesture Passwords”, the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2017