Brian Jay Tang

Brian Jay Tang is a Ph.D. candidate in Computer Science and Engineering at the University of Michigan, where he works in the Real-Time Computing Lab, advised by Prof. Kang G. Shin and collaborates with Prof. Florian Schaub. His research sits at the intersection of AI safety, security, and privacy—particularly the surveillance, security, and privacy risks of large language models and vision-language models. Across his projects, Tang has built and studied real-time privacy defenses, developed automated auditing systems that uncover mismatches between stated and actual privacy practices at internet scale, and investigated security and fairness issues in face recognition systems. He has published at top-tier venues including USENIX Security Symposium, IEEE Symposium on Security and Privacy, ACM Conference on Computer and Communications Security, and Privacy Enhancing Technologies Symposium, frequently serving as lead author, and his work includes user studies examining trust and disclosure in ad-injected LLM conversations. Before graduate school, he earned a B.S. in Computer Science from the University of Wisconsin–Madison, conducted ML security and privacy research, and completed software engineering internships at Roblox Corporation and Optum, where he built production features and large-scale security risk visualization tooling.