Amir Houmansadr

Amir Houmansadr's research group evaluates the privacy of in-the-wild Internet services, such as messaging applications, IoT devices, and machine learning APIs, and designs and implements tools to enhance the privacy of Internet users, such as anti-censorship systems. To this end, Houmansadr's research group combines the development of practical systems with rigorous theoretical analysis, and incorporates techniques from various disciplines such as computer networking, cryptography, and information theory. The specific problems his research group is currently exploring include Internet censorship resistance, network traffic analysis, covert communications, and machine learning security and privacy. His research has found flaws in popular privacy-preserving tools, and has led to the advent of novel designs to overcome these problems.

His work has been publicized in the media through interviews, and he has received several awards for his research, including an NSF CAREER Award in 2016, a Google Faculty Research Award in 2015, and the 2013 Best Practical Paper award of the IEEE Symposium on Security & Privacy (Oakland). Houmansadr serves regularly on the technical program committees and editorial boards of top-tier conferences and journals in the area of security and privacy.