Network and AI security and privacy, censorship circumvention, traffic analysis, trustworthy AI.
Amir Houmansadr's research is on network and AI security and privacy, with emphasis on privacy-enhancing technologies and trustworthy AI. He leads the SPIN group and co-leads the UMass AI Security Lab. He designs censorship-circumvention systems, analyzes messaging platforms and machine-learning APIs for privacy leakage, and studies the security of emerging AI technologies. Earlier work includes traffic analysis, covert communications, and attacks on privacy tools. The group combines practical systems with cryptography, networking, and statistical analysis.
Amir Houmansadr is a Professor of Computer Science at UMass Amherst (faculty since 2014; full professor in 2025). He received his Ph.D. from the University of Illinois at Urbana-Champaign in 2012 and was a postdoctoral scholar at the University of Texas at Austin. He is an NSF CAREER and DARPA Young Faculty Award recipient.
He leads the SPIN Research Group and co-leads the UMass AI Security Lab.
Amir's work has been publicized in the media through interviews, and he has received several awards for his research, including UIUC's Computer Engineering Fellowship award and the Best Practical Paper award of the IEEE Symposium on Security & Privacy (Oakland) 2013. Amir has served as a program chair, technical program committee member, and reviewer for various conferences, workshops, and journals in the area of security and privacy.