Ram Shankar is a Data Cowboy in Azure Security at Microsoft, empowering engineers to secure machine learning systems. His work has appeared in industry conferences like RSA, BlackHat, Defcon, BlueHat, DerbyCon, MIRCon, Infiltrate, academic conferences like NeurIPS, ICLR, ICML, IEEE S&P, ACM - CCS and covered by Bloomberg, VentureBeat, Wired and Geekwire. He founded the Adversarial ML Threat Matrix, an ATT&CK style framework enumerating threats to machine learning. His work on adversarial machine learning appeared notably in the National Security Commission on Artificial Intelligence's final report presented to the United States Congress and the President. He is an affiliate at the Berkman Klein Center for Internet and Society at Harvard University and a Technical Advisory Board Member at the University of Washington.