Ram Shankar Siva Kumar is a Data Cowboy working on secure machine learning systems. At Microsoft, he founded the Trustworthy Machine Learning Group, bringing together an interdisciplinary group of researchers and engineers to proactively attack AI systems and defend from attacks, He co-founded the Adversarial ML Threat Matrix, to enumerate threats to machine learning used in commercial assets, which notably appeared in the National Security Commission on Artificial Intelligence’s final report presented to the United States Congress and the President. More broadly, his work has appeared in industry conferences like RSA, Enigma, BlackHat, Defcon’s AI Village, BlueHat, DerbyCon, MIRCon, Infiltrate as well as academic venues like Harvard Business Review, NeurIPS, ICLR, ICML, IEEE S&P, ACM - CCS and covered by popular media outlets like Bloomberg, VentureBeat, Wired and Geekwire. 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.
Hyrum S. Anderson is Principal Architect in the Azure Trustworthy Machine Learning group at Microsoft. Prior to joining Microsoft, he was the Chief Scientist at Endgame, and conducted research in information security and situational awareness at FireEye, Mandiant, Sandia National Laboratories, and MIT Lincoln Laboratory. He received his Ph.D. in Electrical Engineering (signal processing + machine learning) from the University of Washington and BS+MS degrees from Brigham Young University. He is co-founder and co-chair for the Conference on Applied Machine Learning in Information Security and has spoken at numerous signal processing, machine learning, and security conferences, including RSA, DEFCon, and BlackHat.