Andrew is the Chief Data Scientist at HiddenLayer, responsible for researching and design of adversarial ML detection mechanisms. He has nearly a decade of experience in the cyber-security industry working for Cylance, Sophos, and Elastic, where he trained and deployed machine learning solutions to detect malware and emerging threats. Andrew has previously worked with computer vision and machine learning for neuroscience research, as well as applying ML/AI in the fields of speaker recognition, vehicle fault prediction, and financial modeling. Andrew holds a Ph.D. in computer engineering and machine learning from the University of Tennessee, Knoxville, with his dissertation focusing on Conditional Computation in Deep and Recurrent Neural Networks. As a two-time BlackHat speaker, most recently on Deep Learning on Disassembly, Andrew is passionate about sharing knowledge with the cyber-security community. His interests include attacking machine learning systems to better understand how to develop defensive countermeasures, and plays a mean guitar. He resides in Portland, Oregon, with his wife, dog, and two cats.
HiddenLayer, a Gartner recognized Cool Vendor for AI Security, is the leading provider of Security for AI. Its security platform helps enterprises safeguard the machine learning models behind their most important products. HiddenLayer is the only company to offer turnkey security for AI that does not add unnecessary complexity to models and does not require access to raw data and algorithms. Founded by a team with deep roots in security and ML, HiddenLayer aims to protect enterprise’s AI from inference, bypass, extraction attacks, and model theft. The company is backed by a group of strategic investors, including M12, Microsoft’s Venture Fund, Moore Strategic Ventures, Booz Allen Ventures, IBM Ventures, and Capital One Ventures.
