Dr. Robert Wright, AIS, Senior Advising Engineer
Binghamton University, Ph.D., Computer Science, 2014
Dr. Robert Wright brings 15 years of proven experience developing advanced machine learning technologies that deliver revolutionary capabilities for the DoD. Dr. Wright is a senior advising research scientist with AIS and a Principal Investigator for the VIC3TORS effort, applying his machine learning expertise to advance biometric verification and attribution. He received his Ph.D. in computer science from Binghamton University in 2014 for his work in developing reinforcement algorithms that learn efficiently from experience. He has published 10+ papers and journal articles on machine learning in top tier scientific venues and was awarded “Best Paper” at the 2013 European Conference on Machine Learning. Prior to AIS, Dr. Wright was a research scientist with the AFRL and co-lead for the Autonomy Community of Interest: Machine Perception Reasoning and Intelligence technical challenge area for the Office of the Secretary of Defense. In his 13 years with AFRL, he led several research efforts in machine learning and autonomous systems. His research interests and areas of expertise include reinforcement learning, deep learning, data mining, biometrics, multi-agent systems, and genetic algorithms.
Machine learning is revolutionizing many industries and forever changing how we interact with computer systems and each other. The revolution is occurring in cyber security, where machine learning is already being applied to assist human analysts and security experts ingest and make sense of increasingly large amounts of data so that they can identify threats and make decisions as to how best to mitigate vulnerabilities. While this symbiotic relationship is improving our security posture, it is fundamentally limited by humans making decisions at human speed. Significant gains in security can be made if autonomy is embraced and “trusted” machine learning is given license to make actionable decisions at machine speed. Despite the potential benefits, there have been relatively few research efforts and applications of autonomous machine learning towards cyber security applications. The purpose of this article is to make the case for increased research and development of autonomous control machine learning approaches in the cyber domain. In it, we discuss emerging autonomous machine learning technologies and their recent successes, technical and non-technical challenges that still need to be overcome for practical autonomous applications of machine learning, and finally some thoughts on potential near-term applications of autonomous machine learning to cyber security.View Document
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Machine learning is revolutionizing many industries and forever changing how we interact with computer systems and each other. The revolution is particularly relevant to cyber security, where machine learning is used to help human analysts make sense of increasingly large… Read More