The Intel Science & Technology Center for Adversary-Resilient Security Analytics (ISTC-ARSA) at the Georgia Institute of Technology is dedicated to the emerging field of machine-learning (ML) cybersecurity. Researchers from Intel Corporation, along with students and faculty from Georgia Tech, are strengthening the analytics behind malware detection and threat analysis.
Researchers study the vulnerabilities of ML algorithms and develop new security approaches to improve the resilience of ML applications. Outcomes are expected to benefit the security of analytics, search engines, customized news feeds, facial and voice recognition, fraud detection, and more.
Intel Corp. researchers
Li Chen is the co-primary investigator (PI) and research lead at the Intel Science & Technology Center for Adversary-Resilient Security Analytics. She designs the roadmaps with Intel and Georgia Tech PIs to jointly meet both industrial and academic research objectives. She also provides research direction and in-depth technical guidance to advance the ARSA research agenda. She is a data scientist and research scientist in the Security and Privacy Lab at Intel Labs, where she focuses on developing state-of-the-art robust machine learning and deep learning algorithms for security analytics including applications in malware detection and image classification in the adversarial setting. Prior to joining Intel Labs, Chen was a Data Scientist in Software and Services Group at Intel, where she focused on developing advanced and principled machine learning methods for cloud workload characterization and cloud computing performance. Li Chen received her Ph.D. degree in Applied Mathematics and Statistics from Johns Hopkins University. Her research interests primarily include machine learning, statistical pattern recognition, computational statistics, random graph inference, data mining, and inference for high-dimensional data. Her research has been featured in a number of pioneering scientific and engineering journals and conferences including IEEE Transactions on Pattern Analysis and Machine Intelligence, Annals of Applied Statistics, Parallel Computing, AAAI Conference on Artificial Intelligence and SPIE. She has given more than 30 technical presentations, including at the Joint Statistical Meeting (the largest statistics conference in North America), AAAI conference, International Joint Conference on Artificial Intelligence, and Spring Research Conference on Statistics and Industry Technology.
Michael Kounavis is a research scientist with Intel Corporation, working in the areas of machine learning, computer vision and cryptography. His current research focuses on adversarial machine learning. Dr. Kounvavis co-invented Intel’s AES-NI instruction set for accelerating AES encryption, an accomplishment for which he received an Intel Achievement Award in 2008, and is one of the main authors of Intel’s intellectual property portfolio in the area of hand gesture recognition. He has published more than 60 technical papers in the above areas and holds more than 20 patents. His prior work includes an early proposal on programmable virtual networks (Spawning Networks, 1999), which was a forerunner of today’s Software Defined Network (SDN) architectures. This work eventually became Kounvavis' Ph.D. thesis, which was awarded with distinction from Columbia University in 2004.
Scott Buck is a member of the University Research Collaborative Group in Intel Labs where he is the Program Director of the Intel Science and Technology Centers (ISTC) for Adversarial-Resilient Security Analytics, Secure Computing, Cyber Physical Systems and Intel’s AI Academic Outreach Research Program. Scott joined Intel in 1995 and has more than 30 years of experience in high technology.