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DTSTART;TZID="Pacific Time (US & Canada)":20250226T100000
DTEND;TZID="Pacific Time (US & Canada)":20250226T110000
SUMMARY:AGI SP25 Power Seminar Series:  Confidential Deep Neural Inference for Real-Time Systems by Professor Monowar Hasan
LOCATION:Electrical and Mechanical Engineering Building
DESCRIPTION:Advanced Grid Institute (AGI) hosts "Confidential Deep Neural Inference for Real-Time Systems" to be presented by Professor Monowar Hasan, School of Electrical Engineering and Computer Science, WSU\n\nAbstract\n\nDeep neural networks (DNNs) are increasingly used in time-critical, learning-enabled cyber-physical applications such as autonomous driving and robotics. Many of them have stringent temporal (i.e., "real-time") requirements. Despite the growing use of various deep learning models in real-time systems, protecting DNN inference from adversarial threats while preserving model privacy and confidentiality remains a key concern for resource and timing-constrained systems. Based on our recent exploration and findings in this domain, this talk will present challenges and new techniques to enable confidential deep neural for real-time systems.\n\nBio\n\nDr. Monowar Hasan is a Computer Science Assistant Professor at Washington State University (WSU). Before joining WSU, he held an Assistant Professor position at Wichita State University from 2021-2022. Dr. Hasan received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2020. His research interests include exploring security and resiliency techniques of cyber-physical system domains.
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