Скачать или смотреть CS Colloquium: Advancing Efficient and Trustworthy Deep Learning for Sustainable AI Systems

Abstract: The rapid advancements in deep learning and artificial intelligence have led to transformative applications across various domains. To facilitate the broader application of machine intelligence, ensuring the efficiency and trustworthiness is more important than ever. In this talk, Yifan (Evelyn) Gong will present her work in building sustainable AI systems by advancing the efficiency and trustworthiness of deep learning to minimize environmental impact and promote social responsibility. She will start by introducing two system-level approaches to tackle the efficiency challenge. The first is a bottom-up approach that conducts AI algorithm-aware efficient system design. The second is a top-down approach to enable hardware-driven efficient AI algorithm design. Then, she will cover her work in reverse engineering of deceptions to show how adversarial strategies can be reverse-engineered to safeguard AI systems, which also reveals new connections between model efficiency and adversarial transferability. Finally,  she will conclude by providing pointers to the future direction of building sustainable AI systems.

Bio: Yifan (Evelyn) Gong received her Ph.D. in Electrical and Computer Engineering from Northeastern University in 2024. Her research vision is in general artificial intelligence systems to facilitate deep learning implementation on various edge devices and bridge the gap between algorithm innovations and hardware performance optimizations. Evelyn is the recipient of the College of Engineering Outstanding Graduate Student Award and the Dean’s Fellowship Award from Northeastern University. Her research won first place at the DAC Ph.D. Forum, and she is selected as the 2024 EECS Rising Star and t
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