About Tao Ni Tao Ni Assistant Professor, Computer Science Cyber Security resilient cyber-physical Professor Tao Ni (Tony) is focused on building secure, reliable and energy-efficient cyber-physical systems to enable trustworthy computing and confidential sensing in critical infrastructure. His approach combines hardware-software co-design with advanced AI technologies. Events Presented Events Sep 6 - Sep 12, 2026 The Dark Side of Metaverse: Unmasking the Threats from eXtended Reality (XR) Systems Tao Ni, Assistant Professor, Computer Science Sep 7, 12:00 - 13:00 B9 R2325 extended reality XR cyber-physical systems privacy cybersecurity This talk reveals how the seamless integration of computation, networking, and physical components in XR devices turns every layer of the system into an attack surface that non-intrusively leaks user privacy, ranging from what users type and do to what they perceive in virtual scenes. Mar 15 - Mar 21, 2026 Demystifying Adversarial Patch Attacks and Defenses in the Physical World Tao Ni, Assistant Professor, Computer Science Mar 16, 12:00 - 13:00 B9 R2325 Trustworthy AI Computer Vision computational predictions spoofing In this talk, I will introduce a series of adversarial patch attacks in face recognition systems and autonomous driving cars, and present our recent studies in developing a zero-shot and patch-agnostic defense framework.
The Dark Side of Metaverse: Unmasking the Threats from eXtended Reality (XR) Systems Tao Ni, Assistant Professor, Computer Science Sep 7, 12:00 - 13:00 B9 R2325 extended reality XR cyber-physical systems privacy cybersecurity This talk reveals how the seamless integration of computation, networking, and physical components in XR devices turns every layer of the system into an attack surface that non-intrusively leaks user privacy, ranging from what users type and do to what they perceive in virtual scenes.
Demystifying Adversarial Patch Attacks and Defenses in the Physical World Tao Ni, Assistant Professor, Computer Science Mar 16, 12:00 - 13:00 B9 R2325 Trustworthy AI Computer Vision computational predictions spoofing In this talk, I will introduce a series of adversarial patch attacks in face recognition systems and autonomous driving cars, and present our recent studies in developing a zero-shot and patch-agnostic defense framework.
