Events Calendar

Towards Trustworthy Physical AI Generalists

Speaker: Ding Zhao , Associate Professor & Dean's Early Career Fellow - Carnegie Mellon University

Talk Title: Towards Trustworthy Physical AI Generalists

Abstract: Large language models like ChatGPT have shown that generalist foundation models can effectively tackle long-horizon tasks by training on extensive text data from the internet. It is anticipated that larger-scale data from the physical world, such as those generated by autonomous vehicles and the healthcare industry, could drive the next wave of AI development. A common challenge in deploying highly intelligent agents at scale in the physical world is ensuring their safety. In this talk, I will present our efforts to establish Trustworthy Physical AI Generalists to support this crucial transformation. I will explore the challenges of ensuring safety and generalization in the development of trustworthy AI, and discuss potential solutions, including rare event analysis, safe reinforcement learning, hierarchical generative models for task identification and transferability, and causal reasoning to improve generalizability. Additionally, I will discuss the advantages and challenges of using LLMs to develop physical AI generalists. I will introduce applications of our work in heart attack detection and acute care, self-driving technology, and robotic autonomy for assisting seniors and conducting safety-critical tasks related to climate change resilience.    
 
This lecture satisfies requirements for CSCI 591: Research Colloquium.  
 
https://usc.zoom.us/j/99488778795?pwd=oXg76V89VYG9b5I0CIEcn2E2Fz7d6z.1
 
Meeting ID: 994 8877 8795
Passcode: 868727

Biography: Ding Zhao is an Associate Professor and Dean's Early Career Fellow at Carnegie Mellon University, where he leads the Safe AI Lab. His research focuses on developing Trustworthy Physical AI Generalists for high-stakes applications at scale. Prof Zhao was invited by Uber ATG to enhance fleet safety following the world’s first fatal autonomous vehicle collision. Zhao collaborates with leading industry partners, including Google, Nvidia, Amazon, Apple, Microsoft, IBM, Ford, Uber, Bosch, Toyota, and Rolls-Royce. He has grants from NSF, DOT, DOE, and DARPA and published over 120 papers in top venues such as ICML, NeurIPS, ICLR, AISTATS, CoRL, ICRA, IROS, and Nature Communications. Zhao has mentored 20 Ph.D. students and 7 postdocs, with 7 of them becoming faculty members in academia.    Zhao has received numerous awards, including CMU Dean's Early Career Fellow Professorship, Provost's Inclusive Teaching Fellows Award, National Science Foundation CAREER Award, MIT Technology Review 35 Under 35 Award in China, George N. Saridis Best IEEE Transactions Paper Award, George Tallman Ladd Research Award, Struminger Teaching Award, Ford University Collaboration Award, Qualcomm Innovation Award, Carnegie-Bosch Research Award, and various industrial fellowship awards from Google DeepMind, Adobe, Toyota, and Bosch. His work has garnered attention from media outlets such as the New York Times, Forbes, TIME, IEEE Spectrum, Popular Science, Telegraph, and Wired.

Host: Assistant Prof. Yue Wang

Webcast: https://usc.zoom.us/j/99488778795?pwd=oXg76V89VYG9b5I0CIEcn2E2Fz7d6z.1

Location: Olin Hall of Engineering (OHE) - 132

WebCast Link: https://usc.zoom.us/j/99488778795?pwd=oXg76V89VYG9b5I0CIEcn2E2Fz7d6z.1

Audiences: Everyone Is Invited

Contact: Thomas Lord Department of Computer Science