[Raise-faculty] Meet with Qi Zhou (Friday AI Seminar speaker)

[Vassilis Tsotras]

Dear RAISE faculty,
you probably saw the email about this Friday's AI seminar speaker, Prof. 
Zhou from Northwestern U. (let me know if you did not get the 
announcement and I can add you to the seminar email-list).

If you would like to talk with him, or join for lunch please use this 
google doc
https://docs.google.com/document/d/1TjptPbKtCHweclTaL1Tg2uckULxoAGxJU2rFgATlOnw/edit?tab=t.0

Best,
Vassilis


-------- Original Message --------
Subject: [AI Seminar Series] Seminar by Prof. Qi Zhou (Northwestern U.), 
Friday January 31, 12-1pm, MRB Seminar Room
Date: 2025-01-26 19:51
 From: Vassilis Tsotras <tsotras at cs.ucr.edu>
To: raise-seminar at lists.ucr.edu

The next talk at the AI Seminar Series, will be next Friday, January 31, 
12:00-1:00pm at the MRB Seminar Room (1st floor).

**** Pizza and refreshments will be provided ****

To keep track of the number of attendees, please *register* at:
https://www.eventbrite.com/e/ai-seminar-series-tickets-1222434898689

The talk will be given by Prof. Qi Zhu, Department of Electrical and 
Computer Engineering, Northwestern University

TITLE:
"Building Safe and Robust Embodied AI Systems"

ABSTRACT:
Embodied AI systems are cyber-physical systems that employ advanced 
machine learning techniques to perceive, analyze, and interact with 
their environment. As these systems become more prevalent in fields such 
as self-driving cars and robotics, ensuring their safety and robustness 
has become a critical and yet challenging requirement. In this talk, I 
will discuss key challenges for building safe and robust embodied AI 
systems, particularly those arising from uncertainties in system inputs, 
model inaccuracies, and the lack of analyzability of neural 
network-based components. I will present our recent work on tackling 
these challenges by developing a holistic set of end-to-end 
verification, design, and adaptation methods to ensure the safe and 
robust application of neural networks in the perception, planning, and 
control of embodied AI systems.


Bio: Qi Zhu is a Professor in the ECE Department at Northwestern 
University. He earned his Ph.D. in EECS from the University of 
California, Berkeley in 2008 and a B.E. in CS from Tsinghua University 
in 2003. His research interests include design automation for 
cyber-physical systems (CPSs) and Internet of Things (IoTs), safe and 
robust machine learning for embodied AI systems, energy-efficient CPSs, 
cyber-physical security, and system-on-chip design, with applications in 
domains such as connected and autonomous vehicles, smart buildings, 
advanced manufacturing, wearable computing, and robotic systems. He is a 
recipient of the NSF CAREER award, the IEEE TCCPS Early-Career Award, 
and the Humboldt Research Fellowship for Experienced Researchers. He 
received best paper awards at DAC 2006, DAC 2007, ICCPS 2013, ACM TODAES 
2016, and DATE 2022. He is the Conference Chair for IEEE TCCPS, and VP 
of Initiatives for IEEE CEDA. He is an Associate Editor for IEEE TCAD, 
IEEE TCASAI, and ACM TCPS, and has served as a Guest Editor for the 
Proceedings of the IEEE, ACM TCPS, IEEE T-ASE, IEEE IoT Journal, 
Elsevier JSA, and Elsevier Integration, the VLSI journal.


------------------------------------
Sponsored by the RAISE at UCR Institute, the AI Seminar Series presents 
speakers working on cutting edge Foundational AI or apply AI in their 
research. The goal of these seminars is to inform the UCR community 
about current trends in AI research and promote collaborations between 
faculty in this emerging field.
These seminars are open to interested faculty and graduate students. 
Please forward this email to other colleagues or graduate students in 
your lab that may be interested. After the seminar a discussion will 
follow for questions, open problems, ideas for possible collaborations 
etc.

Sincerely,
Vassilis Tsotras
Professor, CSE Department
co-Director, RAISE at UCR Institute

Amit Roy-Chowdhury
Professor, ECE Department
co-Director, RAISE at UCR Institute