BEGIN:VCALENDAR
VERSION:2.0
PRODID:-// - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://tilos.ai
X-WR-CALDESC:Events for 
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20250309T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20251102T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20260308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20261101T090000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20270314T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20271107T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20260206T110000
DTEND;TZID=America/Los_Angeles:20260206T120000
DTSTAMP:20260404T033834
CREATED:20251014T201307Z
LAST-MODIFIED:20260304T210204Z
UID:7668-1770375600-1770379200@tilos.ai
SUMMARY:Optimization for ML and AI Seminar: Extended Convex Lifting for Policy Optimization in Control
DESCRIPTION:Yang Zheng\, UC San Diego \nAbstract: Direct policy search has achieved great empirical success in reinforcement learning. Many recent studies have revisited its theoretical foundation for continuous control\, which reveals elegant nonconvex geometry in various benchmark problems. In this talk\, we introduce an Extended Convex Lifting (ECL) framework\, which reveals hidden convexity in classical optimal and robust control problems from a modern optimization perspective. Our ECL offers a bridge between nonconvex policy optimization and convex reformulations. Despite non-convexity and non-smoothness\, the existence of an ECL not only reveals that minimizing the original function is equivalent to a convex problem\, but also certifies a class of first-order non-degenerate stationary points to be globally optimal. This ECL framework encompasses many benchmark control problems\, including LQR\, LQG\, state-feedback\, and output-feedback H-infinity robust control. We believe that the ECL framework may be of independent interest for analyzing nonconvex problems beyond control. \n\nYang Zheng is an Assistant Professor in the ECE Department at UC San Diego. His research focuses on control theory\, convex and nonconvex optimization\, and their applications to autonomous vehicles and traffic systems. He received his DPhil (Ph.D.) in Engineering Science from the University of Oxford in 2019\, and his B.E. and M.S. degrees from Tsinghua University in 2013 and 2015\, respectively. His work has been recognized with several awards\, including the 2019 European Ph.D. Award on Control for Complex and Heterogeneous Systems\, the 2022 Best Paper Award from IEEE Transactions on Control of Network Systems\, the 2023 Best Graduate Teacher Award from UC San Diego’s ECE Department\, the 2024 NSF CAREER Award\, and the 2025 Donald P. Eckman Award from the American Automatic Control Council.
URL:https://tilos.ai/event/optimization-for-ml-and-ai-seminar-with-yang-zheng-uc-san-diego/
LOCATION:HDSI 123 and Virtual\, 3234 Matthews Ln\, La Jolla\, CA\, 92093\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/10/zheng-yang-scaled-e1769464299795.jpg
END:VEVENT
END:VCALENDAR