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:20240310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20241103T090000
END:STANDARD
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
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20251119T110000
DTEND;TZID=America/Los_Angeles:20251119T120000
DTSTAMP:20260404T045625
CREATED:20251105T193505Z
LAST-MODIFIED:20260227T215217Z
UID:7735-1763550000-1763553600@tilos.ai
SUMMARY:TILOS-SDSU Seminar: Certifiably Correct Machine Perception
DESCRIPTION:David Rosen\, Northeastern University \nAbstract: Many fundamental machine perception and state estimation tasks require the solution of a high-dimensional nonconvex estimation problem; this class includes (for example) the fundamental problems of simultaneous localization and mapping (in robotics)\, 3D reconstruction (in computer vision)\, and sensor network localization (in distributed sensing). Such problems are known to be computationally hard in general\, with many local minima that can entrap the smooth local optimization methods commonly applied to solve them. The result is that standard machine perception algorithms (based upon local optimization) can be surprisingly brittle\, often returning egregiously wrong answers even when the problem to which they are applied is well-posed. \nIn this talk\, we present a novel class of certifiably correct estimation algorithms that are capable of efficiently recovering provably good (often globally optimal) solutions of generally-intractable machine perception problems in many practical settings. Our approach directly tackles the problem of nonconvexity by employing convex relaxations whose minimizers provide provably good approximate solutions to the original estimation problem under moderate measurement noise. We illustrate the design of this class of methods using the fundamental problem of pose-graph optimization (a mathematical abstraction of robotic mapping) as a running example. We conclude with a brief discussion of open questions and future research directions. \n\nDavid M. Rosen is an Assistant Professor in the Departments of Electrical & Computer Engineering and Mathematics and the Khoury College of Computer Sciences (by courtesy) at Northeastern University\, where he leads the Robust Autonomy Laboratory (NEURAL). Prior to joining Northeastern\, he was a Research Scientist at Oculus Research (now Meta Reality Labs) from 2016 to 2018\, and a Postdoctoral Associate at MIT’s Laboratory for Information and Decision Systems (LIDS) from 2018 to 2021. He holds the degrees of B.S. in Mathematics from the California Institute of Technology (2008)\, M.A. in Mathematics from the University of Texas at Austin (2010)\, and ScD in Computer Science from the Massachusetts Institute of Technology (2016). \n\nHe is broadly interested in the mathematical and algorithmic foundations of trustworthy machine perception\, learning\, and control. His work has been recognized with the IEEE Transactions on Robotics Best Paper Award (2024)\, an Honorable Mention for the IEEE Transactions on Robotics Best Paper Award (2021)\, a Best Student Paper Award at Robotics: Science and Systems (2020)\, a Best Paper Award at the International Workshop on the Algorithmic Foundations of Robotics (2016)\, and selection as an RSS Pioneer (2019).
URL:https://tilos.ai/event/tilos-sdsu-seminar-with-david-rosen-northeastern/
LOCATION:Lamden Hall 341 (SDSU) and Virtual\, San Diego\, CA\, 92182\, United States
CATEGORIES:TILOS Seminar Series,TILOS Sponsored Event
ATTACH;FMTTYPE=image/jpeg:https://tilos.ai/wp-content/uploads/2025/11/rosen-david-scaled-e1762371210779.jpg
END:VEVENT
END:VCALENDAR