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DTSTAMP:20260404T051747
CREATED:20250924T154049Z
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UID:7606-1764759600-1764763200@tilos.ai
SUMMARY:TILOS-SDSU Seminar: 95 Percent: Bridging the Gap Between Prototype and Product
DESCRIPTION:Jeremy Schwartz\, Zoox \nAbstract: When transitioning from the academic world to the professional world of engineering\, one of the most common pitfalls is failing to understand the difference between a compelling prototype and a successful product. This talk will focus on that distinction. We will discuss the differences between them\, and the work required to evolve a good prototype into a real product. We will also discuss some common pitfalls encountered in product development\, and some of the practical software design considerations to keep in mind for development of robust\, mature code. The talk will include examples from my background developing robotic systems for air\, space\, and ground. \n\nJeremy Schwartz is a robotics engineer at Zoox with expertise in a wide variety of areas of mechanical and electrical engineering and computer science. His primary professional expertise is in autonomy and behavioral algorithms\, and he has worked in the aerospace industry as well as ground robotics\, specializing in autonomous systems of all kinds.
URL:https://tilos.ai/event/tilos-sdsu-seminar-with-jeremy-schwartz-of-zoox/
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/2025/09/schwartz-jeremy-e1758728403382.jpeg
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DTSTART;TZID=America/Los_Angeles:20251203T130000
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CREATED:20250930T163903Z
LAST-MODIFIED:20260304T210653Z
UID:7627-1764766800-1764770400@tilos.ai
SUMMARY:Optimization for AI and ML Seminar: Training Neural Networks at Any Scale
DESCRIPTION:Volkan Cevher\, École Polytechnique Fédérale de Lausanne \nAbstract: At the heart of deep learning’s transformative impact lies the concept of scale–encompassing both data and computational resources\, as well as their interaction with neural network architectures. Scale\, however\, presents critical challenges\, such as increased instability during training and prohibitively expensive model-specific tuning. Given the substantial resources required to train such models\, formulating high-confidence scaling hypotheses backed by rigorous theoretical research has become paramount. \nTo bridge theory and practice\, the talk explores a key mathematical ingredient of scaling in tandem with scaling theory: the numerical solution algorithms commonly employed in deep learning\, spanning domains from vision to language models. We unify these algorithms under a common master template\, making their foundational principles transparent. In doing so\, we reveal the interplay between adaptation to smoothness structures via online learning and the exploitation of optimization geometry through non-Euclidean norms. Our exposition moves beyond simply building larger models–it emphasizes strategic scaling\, offering insights that promise to advance the field while economizing on resources. \n\nVolkan Cevher received the B.Sc. (valedictorian) in electrical engineering from Bilkent University in Ankara\, Turkey\, in 1999 and the Ph.D. in electrical and computer engineering from the Georgia Institute of Technology in Atlanta\, GA in 2005. He was a Research Scientist with the University of Maryland\, College Park from 2006-2007 and also with Rice University in Houston\, TX\, from 2008-2009. Currently\, he is an Associate Professor at the Swiss Federal Institute of Technology Lausanne and a Faculty Fellow in the Electrical and Computer Engineering Department at Rice University. His research interests include machine learning\, signal processing theory\, optimization theory and methods\, and information theory. Dr. Cevher is an ELLIS fellow and was the recipient of the Google Faculty Research award in 2018\, the IEEE Signal Processing Society Best Paper Award in 2016\, a Best Paper Award at CAMSAP in 2015\, a Best Paper Award at SPARS in 2009\, and an ERC CG in 2016 as well as an ERC StG in 2011.
URL:https://tilos.ai/event/optimization-for-ai-and-ml-seminar-with-volkan-cevher-epfl/
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/2025/09/cevher-volkan-e1759250260485.jpg
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