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:20260325T110000 DTEND;TZID=America/Los_Angeles:20260325T120000 DTSTAMP:20260325T225742 CREATED:20260310T175540Z LAST-MODIFIED:20260314T155938Z UID:8191-1774436400-1774440000@tilos.ai SUMMARY:TILOS-SDSU Seminar: Autopilots Need Parachutes: Reliability Lessons from LLM-Automated Embedded AI Systems DESCRIPTION:Roberto Morabito\, EURECOM \nAbstract: Embedded AI systems are becoming increasingly complex to develop and maintain\, requiring specialized workflows that span data processing\, model conversion\, optimization\, and deployment across heterogeneous hardware platforms. Recently\, large language models have emerged as a promising tool to automate parts of this lifecycle. In this talk\, I present recent work investigating the use of generative AI models as orchestration agents for embedded machine learning pipelines. Using an automated system that leverages LLMs to generate and iteratively refine software artifacts for embedded platforms\, we evaluate the feasibility of automating key stages of the AI lifecycle. Our empirical results reveal both the promise and the limitations of this approach. Generative models can significantly accelerate development workflows. However\, they also introduce instability\, iterative failure modes\, and unpredictable operational costs. I will discuss the main failure patterns observed in practice and outline research directions aimed at improving reliability through hybrid reasoning frameworks and system-level feedback mechanisms. \n\nRoberto Morabito is an Assistant Professor in the Networked Systems group of the Communication Systems Department at EURECOM\, France\, and a Docent at the University of Helsinki. Before joining EURECOM\, he was a Senior Researcher in the Department of Computer Science at the University of Helsinki. Earlier in his career\, he spent eight years at Ericsson Research Finland\, where he worked on cloud platforms\, IoT systems\, and cyber-physical systems. He received his PhD in Networking Technology from Aalto University in 2019 and was a postdoctoral researcher at the EDGE Lab\, School of Electrical and Computer Engineering\, Princeton University. His research lies at the intersection of networked systems\, edge computing\, and distributed AI\, focusing on the design and lifecycle management of AI systems operating under computing and networking resource constraints. \nZoom: https://SDSU.zoom.us/j/89168949542 URL:https://tilos.ai/event/tilos-sdsu-seminar-autopilots-need-parachutes-reliability-lessons-from-llm-automated-embedded-ai-systems/ 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/2026/03/morabito-roberto-e1773165764846.jpeg END:VEVENT END:VCALENDAR