BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical and Computer Engineering - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Electrical and Computer Engineering
X-ORIGINAL-URL:https://engineering.jhu.edu/ece
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260128T150000
DTEND;TZID=America/New_York:20260128T163000
DTSTAMP:20260605T054814
CREATED:20260121T224456Z
LAST-MODIFIED:20260121T224456Z
UID:577521-1769612400-1769617800@engineering.jhu.edu
SUMMARY:Reliable and Actionable AI Copilots for Medicine
DESCRIPTION:Abstract: While modern AI models achieve impressive performance on standard benchmarks\, they remain brittle in high-stakes domains such as medicine\, where uncertainty is pervasive and errors are costly. The central challenge is not model capacity\, but bridging the gap between predictive accuracy in controlled settings and reliable behavior in real-world clinical workflows. Existing methods often struggle with noisy\, multimodal data and lack principled mechanisms for incorporating expert knowledge during deployment.  \nIn this talk\, I present a set of machine learning methods designed to address these challenges. I will first describe approaches that leverage training dynamics and implicit signals to learn robust models from uncurated\, imperfect data without amplifying noise. I will then introduce an inference-time framework that treats natural language not merely as input\, but as a control interface\, allowing domain experts to steer model behavior and agentic systems through structured linguistic feedback. By integrating robust learning and language-based model steering within agent-based framework\, I demonstrate how AI agents can operate in closed-loop clinical settings with transparent reasoning\, adaptive behavior\, and human oversight.  \nBio: Sheng Liu is a Postdoctoral Researcher at Stanford University\, working with James Zou and Lei Xing. He received his PhD in Data Science from New York University. His research develops theory and algorithms for trustworthy and steerable AI\, with the goal of bridging the gap between AI developed in the lab and actionable AI systems in the real world. His work is motivated by healthcare\, with applications to dementia prediction and cancer treatment planning. Sheng’s research has been published in leading venues\, including Nature and other Nature Research journals\, and presented in oral and spotlight sessions at top machine learning conferences such as CVPR\, NeurIPS\, ICML\, and ICLR. His work has been featured by media outlets including The New York Times\, YouTube\, and Stanford News. 
URL:https://engineering.jhu.edu/ece/event/reliable-and-actionable-ai-copilots-for-medicine/
LOCATION:Shaffer 3\, United States
CATEGORIES:Faculty Candidate Seminar
END:VEVENT
END:VCALENDAR