BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Chemical and Biomolecular Engineering - ECPv6.16.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Chemical and Biomolecular Engineering
X-ORIGINAL-URL:https://engineering.jhu.edu/chembe
X-WR-CALDESC:Events for Department of Chemical and Biomolecular 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:20260129T103000
DTEND;TZID=America/New_York:20260129T113000
DTSTAMP:20260612T184836
CREATED:20260115T200459Z
LAST-MODIFIED:20260115T200459Z
UID:52053-1769682600-1769686200@engineering.jhu.edu
SUMMARY:Seminar Series: Jason Yang
DESCRIPTION:Accelerating Protein Engineering with Artificial Intelligence\nJason Yang\, Caltech \nAbstract: \nProteins are central to health\, sustainability\, and chemical synthesis\, yet engineering them for specific functions is challenging due to the vastness of the sequence design space. Directed evolution\, inspired by natural evolution\, has enabled remarkable advances but is slow and limited to local optimization. In this talk\, I present my research on integrating machine learning with experimental workflows to overcome key bottlenecks of directed evolution. First\, I introduce Active Learning-Assisted Directed Evolution (ALDE)\, which leverages Bayesian optimization to enable efficient and rapid protein fitness optimization. Next\, I discuss Contrastive Reaction-Enzyme Pretraining (CREEP)\, a retrieval model for discovering enzymes with new-to-nature functions. Finally\, I present emerging generative approaches to unify these two perspectives\, to enable holistic data-driven protein engineering. Together\, these innovations point toward a future where AI-driven strategies can automate biomolecular engineering: unlocking sustainable synthesis\, novel therapeutics\, and programmable biology at the molecular scale. \nBio: \nJason Yang is a final-year Chemical Engineering PhD candidate at Caltech\, co-advised by Professor Frances Arnold and Professor Yisong Yue\, where he is supported by the NSF graduate research fellowship program and the Google PhD Fellowship. He received dual BS degrees in Chemical Engineering and Applied Math at Yale University and was awarded the Barry Goldwater Scholarship. Overall\, Jason is interested in developing machine learning-assisted workflows for protein discovery\, design\, and optimization. Additionally\, he has applied machine learning\, computational modeling\, and experimentation to real-world applications through internships at Profluent Bio\, Genentech\, and NREL. \n  \n10:30am\, Remsen Hall 1
URL:https://engineering.jhu.edu/chembe/event/seminar-series-jason-yang/
LOCATION:Remsen Hall 1
CATEGORIES:Seminars and Lectures
END:VEVENT
END:VCALENDAR