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DTSTART;TZID=America/New_York:20260317T090000
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SUMMARY:SIG-MET Distinguished Speaker Series: Lecture by Dr. Yong-Yeol (YY) Ahn
DESCRIPTION:Title: The Geometry of Science What would scientometrics look like if scientific ideas lived in a concrete physical space? Deep representation learning now allows us to imagine such a shared knowledge embedding space where scholarly works\, ideas\, and other entities can coexist. Yet these powerful models are often black boxes\, hard to translate into interpretable metrics. In this talk\, I show that a fundamental understanding of embedding methods can make this space interpretable\, allowing its geometry to be measured directly. The flow of scientists between institutions follows a gravity law governed by distance in the space\, and scientific disruption can be captured through the geometric displacement of a field's trajectory without relying on sparse local citation links. This embedding-based framework may offer a unified language for describing how science attracts\, disrupts\, and moves\, complementing traditional citation-based indicators. Speaker: Dr. Yong-Yeol (YY) Ahn Dr. Yong-Yeol (YY) Ahn is a network and data scientist whose work combines network science\, machine learning\, and the study of complex social\, biological\, and information systems. He is a Quantitative Foundation Distinguished Professor at the University of Virginia's School of Data Science. Before joining UVA\, he was a Professor at Indiana University's CNetS\, Luddy School of Informatics\, Computing\, and Engineering and a Visiting Professor at MIT. Earlier\, he worked as a postdoctoral research associate at the Center for Complex Network Research at Northeastern University and as a visiting researcher at the Center for Cancer Systems Biology at Dana-Farber Cancer Institute after completing his PhD in Statistical Physics from KAIST. His research focuses on the architectures of complex systems how networks shape behavior\, cognition\, and scientific progress and on developing methods in network analysis\, machine learning\, and natural language processing to investigate these mechanisms at scale. He is the co-author of Working with Network Data. His work has been recognized with several honors\, including the Microsoft Research Faculty Fellowship.
X-ALT-DESC;FMTTYPE=text/html:Title: The Geometry of Science \;
What would scientometrics look like if scientific ideas lived in a concrete physical space? Deep representation learning now allows us to imagine such a shared knowledge embedding space where scholarly works\, ideas\, and other entities can coexist. Yet these powerful models are often black boxes\, hard to translate into interpretable metrics. In this talk\, I show that a fundamental understanding of embedding methods can make this space interpretable\, allowing its geometry to be measured directly. The flow of scientists between institutions follows a gravity law governed by distance in the space\, and scientific disruption can be captured through the geometric displacement of a field'\;s trajectory &mdash\; without relying on sparse local citation links. This embedding-based framework may offer a unified language for describing how science attracts\, disrupts\, and moves\, complementing traditional citation-based indicators. \;
Speaker: Dr. Yong-Yeol (YY) Ahn \;
Dr. Yong-Yeol (YY) Ahn is a network and data scientist whose work combines network science\, machine learning\, and the study of complex social\, biological\, and information systems. He is a Quantitative Foundation Distinguished Professor at the University of Virginia&rsquo\;s School of Data Science. Before joining UVA\, he was a Professor at Indiana University&rsquo\;s CNetS\, Luddy School of Informatics\, Computing\, and Engineering and a Visiting Professor at MIT. Earlier\, he worked as a postdoctoral research associate at the Center for Complex Network Research at Northeastern University and as a visiting researcher at the Center for Cancer Systems Biology at Dana-Farber Cancer Institute after completing his PhD in Statistical Physics from KAIST. His research focuses on the architectures of complex systems&mdash\;how networks shape behavior\, cognition\, and scientific progress&mdash\;and on developing methods in network analysis\, machine learning\, and natural language processing to investigate these mechanisms at scale. He is the co-author of Working with Network Data. His work has been recognized with several honors\, including the Microsoft Research Faculty Fellowship.
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DTSTAMP:20260406T062246Z
URL:https://asist.growthzoneapp.com/events/Details/sig-met-distinguished-speaker-series-lecture-by-dr-yong-yeol-yy-ahn-1671037?sourceTypeId=Hub
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