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AI Reshaping Knowledge Space Measurement: SIGMET Distinguished Series Talk The Geometry of Science Successfully Held

On March 17, 2026, the SIGMET invited talk titled "The Geometry of Science" was successfully held. The seminar featured Yong-Yeol Ahn, a Distinguished Professor from the University of Virginia, and attracted experts and scholars from 10 countries and regions, including China, the USA, the Netherlands, Germany, and South Korea.

Professor Ahn explored the potential of redefining scientific measurement within multi-dimensional geometric spaces using embedding-based measures. Drawing a parallel to the history of astronomical observation, he emphasized that the precision of measurement tools is a decisive factor in scientific discovery. He then demonstrated how representation learning in deep learning can capture semantic relationships to construct knowledge space models capable of simulating scientist mobility and knowledge diffusion.

Addressing core issues in scientific evaluation, Professor Ahn proposed a new method based on graph embeddings. By comparing the "past" and "future" vectors of a paper, this approach achieves a more robust measurement of disruptive research. It effectively resolves the noise issues prevalent in traditional indicators when identifying disruptive papers and simultaneous discoveries in science. Furthermore, he introduced a citation model based on knowledge space. Unlike traditional models that focus excessively on the intrinsic attributes of a paper, this model incorporates the perspective of collective attention, allowing it to more naturally generate and explain common citation patterns such as Sleeping Beauties, exponential growth, and preferential attachment.

During the subsequent discussion, participants engaged in deep dialogues on topics such as the boundaries of Large Language Models (LLMs) in peer review and the applicability of knowledge space models. By providing a geometric lens, this lecture offered a novel observational tool for understanding scholarly evolution. This cross-border, multidisciplinary dialogue not only sparked academic inspiration but also provided valuable insights for the transformation of research evaluation paradigms in the age of AI.

 

PAST EVENTS

SIG-MET Distinguished Speaker Series: Lecture by Dr. Yong-Yeol (YY) Ahn

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.

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.

Lecture Guide:

Registration - https://www2.asist.org/ap/Events/Register/xRFXn32sMCYC7

Location - Online event, the lecture link will be sent to registered attendees via email.

Time - 9:00-10:00 AM (EDT) USA time on Tuesday, March 17th, 2026.

Organizers:

ASIS&T SIG-MET

Faculty of Education, The University of Hong Kong

School of Information Management, Sun Yat-sun University

 

SIG-MET Distinguished Speaker Series: Opening Lecture by Dr. Cassidy Sugimoto

Speaker: Dr. Cassidy Sugimoto

Dr. Cassidy Sugimoto is Tom and Marie Patton Professor and School Chair in the School of Public Policy at Georgia Institute of Technology. Her research examines the formal and informal ways in which knowledge is produced, disseminated, consumed, and supported, with an emphasis on issues of diversity, equity, and inclusion.

Title: How to Be a Scientific Superpower

This talk explores the systemic factors and policy frameworks that enable nations and institutions to achieve scientific excellence and global influence in research. Drawing on her metascience research, Sugimoto explores how indicators around investments and production have been the primary drivers of what constitutes a "superpower". Weaving together historical evidence, she argues for indicators that consider diffusion and epistemic influence and demonstrates the current polycentric organization of global science. The conclusion will focus on how scientists, policymakers, and administrators can be more intentional in setting the global scientific agenda.

Conference Guide:

Registration - https://www2.asist.org/events/Details/sig-met-invited-talk-1-1565651?sourceTypeId=Hub

Location - Online event, the lecture link will be sent to registered attendees via email.

Time - 9:00-10:00 am USA time on Friday, December 5th, 2025.

Sponsors: