Yong-Yeol (YY) Ahn

Quantitative Foundation Distinguished Professor
School of Data Science
University of Virginia

CV: cv.pdf
Email: yyahn@virginia.edu
Office: 1919 Ivy Rd., Room 444

Hello! 👋 Welcome to my homepage! You can find news, YY’s bio, and selected publications below. See Y Lab for more information about my research group & open positions, and Research for the research overview and the full list of publications. I go by “YY” and you can find me on various social platforms below.

News

• 2025-08-31 Paper accepted! Devin’s paper “Cognitive Linguistic Identity Fusion Score (CLIFS): A Scalable Cognition‑Informed Approach to Quantifying Identity Fusion from Text” has been accepted in EMNLP‘25!
• 2025-08-31 I will be one of the keynote speakers at CIKM‘25!
• 2025-08-26 Check out Munjung’s PhD student highlight featured on SDS homepage!
• 2025-08-15 I will serve as a member of the National Academies’ Standing Committee on Advancing Science Communication.
• 2025-08-10 We’ve moved! After about 14 years at Indiana University, now I’m a faculty members at University of Virginia School of Data Science! See my LinkedIn post.

Bio

Yong-Yeol (YY) Ahn is a Quantitative Foundation Distinguished Professor at the University of Virginia School of Data Science. He was previously a Professor at Indiana University, CNetS, Luddy School of Informatics, Computing, and Engineering (2011–2025) and a Visiting Professor at MIT (2020–2021). 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 earning his PhD in Statistical Physics from KAIST in 2008. His research focuses on data science, spanning methodological work in network science, machine learning, and AI, as well as their applications to computational social science, computational neuroscience and biology, and the science of science. He is a recipient of several awards, including Microsoft Research Faculty Fellowship and LinkedIn Economic Graph Challenge.

Research

We study the hidden architectures of complex systems through network science and machine learning. Inspired by real-world problems, we develop network science, machine learning, and natural language processing methods; we leverage deep understanding of these methods to find novel solutions for real-world challenges. Some highlights below & See Research for the full publication list:

Interpretable embedding space and its applications

• Beyond Distance: Mobility Neural Embeddings Reveal Visible and Invisible Barriers in Urban Space (under review in PNAS).
• A semantic embedding space based on large language models for modelling human beliefs (Nature Human Behaviour, 2025).
• Uncovering simultaneous breakthroughs with a robust measure of disruptiveness (under review in Science Advances).
• Unsupervised embedding of trajectories captures the latent structure of scientific migration (PNAS, 2023).
• Neural Embeddings of Scholarly Periodicals Reveal Complex Disciplinary Organizations (Science Advances, 2021).

Network science and machine learning

• Implicit degree bias in the link prediction task (ICML‘25)
• Network community detection via neural embeddings (Nature Communications, 2024).
• Residual2Vec: Debiasing graph embedding with random graphs (NeurIPS‘21).
• Link communities reveal multiscale complexity in networks (Nature, 2010).

Science of Science

• Community-centric modeling of citation dynamics explains collective citation patterns in science, law, and patents (under review in Nature Communications).
• Persistent Hierarchy in Contemporary International Collaboration (submitted).
• Cooperation and interdependence in global science funding (submitted).
• Uncovering simultaneous breakthroughs with a robust measure of disruptiveness (under review in Science Advances).
• Unsupervised embedding of trajectories captures the latent structure of scientific migration (PNAS, 2023).
• Neural Embeddings of Scholarly Periodicals Reveal Complex Disciplinary Organizations (Science Advances, 2021).
• The latent structure of global scientific development (Nature Human Behaviour, 2022).
• Factors affecting sex-related reporting in medical research: a cross-disciplinary bibliometric analysis (Lancet, 2019).

Future of work and AI

• The Potential Impact of Disruptive AI Innovations on U.S. Occupations (submitted).
• AI exposure predicts unemployment risk: A new approach to technology-driven job loss (PNAS Nexus, 2025).

Beliefs, contagion, culture

• A semantic embedding space based on large language models for modelling human beliefs (Nature Human Behaviour, 2025).
• Sameness entices, but novelty enchants in fanfiction online (Humanities & Social Sciences Communications, 2025).
• Emergence of simple and complex contagion dynamics from weighted belief networks (Science Advances, 2024).
• The effectiveness of backward contact tracing in networks (Nature Physics, 2021).
• A Network Framework of Cultural History (Science, 2014).
• Flavor network and the principles of food pairing (Scientific Reports, 2011).

Biology and neuroscience

• Cooperative and Competitive Spreading Dynamics on the Human Connectome (Neuron, 2015).
• Evidence for Network Evolution in an Arabidopsis Interactome Map (Science, 2011).