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Faculty
Alex Gates
Assistant Professor of Data Science
School of Data Science
Email:
agates@virginia.edu
Additional Contact Information
Website: www.alexandergates.net
Office: 1919 Ivy Rd., Rm. 341
Lab: Connected Data Hub
Pronouns: he/him
Alex Gates is a computational social scientist with an avid curiosity for understanding how connectivity shapes human behavior.
Prior to joining the University of Virginia School of Data Science as an Assistant Professor in 2022, he was an Associate Research Scientist in Northeastern University’s Network Science Institute and the Department of Sociology.
His work has been featured in top journals, including Nature, The Proceedings of the National Academy of Sciences, and The Journal of Machine Learning. Gates also embraces multi-modal communication channels, including award-winning data visualizations, journal covers, interactive web-based models, and videos. Examples of his work include exploring the interdisciplinary structure of a major scientific journal and a video on the collaboration network emerging from one of the NSF's largest gender equity efforts.
Gates holds a joint Ph.D. in Informatics (complex networks) and Cognitive Science from Indiana University, Bloomington, an M.Sc. in Complex Systems Modeling from King's College London, and a B.A. in Mathematics from Cornell University.
Education
- Ph.D., Informatics and Cognitive Science, Indiana University, Bloomington
- M.Sc., Complex Systems Modeling, King's College London
- B.A., Mathematics, Cornell University
Data Science Domains
Analytics, Design, Data + Society
Areas of Practice
Artificial Intelligence and Machine Learning, Business, Markets, and Policy, Computational Sciences, Human-Centered Design and Interaction, Theory, Foundations, and Advanced Methodologies
Methodologies
Network Science, Computational Social Science, Complex Systems, Community Detection
Selected Publications
Shekhtman, L.M., Gates, A.J. & Barabasi, A.-L. (2024) Mapping
philanthropic support of US science. Scientific Reports 14, 9397.
Gates, A., Brattig Correia, R., Wang, X., Rocha, L. (2021). The effective graph reveals redundancy, canalization, and control pathways in biochemical regulation and signaling. Proceedings of the National Academy of Sciences, 118 (12), e2022598118
Huang, J., Gates, A., Sinatra, R., Barabási, A. (2020). Historical comparison of gender inequality in scientific careers across countries and disciplines. Proceedings of the National Academy of Sciences, 117 (9), 4609-4616
Gates, A., Ke, Q., Varol, O., Barabási, A. (2019). Nature’s reach: narrow work has broad impact. Nature, 575 (7781), 32-34
Gates, A., Ahn, Y. (2017). The impact of random models on clustering similarity. Journal of Machine Learning Research, 18, 1-28
Ke, Q., Gates, A.J. & Barabasi, A.-L. (2023) A network normalized impact measure reveals successful periods of scientific discovery across discipline. Proc. Natl. Acad. Sci. USA (PNAS) 120, 48 e230937812
Herrera-Guzmán, Y., Gates, A.J., Candia, C. & Barabasi, A.-L.∗ (2023) Quantifying hierarchy and prestige in US ballet academies as social predictors of career success.
Scientific Reports 13, 1859