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Faculty
Alex Gates
Assistant Professor of Data Science
School of Data Science
Email:
agates@virginia.edu
Additional Contact Information
Alex Gates is a network scientist whose research explores how patterns of connectivity influence human behavior, decision-making, and the dynamics of complex systems, from science to art to business. His research takes a multidisciplinary approach, blending methods from computational social science, data science, and network science with sociological theory. His current focus is on the Science of Science, where he analyzes how organizational dynamics and strategic decisions influence innovation, creativity, and success.
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; Graphical Models; Community Detection; Cluster Analysis; Natural Language Processing; Causal Inference; Machine Learning; Statistical Modeling; Data Visualization
Selected Publications
- Shekhtman, L.M., Gates, A.J. & Barabasi, A.-L.* (2024) Mapping philanthropic support of US science. Scientific Reports 14, 9397.
- 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
- 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
- Nature 150 Interactive