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| Mark Newman (physicist) | |
|---|---|
| Name | Mark Newman |
| Birth date | 1965 |
| Birth place | United Kingdom |
| Fields | Physics, Complex systems, Network science |
| Workplaces | University of Michigan, Santa Fe Institute, Los Alamos National Laboratory |
| Alma mater | University of Cambridge, University of Oxford |
| Doctoral advisor | M. E. Fisher |
| Known for | Network theory, community detection, centrality measures |
| Awards | Sloan Research Fellowship, James S. McDonnell Foundation |
Mark Newman (physicist) is a British physicist and scholar noted for foundational work in network science, statistical mechanics, and complex systems. He is a professor associated with the University of Michigan and has held appointments at the Santa Fe Institute and Los Alamos National Laboratory, contributing to interdisciplinary research that connects physics with sociology, biology, and computer science. His work on network structure, community detection, and centrality metrics has influenced research across epidemiology, ecology, and computer networks.
Newman was born in the United Kingdom and studied physics at the University of Cambridge before pursuing graduate studies at the University of Oxford. At Oxford, he completed doctoral work under advisors connected to the legacy of Michael E. Fisher and trained in theoretical statistical mechanics traditions associated with Paul Dirac's and Erwin Schrödinger's conceptual lineages. During his early career he engaged with research communities around the Royal Society and participated in conferences hosted by CERN and the Institute for Advanced Study.
Newman joined the faculty of the University of Michigan after research positions at Los Alamos National Laboratory and affiliations with the Santa Fe Institute. At Michigan, he has held appointments in departments that collaborate with the School of Information, the Department of Electrical Engineering and Computer Science, and the Center for the Study of Complex Systems. He has been a visiting scholar at Princeton University and a speaker at the National Academy of Sciences symposia. Newman has served on editorial boards for journals associated with the American Physical Society and the Royal Society Publishing.
Newman's research established quantitative methods in network theory; he developed models and metrics used in studies by researchers at Harvard University, Stanford University, and Massachusetts Institute of Technology. He formalized algorithms for community detection used alongside methods from Sociometry and techniques popularized by teams at Google and Facebook. His work on centrality measures built on concepts from Freeman (social network analysis), connecting to eigenvector centrality notions relevant to Perron–Frobenius theorem applications in linear algebra. Newman introduced modularity optimization approaches that influenced community detection algorithms implemented in software libraries maintained by teams at GitHub and research groups at Carnegie Mellon University.
He contributed to the theoretical understanding of random graphs, expanding on models by Erdős–Rényi and correlating with developments in percolation theory and phase transitions studied at Bell Labs and IBM Research. Newman's analyses of epidemic spreading used frameworks comparable to those in work from the Centers for Disease Control and Prevention and influenced modeling in epidemiology during outbreaks studied by researchers at Johns Hopkins University. He has coauthored papers on network null models and assortative mixing that engage with studies by the National Institutes of Health and practitioners at Microsoft Research.
Newman has received recognition including a Sloan Research Fellowship and support from the James S. McDonnell Foundation. He has been invited to present named lectures at institutions such as Columbia University and Yale University and has been elected to fellowships in organizations associated with the American Physical Society and the Institute of Physics. His work has been cited in award announcements from the Royal Society and used as part of curriculum recommendations by panels at the National Science Foundation.
- Newman, M. E. J., "The structure and function of complex networks", Proceedings of the National Academy of Sciences. - Newman, M. E. J., "Modularity and community structure in networks", Physical Review E. - Newman, M. E. J., "Detecting community structure in networks", European Physical Journal B. - Newman, M. E. J., "Networks: An Introduction", published by Oxford University Press. - Newman, M. E. J., Strogatz, S.H., Watts, D.J., "Random graphs with arbitrary degree distributions and their applications", Physical Review E.
At the University of Michigan, Newman has taught courses bridging physics and data science and has supervised graduate students who later joined faculties at University of California, Berkeley, Imperial College London, and ETH Zurich. His mentorship has connected early-career researchers to networks of collaborators at Los Alamos National Laboratory, the Santa Fe Institute, and research groups at Princeton University. He regularly leads workshops at conferences organized by NetSci and summer schools supported by the European Research Council.
Category:Living people Category:British physicists Category:Network scientists