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Chris Lynn is an Assistant Professor of Physics and a member of the Quantitative Biology Institute and the Wu Tsai Institute at Yale University. He is also an Early Career Member-at-Large of the Division of Biophysics in the American Physical Society. His SPIN lab studies the statistical physics of the brain and other complex living systems. Previously, he was a James S. McDonnell Postdoctoral Fellow at the Center for the Physics of Biological Function at Princeton University and the City University of New York. Prof. Lynn earned his Ph.D. in Physics & Astronomy at the University of Pennsylvania in 2020 under the guidance of Prof. Dani Bassett, and he obtained B.A.’s with High Honors in Physics and Mathematics from Swarthmore College in 2014.

What do you do with Data Science?

I seek to understand how collective patterns of activity emerge, and how the underlying networks of interactions self-organize, in the brain. While these goals have long inspired neuroscientists, recent experimental progress has provided glimpses of neural function and structure on unprecedented scales. We can now map the physical wiring among tens of thousands of neurons and record the simultaneous activity of over one million neurons. Despite these advances in neuroimaging, fundamental questions remain: How do large-scale patterns of activity emerge from networks of fine-scale interactions? And how do these patterns dynamically evolve to process information? Answering these questions requires new theoretical insights and computational techniques to distill the recent explosion of high-quality data down to basic underlying principles. In my research, I directly address this gap between theory and data by developing computational tools grounded in statistical physics and information theory. References: Christopher W. Lynn, Caroline M. Holmes, & Stephanie E. Palmer. Heavy-tailed neuronal connectivity arises from Hebbian self-organization. Nature Physics (2024). Christopher W. Lynn, Caroline M. Holmes, William Bialek, & David J. Schwab. Decomposing the local arrow of time in interacting systems. Physical Review Letters 129,11 (2022). Christopher W. Lynn, Eli J. Cornblath, Lia Papadopoulos, Maxwell A. Bertolero, & Dani S. Bassett. Broken detailed balance and entropy production in the human brain. Proceedings of the National Academy of Sciences 118,47 (2021). Christopher W. Lynn & Dani S. Bassett. Quantifying the compressibility of complex networks. Proceedings of the National Academy of Sciences 118,32 (2021). Christopher W. Lynn, Lia Papadopoulos, Ari E. Kahn, & Dani S. Bassett. Human information processing in complex networks. Nature Physics 16,9 (2020). Christopher W. Lynn, Ari E. Kahn, Nathaniel Nyema, & Dani S. Bassett. Abstract representations of events arise from mental errors in learning and memory. Nature Communications 11,1 (2020). Christopher W. Lynn & Dani S. Bassett. The physics of brain network structure, function, and control. Nature Reviews Physics 1 (2019).

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