I’m a Ph.D. candidate in Physics at the University of Pennsylvania (expected 2020), working under the supervision of Prof. Danielle S. Bassett and previously under Prof. Daniel D. Lee (now at Cornell Tech). Through my research, I enjoy studying how simple interactions at small scales (e.g., between humans or neurons) can give rise to complex behaviors at large scales (e.g., spikes in internet usage in human populations or large-scale synchronization in the brain). To understand these phenomena, I like to adapt and extend intuitions from statistical mechanics, network science, and information theory. My research thus far can be summarized by a few driving questions: How do large-scale surges in human activity emerge from simple fine-scale interactions [1]? Furthermore, given an understanding of the network of interactions between people, can we optimally control the behavior of the entire population [2,3,4]? On a somewhat unrelated note, how do individual humans brave the noise in their environment to pick out structures and patterns that allow them to make accurate predictions [5]?

Also, check out my new review with Dani Bassett where we discuss the foundations and frontiers of the physics of brain network structure, function, and control [6]!

Before coming to Penn, I studied at Swarthmore College, where I earned my BA in Physics and Mathematics with High Honors and played on the soccer team (go Garnet!). While in college, I was a Lee Teng Fellow in Accelerator Science at Fermilab with Dr. Tanaji Sen, working on simulations of channeling radiation experiments at the Fermilab Accelerator Science and Technology (FAST) Facility. I was also a Moncrief Undergraduate Intern at the University of Texas at Austin under the supervision of Prof. Dmitrii E. Makarov, where I performed molecular dynamics simulations to quantify self-induced friction in proteins.

References:

[1] Christopher W. Lynn, Lia Papadopoulos, Daniel D. Lee, and Danielle S. Bassett. Surges of collective human activity emerge from simple pairwise correlations. In Revision, Physical Review X. (arxiv.org/abs/1803.00118)

[2] Christopher W. Lynn and Daniel D. Lee. Maximizing Activity in Ising Networks via the TAP Approximation. Association for the Advancement of Artificial Intelligence (AAAI). 2018. (arxiv.org/abs/1803.00110)

[3] Christopher W. Lynn and Daniel D. Lee. Statistical Mechanics of Influence Maximization with Thermal Noise. EPL (Europhysics Letters) 117.6: 66001. 2017.

[4] Christopher W. Lynn and Daniel D. Lee. Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution. Advances in Neural Information Processing Systems (NIPS). 2016. (arxiv.org/abs/1608.06850)

[5] Christopher W. Lynn, Ari E. Kahn, and Danielle S. Bassett. Structure from noise: Mental errors yield abstract representations of events. In Revision, Nature Communications. (arxiv.org/abs/1805.12491)

[6] Christopher W. Lynn and Danielle S. Bassett. The physics of brain network structure, function, and control. Submitted, Nature Reviews Physics. (arxiv.org/abs/1809.06441)

Links:

My Google scholar page

Danielle Bassett’s website

Daniel Lee’s website

Penn Physics and Astronomy

Contact:

Department of Physics and Astronomy
209 S. 33rd St.
Philadelphia, PA 19104
Office 2C9

chlynn@sas.upenn.edu

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