Associate Professor Department of Statistical Sciences and Operations Research
Ph.D. in Mathematics, Courant Institute (New York University), New York, NY 2002-2007 Title: Population Density Approach to Neural Network Modeling: Dimension Reduction Analysis, Techniques, and Firing Rate Dynamics Advisor: Dr. Daniel Tranchina
M.S. in Mathematics, Courant Institute (New York University), New York, NY 2002-2004
B.S. in Applied Mathematics with a specialization in computing, University of California at Los Angeles, Los Angeles, CA 1998-2002
My primary research area is computational neuroscience, with a specific focus on the variability, or fluctuations, of cortical neural network activity and their dynamics with sensory inputs. There are mathematical issues that arise, leading to challenges in: developing numerical methods to solve probability density equations, large-scale (Monte-Carlo) simulations, as well as applied analysis to describe such complicated stochastic systems. A variety of models are required for different purposes because there is a wide range of biological complexity. I have worked on: noisy neural oscillators, spiking stochastic networks, detailed biophysical cellular modeling, and questions regarding coding of sensory signals.
- C. Ly, 2014. Dynamics of Coupled Noisy Neural Oscillators with Heterogeneous Phase Resetting Curves, SIAM Journal on Applied Dynamical Systems, Vol. 13: pp. 1733–1755.
- C. Ly, J. Middleton, & B. Doiron, 2012. Cellular and circuit mechanisms maintain low spike co-variability and enhance population coding in somtaosensory cortex, Frontiers in Computational Neuroscience, Vol. 6: pp. 1–26.
- C. Ly & B. Doiron, 2009. Divisive Gain Modulation with Dynamic Stimuli in Integrate-and-fire Neurons, PLoS Computational Biology, 5(4): e1000365.
- C. Ly & B. Ermentrout, 2009. Synchronization Dynamics of Two Coupled Neural Oscillators Receiving Shared and Unshared Noisy Stimuli, Journal of Computational Neuroscience, Vol. 26: pp. 425–443.
- C. Ly & D. Tranchina, 2007. Critical Analysis of Dimension Reduction for a Moment Closure Method in a Population Density Approach to Neural Network Modeling, Neural Computation, Vol. 19: pp. 2032–2092.
Society of Industrial and Applied Mathematics (SIAM)
Pi Mu Epsilon- National Mathematics Honors society (PME)
Virginia Academy of Sciences (Lifetime member) Phi Beta Kappa, Eta of California
HONORS and PROFESSIONAL ACTIVITIES
Simons Foundation Collaboration Grant for Mathematicians (2015-2020)
NSF Mathematical Sciences Postdoctoral Research Fellowship (2007-2010)
Department of Defense Graduate Fellowship (2003-2006)
Reviewer for: PLoS Computational Biology, Journal of Neurophysiology, Journal of Computational Neuroscience, Journal of Mathematical Biology, Journal of Neurophysiology, Mathematical Biosciences and Engineering, Neural Computation, SIAM Journal on Applied Dynamical Systems.