Office: Room 4126, Grace E. Harris Hall
B.S., Applied Mathematics with a specialization in computing, University of California at Los Angeles, 2002
M.S., Mathematics, Courant Institute (New York University), 2004
Ph.D., Mathematics, Courant Institute (New York University), 2007 (Advisor: Dr. Daniel Tranchina)
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.
1. A. Barreiro, S.H. Gautam, W.L. Shew, & C. Ly, 2017. A Theoretical Framework for Analyzing Coupled Neuronal Networks: Application to the Olfactory System, PLoS Computational Biology, 13(10): e1005780.
2. 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.
3. C. Ly & B. Doiron, 2009. Divisive Gain Modulation with Dynamic Stimuli in Integrate-and-fire Neurons, PLoS Computational Biology, 5(4): e1000365.
4. 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 (Lifetime member)
Pi Mu Epsilon- National Mathematics Honors society (PME)
Virginia Academy of Sciences (Lifetime member)
Phi Beta Kappa, Eta of California