Cheng Ly

Assistant Professor Department of Statistical Sciences and Operations Research
(804) 828-5842



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.


  1. 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.
  2. 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.
  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 & 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.
  5. 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


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.