Speakers

• Gang Bao

  Zhejiang University - Inverse Source Scattering in Wave Propagation
• Victor Barranca

  Swarthmore College - Reconstruction of Sparse Connectivity in Neuronal Networks Using Compressive Sensing of Network Dynamics
• Guoqiang Bi

  Univeristy of Science and Technology of China - Neuronal learning - from synapse to network
• Roberto Camassa

  University of North Carolina at Chapel Hill - Hydrodynamic models and boundary confinement effects
• Weinan E

  Peking University - Machine learning and multi-scale modeling
• Jianfeng Feng

  Fudan University - Peer into your brain via contiguous scale data
• David Hansel / Carl van Vreeswijk

  University Paris Descartes - Emergent orientation selectivity from random networks in mouse visual cortex: Mice are not Cats - An (almost) exact proof
• Jingfang Huang

  University of North Carolina at Chapel Hill - A Fast Hierarchical Algorithm for Computing High Dimensional Truncated Multivariate Gaussian Probabilities and Expectations
• Song Jiang

  Beijing Institute of Applied Physics and Computational Mathematics - Magnetic stabilizing effects on the Rayleigh-Taylor instability in non-resistive magnetohydrodynamics
• Peter Kramer

  East China Normal University - Spatial Stochastic Models for Molecular Motors Attaching and Detaching from Parallel Microtubules
• Chengyu Li

  Chinese Academy of Sciences - Relationship between cross-region correlation in firing and neuronal response to behavioral events
• Tatsien Li

  Fudan University - Exact boundary controllability of nodal profile for Saint-Venant system on a network with loops
• Chun Liu

  Department of Applied Mathematics, Illinois Institute of Technology - Topics in diffusion: boundaries and temperature
• Peter Markowich

  King Abdullah University of Science and Technology - Continuum Modeling of Transportation Networks with Partial Differential Equations
• David McLaughlin

  New York University - Large-scale Computational Model of Mouse V1
• Katie Newhall

  University of North Carolina at Chapel Hill - Synchrony and Information Flow in Integrate-and-Fire Neuronal Network Models
• Benoit Perthame

  Laboratoire J.-L. Lions, Sorbonne Universite - Fom voltage-conductance kinetic models to integrate&fire equation for neural assemblies
• Estaban Tabak

  New York University, Courant Institute - Conditional Density Estimation through Optimal Transport
• Louis Tao

  Peking University - A Coarse-Graining Framework for Spiking Neuronal Networks: from Strongly-Coupled Integrate-and-Fire Neurons to Augmented Systems of ODEs
• Xiaoming Wang

  Fudan University and Florida State University - The coupling and decoupling of free flow and porous media flow
• Si Wu

  Peking University - Push-pull Feedback Implements Hierarchical Associative Memory
• Xiaohui Zhang

  State Key Laboratory of Cognitive Neuroscience & Learning, Beijing Normal University - Circuitry Mechanisms for Associative Learning and Cortical Activity Oscillation