The Convergence of an Exact Desingularization for Vortex Methods. - Thomas Y. Hou 001; John S. Lowengrub; Michael J. Shelley (SIAM J. Sci. Comput., 1993)

High-Order and Efficient Methods for the Vorticity Formulation of the Euler Equations. - John S. Lowengrub; Michael J. Shelley; Barry Merriman (SIAM J. Sci. Comput., 1993)

Computational Modeling of Orientation Tuning Dynamics in Monkey Primary Visual Cortex. - M. C. Pugh; Dario L. Ringach; Robert Shapley; Michael J. Shelley (J. Comput. Neurosci., 2000)

Efficient and Accurate Time-Stepping Schemes for Integrate-and-Fire Neuronal Networks. - Michael J. Shelley; Louis Tao (J. Comput. Neurosci., 2001)

Coarse-Grained Reduction and Analysis of a Network Model of Cortical Response: I. Drifting Grating Stimuli. - Michael J. Shelley; David McLaughlin (J. Comput. Neurosci., 2002)

States of High Conductance in a Large-Scale Model of the Visual Cortex . - Michael J. Shelley; David McLaughlin; Robert Shapley; Jacob Wielaard (J. Comput. Neurosci., 2002)

Moore's law and the Saffman-Taylor instability. - Petri Fast; Michael J. Shelley (J. Comput. Phys., 2006)

Retinal and cortical nonlinearities combine to produce masking in V1 responses to plaids. - Melinda Evrithiki Koelling; Robert Shapley; Michael J. Shelley (J. Comput. Neurosci., 2008)

Theoretical analysis of reverse-time correlation for idealized orientation tuning dynamics. - Gregor Kovacic; Louis Tao; David Cai; Michael J. Shelley (J. Comput. Neurosci., 2008)

A neuronal network model of primary visual cortex explains spatial frequency selectivity. - Wei Zhu; Michael J. Shelley; Robert Shapley (J. Comput. Neurosci., 2009)

LFP spectral peaks in V1 cortex: network resonance and cortico-cortical feedback. - Kukjin Kang; Michael J. Shelley; James Andrew Henrie; Robert Shapley (J. Comput. Neurosci., 2010)

Modeling simple locomotors in Stokes flow. - Alex Kanevsky; Michael J. Shelley; Anna-Karin Tornberg (J. Comput. Phys., 2010)

Shape optimization of peristaltic pumping. - Shawn W. Walker; Michael J. Shelley (J. Comput. Phys., 2010)

Applying a second-kind boundary integral equation for surface tractions in Stokes flow. - Eric E. Keaveny; Michael J. Shelley (J. Comput. Phys., 2011)

Editorial. - Leslie Greengard; Michael J. Shelley (Adv. Comput. Math., 2012)

A fast platform for simulating semi-flexible fiber suspensions applied to cell mechanics. - Ehssan Nazockdast; Abtin Rahimian; Denis Zorin; Michael J. Shelley (J. Comput. Phys., 2017)

Flexibly imposing periodicity in kernel independent FMM: A multipole-to-local operator approach. - Wen Yan; Michael J. Shelley (J. Comput. Phys., 2018)

Universal image systems for non-periodic and periodic Stokes flows above a no-slip wall. - Wen Yan; Michael J. Shelley (J. Comput. Phys., 2018)

Directed Migration of Microscale Swimmers by an Array of Shaped Obstacles: Modeling and Shape Optimization. - Jiajun Tong; Michael J. Shelley (SIAM J. Appl. Math., 2018)

A scalable computational platform for particulate Stokes suspensions. - Wen Yan; Eduardo Corona; Dhairya Malhotra; Shravan K. Veerapaneni; Michael J. Shelley (J. Comput. Phys., 2020)

A stable and accurate scheme for solving the Stefan problem coupled with natural convection using the Immersed Boundary Smooth Extension method. - Jinzi Mac Huang; Michael J. Shelley; David B. Stein (J. Comput. Phys., 2021)

A fast Chebyshev method for the Bingham closure with application to active nematic suspensions. - Scott Weady; Michael J. Shelley; David B. Stein (J. Comput. Phys., 2022)

Interpretable neural architecture search and transfer learning for understanding sequence dependent enzymatic reactions. - Zijun Zhang; Adam R. Lamson; Michael J. Shelley; Olga G. Troyanskaya (CoRR, 2023)

Stochastic force inference via density estimation. - Victor Chardès; Suryanarayana Maddu; Michael J. Shelley (CoRR, 2023)

Interpretable neural architecture search and transfer learning for understanding CRISPR-Cas9 off-target enzymatic reactions. - Zijun Zhang; Adam R. Lamson; Michael J. Shelley; Olga G. Troyanskaya (Nat. Comput. Sci., 2023)

Inferring biological processes with intrinsic noise from cross-sectional data. - Suryanarayana Maddu; Victor Chardès; Michael J. Shelley (CoRR, 2024)

Learning fast, accurate, and stable closures of a kinetic theory of an active fluid. - Suryanarayana Maddu; Scott Weady; Michael J. Shelley (J. Comput. Phys., 2024)