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ORCIDMichael J. Shelley
Person information
- affiliation: New York University, New York, NY, USA
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2020 – today
- 2024
[j24]Suryanarayana Maddu
, Scott Weady, Michael J. Shelley:
Learning fast, accurate, and stable closures of a kinetic theory of an active fluid. J. Comput. Phys. 504: 112869 (2024)- [i6]Suryanarayana Maddu, Victor Chardès, Michael J. Shelley:
Inferring biological processes with intrinsic noise from cross-sectional data. CoRR abs/2410.07501 (2024) - 2023
- [j23]Zijun Zhang, Adam R. Lamson, Michael J. Shelley, Olga G. Troyanskaya:
Interpretable neural architecture search and transfer learning for understanding CRISPR-Cas9 off-target enzymatic reactions. Nat. Comput. Sci. 3(12): 1056-1066 (2023) - [i5]Zijun Zhang, Adam R. Lamson, Michael J. Shelley, Olga G. Troyanskaya:
Interpretable neural architecture search and transfer learning for understanding sequence dependent enzymatic reactions. CoRR abs/2305.11917 (2023) - [i4]Victor Chardès, Suryanarayana Maddu, Michael J. Shelley:
Stochastic force inference via density estimation. CoRR abs/2310.02366 (2023) - 2022
- [j22]Scott Weady, Michael J. Shelley, David B. Stein:
A fast Chebyshev method for the Bingham closure with application to active nematic suspensions. J. Comput. Phys. 457: 110937 (2022) - 2021
- [j21]Jinzi Mac Huang, Michael J. Shelley, David B. Stein:
A stable and accurate scheme for solving the Stefan problem coupled with natural convection using the Immersed Boundary Smooth Extension method. J. Comput. Phys. 432: 110162 (2021) - [i3]Scott Weady, David B. Stein, Michael J. Shelley:
A fast Chebyshev method for the Bingham closure with application to active nematic suspensions. CoRR abs/2106.14817 (2021) - 2020
- [j20]Wen Yan, Eduardo Corona, Dhairya Malhotra, Shravan K. Veerapaneni, Michael J. Shelley:
A scalable computational platform for particulate Stokes suspensions. J. Comput. Phys. 416: 109524 (2020) - [i2]Jinzi Mac Huang, Michael J. Shelley, David B. Stein:
A stable and accurate scheme for solving the Stefan problem coupled with natural convection using the Immersed Boundary Smooth Extension method. CoRR abs/2006.04736 (2020)
2010 – 2019
- 2019
- [i1]Wen Yan, Eduardo Corona, Dhairya Malhotra, Shravan K. Veerapaneni, Michael J. Shelley:
A scalable computational platform for particulate Stokes suspensions. CoRR abs/1909.06623 (2019) - 2018
- [j19]Wen Yan, Michael J. Shelley:
Flexibly imposing periodicity in kernel independent FMM: A multipole-to-local operator approach. J. Comput. Phys. 355: 214-232 (2018) - [j18]Wen Yan, Michael J. Shelley:
Universal image systems for non-periodic and periodic Stokes flows above a no-slip wall. J. Comput. Phys. 375: 263-270 (2018) - [j17]Jiajun Tong, Michael J. Shelley:
Directed Migration of Microscale Swimmers by an Array of Shaped Obstacles: Modeling and Shape Optimization. SIAM J. Appl. Math. 78(5): 2370-2392 (2018) - 2017
- [j16]Ehssan Nazockdast, Abtin Rahimian, Denis Zorin, Michael J. Shelley:
A fast platform for simulating semi-flexible fiber suspensions applied to cell mechanics. J. Comput. Phys. 329: 173-209 (2017) - 2012
- [j15]Leslie Greengard, Michael J. Shelley:
Editorial. Adv. Comput. Math. 36(1): 1 (2012) - 2011
- [j14]Eric E. Keaveny, Michael J. Shelley:
Applying a second-kind boundary integral equation for surface tractions in Stokes flow. J. Comput. Phys. 230(5): 2141-2159 (2011) - 2010
- [j13]Kukjin Kang, Michael J. Shelley, James Andrew Henrie, Robert Shapley:
LFP spectral peaks in V1 cortex: network resonance and cortico-cortical feedback. J. Comput. Neurosci. 29(3): 495-507 (2010) - [j12]Alex Kanevsky, Michael J. Shelley, Anna-Karin Tornberg:
Modeling simple locomotors in Stokes flow. J. Comput. Phys. 229(4): 958-977 (2010) - [j11]Shawn W. Walker, Michael J. Shelley:
Shape optimization of peristaltic pumping. J. Comput. Phys. 229(4): 1260-1291 (2010)
2000 – 2009
- 2009
- [j10]Wei Zhu, Michael J. Shelley, Robert Shapley:
A neuronal network model of primary visual cortex explains spatial frequency selectivity. J. Comput. Neurosci. 26(2): 271-287 (2009) - 2008
- [j9]Melinda Evrithiki Koelling, Robert Shapley, Michael J. Shelley:
Retinal and cortical nonlinearities combine to produce masking in V1 responses to plaids. J. Comput. Neurosci. 25(2): 390-400 (2008) - [j8]Gregor Kovacic, Louis Tao, David Cai, Michael J. Shelley:
Theoretical analysis of reverse-time correlation for idealized orientation tuning dynamics. J. Comput. Neurosci. 25(3): 401-438 (2008) - 2006
- [j7]Petri Fast, Michael J. Shelley:
Moore's law and the Saffman-Taylor instability. J. Comput. Phys. 212(1): 1-5 (2006) - 2002
- [j6]Michael J. Shelley, David McLaughlin:
Coarse-Grained Reduction and Analysis of a Network Model of Cortical Response: I. Drifting Grating Stimuli. J. Comput. Neurosci. 12(2): 97-122 (2002) - [j5]Michael J. Shelley, David McLaughlin, Robert Shapley, Jacob Wielaard:
States of High Conductance in a Large-Scale Model of the Visual Cortex . J. Comput. Neurosci. 13(2): 93-109 (2002) - 2001
- [j4]Michael J. Shelley, Louis Tao:
Efficient and Accurate Time-Stepping Schemes for Integrate-and-Fire Neuronal Networks. J. Comput. Neurosci. 11(2): 111-119 (2001) - 2000
- [j3]M. C. Pugh, Dario L. Ringach, Robert Shapley, Michael J. Shelley:
Computational Modeling of Orientation Tuning Dynamics in Monkey Primary Visual Cortex. J. Comput. Neurosci. 8(2): 143-159 (2000)
1990 – 1999
- 1993
- [j2]Thomas Y. Hou, John S. Lowengrub, Michael J. Shelley:
The Convergence of an Exact Desingularization for Vortex Methods. SIAM J. Sci. Comput. 14(1): 1-18 (1993) - [j1]John S. Lowengrub, Michael J. Shelley, Barry Merriman:
High-Order and Efficient Methods for the Vorticity Formulation of the Euler Equations. SIAM J. Sci. Comput. 14(5): 1107-1142 (1993)
Coauthor Index
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