CV

Education

B.S. in GitHub, GitHub University, 2012
M.S. in Jekyll, GitHub University, 2014
Ph.D in Version Control Theory, GitHub University, 2018 (expected)

Work experience

Summer 2015: Research Assistant
- Github University
- Duties included: Tagging issues
- Supervisor: Professor Git
Fall 2015: Research Assistant
- Github University
- Duties included: Merging pull requests
- Supervisor: Professor Hub

Skills

Skill 1
Skill 2
- Sub-skill 2.1
- Sub-skill 2.2
- Sub-skill 2.3
Skill 3

Publications

A Stochastic Mortar Mixed Finite Element Method for Flow in Porous Media with Multiple Rock Types

Ganis, B. and Yotov, I. and Zhong, M. (2011). "A Stochastic Mortar Mixed Finite Element Method for Flow in Porous Media with Multiple Rock Types." SIAM Journal on Scientific Computing. 33(3): 1439 - 1474.

Hierarchical Reconstruction Method for Solving Ill-posed Linear Inverse Problems

Zhong, M. (2016). "Hierarchical Reconstruction Method for Solving Ill-posed Linear Inverse Problems." DRUM.

Iterative Refinement of A Modified Lavrentiev Regularization Method for De-convolution of the Discrete Helmholtz Type Differential Filter

Mays, N. and Zhong, M. (2018). "Iterative Refinement of A Modified Lavrentiev Regularization Method for De-convolution of the Discrete Helmholtz Type Differential Filter." arXiv.

Time Relaxation with Iterative Modified Lavrentiev Regularization

Zhong, M. (2018). "Time Relaxation with Iterative Modified Lavrentiev Regularization." arXiv.

Nonparametric inference of interaction laws in systems of agents from trajectory data

Lu, F. and Zhong, M. and Tang, S. and Maggioni, M. (2019). "Nonparametric inference of interaction laws in systems of agents from trajectory data." PNAS. 116(29): 14424 - 14433.

Data-driven discovery of emergent behaviors in collective dynamics

Zhong, M. and Miller, J. and Maggioni, M. (2020). "Data-driven discovery of emergent behaviors in collective dynamics." Physica D: Nonlinear Phenomena. 116(29): 14424 - 14433.

Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems

Miller, J. and Tang, S. and Zhong, M. and Maggioni, M. (2020). "Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems." arXiv.

Learning Interaction Kernels for Agent Systems on Riemannian Manifolds

Maggioni, M. and Miller, J. and Qiu, H. and Zhong, M. (2022). "Learning Interaction Kernels for Agent Systems on Riemannian Manifolds." Proceedsings of the 38th International Conference on Machine Learning. PMLR 130: 7290 - 7300.

Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides

Zhong, M. and Miller, J. and Maggioni, M. (2021). "Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides." arXiv.

On the sparsity of LASSO minimizers in sparse data recovery

Foucart, S. and Tadmor, E. and Zhong, M. (2022). "On the sparsity of LASSO minimizers in sparse data recovery." Construtive Approximation.

Physics-Informed Neural Networks with Adaptive Localized Artificial Viscosity

Coutinho, E.J.R and Dall'Aqua, M. and McClenny, L. and Zhong, M. and Braga-Neto, U. and Gildin, E. (2022). "Physics-Informed Neural Networks with Adaptive Localized Artificial Viscosity." arXiv.

Grassmanian packings: Trust region stochastic tuning for matrix incoherence

Park, J. and Saltijeral, C. and Zhong, M. (2022). "Grassmanian packings: Trust region stochastic tuning for matrix incoherence." 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2022, pp. 1 - 6.

Learning Interaction Variables and Kernels from Observations of Agent-Based Systems

Feng, J. and Maggioni, M. and Martin, P. and Zhong, M. (2022). "Learning Interaction Variables and Kernels from Observations of Agent-Based Systems." IFAC-PapersOnLine, 25th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2022), 55 (30), 162 - 167, 2022.

Using Physics Informed Neural Networks for Supernova Radiative Transfer Simulation

Chen, X. and Jeffery, D. J. and Zhong, M. and McClenny, L. and Braga-Neto, U. and Wang L. (2022). "Using Physics Informed Neural Networks for Supernova Radiative Transfer Simulation." arXiv 2022.

Teaching

Introduction to Data Science

Service and leadership

Currently signed in to 43 different slack teams

Ming Zhong

CV