Curriculum Vitae

Fields Of Interest

Energy transition, Dynamical systems & control, Reduced-order modeling, Physics-informed machine learning, Uncertainty Quantification

Education

• Ph.D., Applied Mathematics, University of Arizona, 2024 (expected)
• M.S., Applied Mathematics, University of Arizona, 2021
• B.S., Mathematics & Physics, University of Arizona, 2016

Research

• Optimal Natural Gas Flows in a Network with Uncertainty Dynamical systems, numerics, optimal (stochastic) control, differentiable programming, optimization

• Machine Learning Statistical Evolution of the Coarse-Grained Velocity Gradient Tensor Physics-informed machine learning, statistical mechanics, stochastic differential equations, big data

Work experience

• 2020-05-13 to present: Graduate Research Assistant
  • University of Arizona
  • Applied Machine Learning to create hypothesis-free, reduced order models for turbulence.
• 2023-05-13 to 2023-08-15: Google Summer of Code, NumFocus, Contributor
  • Julia, SciML
  • Developed software to symbolically discretize a given PDE on a staggered grid and handle boundary conditions
• 2020-05-13 to 2022-08-15: Graduate Student Researcher
  • Los Alamos National Laboratory
• 2019-08-15 to 2020-05-13: Graduate Teaching Assitant
  • University of Arizona
• 2016-05-13 to 2019-08-15: Sofware Engineer II
  • Raytheon Missile Systems, Tucson, AZ

Skills

• Mathematics
  • Dynamical systems & Numerical methods
  • Optimization & Control
  • Stochastic Processes
  • Analysis
• Software/Computer Science
  • Expertise in developing software for high performance, distributed systems
  • Experience in software design
  • Symbolic Programming
  • Experience adapting algorithms to embedded, real-time systems
• Physics
  • Developing expertise in turbulent flows & statistical mechanics
  • General knowledge of classical and non-relativistic quantum mechanics

Development Tools

• Languages: Julia, Python, C/C++, Bash, Matlab, Ada, Cuda
• HPC: Slurm, development and deployment of parallelizable codes, Docker
• Methodologies: Continuous integration, Test-driven development, Agile
• Open-source/collaboration: git, github, workflows
• ML Workflows: Pytorch, TensorFlow, Flux

Fellowships

• NSF Data-Driven Research Training Group Traineeship
• Roots for Resilience Data Science Scholarship

Service and leadership

• Aug 2023: Organized and presented Programming for Applied Mathematicians Bootcamp for incoming graduate students
• Jul 2023: Mentored undergraduate student as part of NSF Data-Driven REU
• Apr 2023: Organized and presented Introduction to Parallelization for NSF Data-Driven Research Training
• Mar 2023: Graduate Mentor for American Statistical Association DataFest Competition
• Quarterly: Organized and presented Introduction to HPC seminar for Math PhD students
• 2021-22: SIAM Brown Bag Student Colloquium Organizer
• 2018-19: Certified Scrum Master: Scaled Agile Framework

Human Languages

• English
  • Native Speaker
• Spanish
  • Basic

Publications & Talks

Control of Line Pack in Natural Gas System: Balancing Limited Resources under Uncertainty

Criston Hyett, Laurent Pagnier, Jean Alisse, Lilach Sabban, Igal Goldshtein, Michael Chertkov, "Control of Line Pack in Natural Gas System: Balancing Limited Resources under Uncertainty." arXiv2304.01955, 2023.

Physics-informed Machine Learning for Reduced-order Modeling of Lagrangian Turbulence

Yifeng Tian, Michael Woodward, Mikhail Stepanov, Chris Fryer, Criston Hyett, Michael Chertkov, Daniel Livescu, "Physics-informed Machine Learning for Reduced-order Modeling of Lagrangian Turbulence." Bulletin of the American Physical Society, 2022.

Lagrangian Large Eddy Simulations via Physics Informed Machine Learning

Yifeng Tian, Michael Woodward, Mikhail Stepanov, Chris Fryer, Criston Hyett, Daniel Livescu, Michael Chertkov, "Lagrangian Large Eddy Simulations via Physics Informed Machine Learning." arXiv preprint arXiv:2207.04012, 2022.

Physics Informed Machine Learning of SPH: Machine Learning Lagrangian Turbulence

Michael Woodward, Yifeng Tian, Criston Hyett, Chris Fryer, Daniel Livescu, Mikhail Stepanov, Michael Chertkov, "Physics Informed Machine Learning of SPH: Machine Learning Lagrangian Turbulence." arXiv preprint arXiv:2110.13311, 2021.

Talks

Physics Informed Machine Learning with Smoothed Particle Hydrodynamics: Compressiblity and Shocks

Michael Woodward, Yifeng Tian, Criston Hyett, Chris Fryer, Daniel Livescu, Mikhail Stepanov, Michael Chertkov, "Physics Informed Machine Learning with Smoothed Particle Hydrodynamics: Compressiblity and Shocks." Bulletin of the American Physical Society, 2022.

Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning

Michael Chertkov, Yifeng Tian, Mikhail Stepanov, Chris Fryer, Michael Woodward, Criston Hyett, Daniel Livescu, "Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning." Bulletin of the American Physical Society, 2022.

Applicability of Machine Learning Methodologies to Model the Statistical Evolution of the Coarse-Grained Velocity Gradient Tensor

Criston Hyett, Yifeng Tian, Michael Woodward, Michael Chertkov, Daniel Livescu, Mikhail Stepanov, "Applicability of Machine Learning Methodologies to Model the Statistical Evolution of the Coarse-Grained Velocity Gradient Tensor." Bulletin of the American Physical Society, 2022.

Physics Informed Machine Learning of Smooth Particle Hydrodynamics: Validation of the Lagrangian Turbulence Approach

Michael Woodward, Yifeng Tian, Michael Chertkov, Mikhail Stepanov, Daniel Livescu, Criston Hyett, Chris Fryer, "Physics Informed Machine Learning of Smooth Particle Hydrodynamics: Validation of the Lagrangian Turbulence Approach." In the proceedings of APS Division of Fluid Dynamics Meeting Abstracts, 2021.

Physics Informed Machine Learning of Smooth Particle Hydrodynamics: Solving Inverse Problems using a mixed mode approach

Michael Woodward, Michael Chertkov, Yifeng Tian, Mikhail Stepanov, Daniel Livescu, Criston Hyett, Chris Fryer, "Physics Informed Machine Learning of Smooth Particle Hydrodynamics: Solving Inverse Problems using a mixed mode approach." In the proceedings of APS Division of Fluid Dynamics Meeting Abstracts, 2021.

Machine Learning Statistical Evolution of the Coarse-Grained Velocity Gradient Tensor

Criston Hyett, Michael Chertkov, Yifeng Tian, Daniel Livescu, Mikhail Stepanov, "Machine Learning Statistical Evolution of the Coarse-Grained Velocity Gradient Tensor." In the proceedings of APS Division of Fluid Dynamics Meeting Abstracts, 2021.

Machine Learning Lagrangian Large Eddy Simulations with Smoothed Particle Hydrodynamics

Yifeng Tian, Michael Chertkov, Michael Woodward, Mikhail Stepanov, Chris Fryer, Criston Hyett, Daniel Livescu, "Machine Learning Lagrangian Large Eddy Simulations with Smoothed Particle Hydrodynamics." In the proceedings of APS Division of Fluid Dynamics Meeting Abstracts, 2021.

Data-Analysis of the Coarse-Grained Velocity Gradient Tensor

Criston Hyett, Yifeng Tian, Michael Chertkov, Daniel Livescu, Mikhail Stepanov, "Data-Analysis of the Coarse-Grained Velocity Gradient Tensor." In the proceedings of APS Division of Fluid Dynamics Meeting Abstracts, 2021.

Machine Learning Statistical Lagrangian Geometry of Turbulence

Criston Hyett, Michael Chertkov, Yifeng Tian, Daniel Livescu, "Machine Learning Statistical Lagrangian Geometry of Turbulence." In the proceedings of APS Division of Fluid Dynamics Meeting Abstracts, 2020.

References

• PhD Advisor, Dr. Michael Chertkov