CV
My CV!
Contact Information
| Name | Christian Engman |
| Professional Title | Student Researcher, Los Alamos National Laboratory |
| cengman3@gatech.edu | |
| Location | Los Alamos, New Mexico NM 87544 |
Professional Summary
Early career researcher with interest in scientific computing, machine learning, and numerical methods.
Experience
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2025 - Los Alamos, NM
Student Research Intern
Los Alamos National Laboratory
Applying reduced-order modelling and machine learning to radiography physics
- Generating synthetic data for the training of ML models to reconstruct objects from radiological measurements.
- Training reduced-order models for forward proton and neutron models to provide a fast alternative to Monte-Carlo simulations
- Developing software in Python using NumPy, SciPy, matplotlib, JAX, and proprietary LANL software.
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2024 - 2024 Seattle, WA
Software Engineering Intern
Meta
- Built debugging tools for Meta’s user-generated AI characters as they launch on Facebook and Instagram
- Presented critical information to developers about discoverability, visibility, and ranking to save development time.
- Developed a backend in PHP Hack, using GraphQL for API requests, and React and Relay for a web app frontend.
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2023 - 2024 Atlanta, GA
Undergraduate Teaching Assistant
Georgia Tech School of Mathematics
UTA for MATH 2552 Differential Equations
- Led 30 student recitations and aimed at strengthening students’ understanding of course material twice weekly.
- Held weekly office hours and graded assignments to assist instructors in managing 100+ student course sections.
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2023 - 2023 Bellevue, WA
Software Development Engineering Intern
Amazon
- Developed and deployed a microservice to generate compliance documents for $300M/yr of imports to China.
- Safely migrated 10 requests/sec. of traffic from legacy system to new microservice with no negative client impact
- Utilized Java for development, deploying on native AWS platforms integrated with Amazon’s microservice fabric.
Education
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2022 - 2025 Atlanta, GA
BS
Georgia Institute of Technology
Mathematics, Computer Science
- Algorithms, Data Structures, Operating Systems, Processor Design, Compilers, Computer Organization, Systems and Networks, Machine Learning, Deep Learning, Statistical Theory for Machine Learning, Probability Theory, Statistical Theory, Graph Theory, Coding Theory, Numerical Linear Algebra, Iterative Methods for Systems of Equations, Numerical Approximation Theory, Real Analysis, Abstract Algebra, Topology, Differential Geometry
Awards
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2026 Science Undergraduate Laboratory Internship
U.S. Department of Energy Office of Scientific and Techical Innovation
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2025 Outstanding Graduating Mathematics Major
Georgia Tech School of Mathematics
Award granted by the School of Mathematics to 5 top mathematics majors in the graduating class
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2024 PURA Travel Award
Georgia Tech UROP Office
$1000 to present at SIAM MDS 2024
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2022 Putnam Competition Top 500 Competitor
Mathematical Association of America
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2023 Putnam Competition Top 500 Competitor
Mathematical Association of America
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2022 National Merit Scholarship Finalist
National Merit Scholarship Corporation
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2021 Putnam Competition Top 500 Competitor
Mathematical Association of America
Skills
Programming: Python, C, Java, JavaScript, C++, Julia, PHP
Mathematics: Numerical analysis, finite element methods, numerical linear algebra
Technical: Web development, machine learning, data analysis, cloud, Systems programming
Languages
English : Native speaker
Spanish : Intermediate
Interests
Phyiscs: Dynamic imaging and radiography, quantum computing and information theory, particle physics, plasma physics
Mathematics: Numerical methods, partial differential equations, probability theory, graph theory, functional analysis
Computer Science: Machine learning theory, deep learning, high-performance computing algorithms, graph algorithms, theory of computation
Music: I play guitar, saxophone, and keyboard, mostly in jazz, rock, and metal styles
Projects
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Sparse Recovery of Poincare-Steklov Operators from Matrix-Vector Products
Developing numerical algorithms for the sparse approximate recovery of Poincare-Steklov operators of elliptic partial differential equations from small amounts of data.
- Exploring applications in elliptic inverse problems, including electric impedance tomography, and domain decomposition methods for elliptic problems.