I am a software engineer with 9 years of experience at NASA, I specialize in developing and leading complex multi-physics simulation platforms enhanced by AI/ML. My journey from Intern to Lead Software Engineer has equipped me with a robust technical background and strong leadership capabilities, including 7 years of team management experience.
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Leading the development and research of predictive simulation software products enhanced by AI/ML.
Predictive Simulation Platforms Design & Development
AI/ML - Surrogate Modeling, Chatbots & Segmentation
Software Testing, Maintenance & Launch
Scalabe Distributed Systems & HPC Solutions
Technical Leadership & Team Management
Data-driven Model Validation & Statistical Analysis
Research on Innovative Physics-Based Modeling
3 years of AI/ML engineering. Directed development of DL tools for video and 3D imaging segmentation. Trained PINNs to develop surrogate models for statistical analysis and fine-tuned GPT-NeoX 1.3B generative AI model to create chatbots for tool documentation. Elevated team proficiency in ML through targeted training sessions.
9 years of software development. Built 6 simulation platforms from scracth to launch. Led CI/CD integration, code optimization, and open-source transitions. Improved UX by adding Conda packages, HPC modules, cloud integration, and SQL web database. Executed large-scale HPC simulations that delivered critical insights for NASA.
7 years of team management. Led 12 NASA projects with 8 engineers, overseeing code development, research, milestones, and long-term planning. Secured $3M in research funding. Coordinated with NASA leadership, managed team administration, hosted 30 annual partner visits, and established 2 international research agreements.
Led applied science projects in material modeling to support NASA missions and technologies. Published 19 journal papers and presented at 21 conferences using large-scale simulation data. Developed multiscale models addressing NASA’s challenges in material response, hypersonic CFD, radiation, and plasma physics.
Description: R&D platform for analyzing porous reactive materials in high-temperature environments, advancing next-generation thermal protection systems
My Role: Lead developer since 2017
Deployment: CI/CD framework, Conda packages, HPC modules, docs, cloud integration, and SQL database
Community: 100+ developers and 200+ users
Language: C/C++ and Python
Release: NASA Open Source
Description: Digital twin of arc heaters, simulating unsteady plasma flows to support aerothermal, electromagnetic, and radiative analysis
My Role: Lead developer since 2016
Deployment: GPU-accelerated radiation optimization, Python tools for visualization, multi-physics integration
Physics: Magnetohydrodynamics and 3D radiation
Language: C/C++ and Python
Release: Internal deployment for NASA analysis
Description: R&D platform for analyzing micro-structural properties of porous materials
My Role: Supervisor since 2019, overseeing code development, research, and long-term planning
Deployment: CI/CD framework, Conda packages, HPC modules, docs, video tutorials
Award: 2022 NASA Software of the Year
Language: C/C++ and Python
Release: NASA Open Source
Description: DSMC code for simulating rarefied gas flows during hypersonic atmospheric entry
My Role: Supervisor since 2019, overseeing code development, research, and long-term planning.
Deployment: HPC modules, user guides, GPU-accelerated computations with Kokkos
Validation: Compared with arc-jet and flight data
Language: C/C++
Release: Sandia Open Source
Developed an equilibrium material response model in PATO. This model accounts for the impact of NuSil, a silicone overcoat used for contamination control during assembly, testing, and launch. The model was applied to heatshields of MSL and Mars 2020 missions, providing accurate predictions to support NASA missions with data-driven decisions.
Developed a suite of modeling tools that integrate aerosol-sampling inlets, thermal protection systems, and optical analysis into a compact Venus probe. These tools predict the aerothermal environment, simulate flow dynamics, and assess particle capture efficiency to guide design improvements and safeguard sample integrity.
Validated the ablation models in PATO using comparisons with MSL/Mars 2020 data and experimental results. The models were benchmarked against both arc-jet tests and in-flight data to ensure accurate predictions of material temperature and recession during atmospheric entry, ensuring reliability for mission-critical design decisions.
Developed embedded flight software to manage CubeSat science data and built a Python-based ground control interface. The code was successfully tested during an Antarctica balloon flight, calibrating a high-energy X-ray detector for solar observations. It ensures real-time data management and enhances system reliability during flight.
Develop ML-integrated simulation platforms
Architect and maintain scalable codebases
Drive HPC performance optimizations for large-scale simulations
Manage 8-member engineering team, directing 5 concurrent projects
Foster collaboration with 100+ external partners
Shape and execute strategic roadmaps
Developed ML-integrated simulation platforms
Wrote, tested, and reviewed production code
Drive HPC performance optimizations for large-scale simulations
Managed 8-member engineering team, leading a total of 12 projects
Collaborated with 10+ external developer teams
Led strategic planning and resource allocation
Built simulation platforms and testing pipelines
Implemented performance tuning strategies
Contributed to core code development
Led 4-member team on digital twin development
Handled project planning and goal setting
Collaborated with 10+ external partners
Developed embedded flight software
Built ground interface for real-time telemetry
Tested code for onboard data processing
Assisted in setting up CubeSat components
Calibrated sensors for Antarctica balloon test
Collaborated with partners on IMU system integration
