Build the models
Python, Rust, and C++ code for orbit determination, conjunction screening, and mission studies.
Virginia Tech · Aerospace Engineering
Truman DeWalch
Aerospace Engineering Ph.D. Candidate · Conjunction Analysis, Transfer Planning, and Mission Software
I build software for conjunction analysis, transfer planning, and mission design.
- Python, Rust, and C++ tools for orbital analysis and conjunction studies
- Led a 5-person systems team at The Aerospace Corporation
- Built the reusable event bank behind published astrodynamics work
Experience
Selected Experience
A mix of applied research, production-minded simulation software, and technical leadership in aerospace settings.
MTS Graduate Intern
The Aerospace Corporation · Northern Virginia / Remote
- Built a bistatic radar sensor model for an enterprise mission-analysis environment.
- Led a 5-person systems engineering sub-team across mission architecture research deliverables.
- Connected legacy tools so analysts could run high-level trade studies in one workflow.
Graduate Research Assistant — Just-in-Time Collision Avoidance
Virginia Tech · Blacksburg, VA
- Built Python, Rust, and C++ software for orbit determination and conjunction analysis.
- Developed a mixed-fidelity workflow that keeps early screening fast and uses higher-fidelity propagation where dust-cloud geometry matters.
- Compared optimizer families and deployer constellations under matched budgets, shared event evidence, and common scoring rules.
- Created a reproducible 1,000-event LEO conjunction bank for evaluation, benchmarking, and publication work.
Graduate Research Assistant — Eclipse Transient OD
Virginia Tech · Blacksburg, VA
- Designed a modular Python simulation architecture for eclipse-based autonomous navigation studies.
- Built a statistical atmospheric model from real climate data and used it in the estimation workflow.
- Applied Unscented Kalman Filters to estimate orbital state from eclipse-transient measurements.
- Presented the resulting approach at AIAA SciTech 2024.
Graduate Student Intern
TrustPoint GNSS · Northern Virginia
- Created high-fidelity models of satellites, ground stations, and onboard clocks for GNSS simulation.
- Implemented Extended Kalman Filters and Batch Least Squares for precise orbit tracking.
- Integrated TDOA, FDOA, and geometric-range measurement models into the simulation engine.
About
What I Build
Most of my work sits between astrodynamics research and the tools people actually use. At Virginia Tech, The Aerospace Corporation, and TrustPoint, I've built Python, Rust, and C++ systems for orbital analysis, led small technical teams, and published work in astrodynamics venues.
Make them usable
Turn research ideas into tools people can rerun, test, and trust.
Lead the work
Lead small technical teams and keep cross-functional projects moving.
Work
Selected Work
If you want the technical version, these pages show how the modeling and trade-study work actually fits together.
Simulation Framework
See Details
→ Simulated Space Environment
Propagation and analysis code for running orbital studies without rebuilding the stack every time.
Dissertation Research
Just-in-Time Collision Avoidance
Dissertation work on how a deployer architecture could respond to risky debris conjunctions.
See Details → Optimization FrameworkConstellation Optimization
Shared-evidence studies of how optimizer choice and constellation design change the trade space.
See Details → Approach
Methodology
These are the pieces I keep fixed so the comparison stays honest across optimizers and constellation studies.
- 01
Reusable event evidence
Catalog-anchored hazardous-event set reused across optimizer and constellation studies
- 02
Two-lane dynamics policy
Screening stays analytical while released dust gets high-fidelity propagation
- 03
Objective / gate separation
Released dust is optimized while intercepted mass remains a strict feasibility gate
- 04
Fair optimizer comparison
Matched budgets, shared event draws, and common scoring keep comparisons interpretable
Optimization
Optimization Background
My dissertation is a stochastic optimization problem end to end: population-based search over constellation designs, where every objective is a noisy Monte Carlo estimate and every candidate competes under a fixed evaluation budget. The machinery generalizes well beyond astrodynamics.
Search under noise
Multi-objective evolutionary search over constellation design spaces, trading delta-v, released dust mass, and remediation success rate under probabilistic constraints.
Sample-efficient evaluation
Objectives are Monte Carlo estimates on a budget, so runs use adaptive evaluation policies and early stopping to spend samples where they change decisions.
Benchmarks before conclusions
A reproducible conjunction-event bank, built by surrogate sampling of the debris catalog, keeps optimizer comparisons on shared evidence and common scoring.
Fast objective functions
Rust batch evaluators and mixed-fidelity propagation keep single evaluations cheap enough that large searches stay tractable on realistic physics.
Where it applies
Machine learning & AI
The problem structure behind hyperparameter optimization and Hyperband-style schedulers: black-box search over noisy objectives with budgeted, racing-style early-stopped evaluation — plus the benchmark and evaluation-harness discipline ML teams rely on.
Probabilistic modeling
Sigma-point propagation, Gaussian mixtures, and divergence-based validation are shared vocabulary with probabilistic ML and uncertainty quantification.
Engineering design
Architecture trade studies — from sensor placement to constellation geometry — are design-space exploration under constraints: the same optimization loop in a different domain.
Publications
Publications
Conference papers and presentations from the dissertation thread, including the papers where I was second author. If a direct link is missing, the citation search is the quickest way in.
Lead-author work
The papers where I led the framing, writing, and conference presentation.
2026
Stochastic Optimization Techniques for the Design of Just-In-Time Collision Avoidance Constellations
Read Paper
Direct paper
2024
Enhancing Eclipse Transient Orbit Determination Methods with Statistical Atmospheric Models
Read Paper
Direct paper
2024
Dispersion of Targeted Orbital Dust Clouds: Applications to Just-in-time Collision Avoidance
Find Citation
Citation search
Collaborative work
Closely related conference work where I contributed as second author.
2025
Statistical Evaluation of Dust-Based JCA Systems and Policies
Find Citation
Citation search
2023
Orbit Determination via Eclipse Transient Timing: Improved Methods and Intensity Models
Find Citation
Citation search
Skills
Technical Snapshot
A quick scan of the languages, methods, and tools I use most often.
Programming
Python NumPy, SciPy, scikit-learn C++ C Rust MATLAB Fortran Julia
Technical Areas
Kalman Filters UKF, EKF Orbit Determination Evolutionary Algorithms Monte Carlo Simulation Statistical Atmospheres Uncertainty Propagation
Core Tools
Git Linux Unix STK Level 3 Certified Jira Confluence LaTeX
Education
Education
Ph.D., Aerospace Engineering
Aug 2022 – Aug 2026 (Expected)Virginia Tech
Focus: High-fidelity OD, Conjunction Risk Modeling, Statistical Atmospheres. Advisor: Dr. Riley Fitzgerald.
B.S., Aerospace Engineering
Aug 2018 – May 2022Virginia Tech
Certifications
STK Level 3 Certified
Leadership
Leadership & Activities
Rank Captain & Tuba Player
Aug 2018 – Jan 2025Marching Virginians
- Led a section of 24 members, teaching music and marching fundamentals while managing logistics.
- Organized and participated in service projects, including the "Hokies for the Hungry" food drive.