Joseph Carpinelli
Software Engineer at RAI Institute
Cambridge, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolJoseph Carpinelli is a software engineer specializing in computational dynamics, robotics, and research-grade scientific software, with 11 years of experience spanning NASA and research institutes. After multi-year work as an integrated GN&C analyst at NASA Johnson Space Center—where he built a custom 6DOF kinematics simulator with a Python frontend and Julia backend and ran massively parallel HPC experiments—he now develops performant robotics software at The AI Institute (RAI Institute) focusing on ROS 2 controllers, motion planning interfaces, and cross-team tooling. He is an active contributor to the Julia scientific ecosystem, extending ModelingToolkit.jl and Symbolics.jl with control-system and code-generation features that bridge symbolic math and numerical solvers. Comfortable across Python, Julia, and ROS 2, he brings both aerospace-grade verification practices and pragmatic engineering (e.g., private PyPI deployment, controller framework design) to research teams. He also leads a research software reading group, signaling a commitment to reproducible, maintainable research code and community learning. Based in Cambridge, MA, he combines deep domain knowledge in astrodynamics with hands-on implementation of robotics middleware and simulation infrastructure.
11 years of coding experience
6 years of employment as a software developer
Master of Science - MS Aerospace Aeronautical and Astronautical/Space Engineering, Master of Science - MS Aerospace Aeronautical and Astronautical/Space Engineering at University of Maryland
Github Skills (18)
computer-algebra10
symbolic-computation10
code-generation10
modeling10
julia10
matlab9
stan9
control-systems9
scientific-machine-learning8
c178
scim8
computer-algebra-system8
c118
testing7
math7
Programming languages (14)
Github contributions (5)
SciML/ModelingToolkit.jl
Mar 2021 - Jul 2021
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Role in this project:
Back-end Developer
Back-end DeveloperContributions:8 reviews, 13 commits, 6 PRs in 4 months
Contributions summary:Joseph primarily focused on extending the `ModelingToolkit.jl` library, adding and refining features related to control systems and discrete systems within the acausal modeling framework. They implemented the `controls` keyword argument and `controls jacobian` to `AbstractODESystem`, and extended discrete systems to include control inputs. Their contributions involved modifications to core system definitions, including `ODESystem`, `SDESystem`, and `DiscreteSystem`, and included the addition of a `generate_control_jacobian` function. The user also made changes to tests and internal system structures.
computer-algebra-systemjuliamachine-learningscientific-machine-learningdifferential-equations
JuliaSymbolics/Symbolics.jl
Mar 2021 - Mar 2021
Symbolic programming for the next generation of numerical software
Role in this project:Back-end Developer
Back-end DeveloperContributions:13 commits, 2 PRs, 6 comments in 14 days
Contributions summary:Joseph focused on enhancing the `symbolics.jl` repository by implementing and rewriting build functions for various targets, including Stan, MATLAB, and C. Their work involved generating code for differential equation solvers, optimizing the build process, and adding deprecation warnings and docstrings for improved usability. These changes introduced new functionality for building functions compatible with different numerical software platforms, demonstrating expertise in code generation and symbolic computation within the Julia ecosystem. The user also updated tests to accommodate the new syntax.
symbolic-programmingsymbolic-computinghigh-performanceparallel-computingcomputer-algebra-system
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