Chris Rackauckas
Director Of Modeling And Simulation
Cambridge, Massachusetts, United States
Summary
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Rockstar🎓
Top SchoolChris Rackauckas is a Director of Modeling and Simulation and applied mathematics instructor focused on Scientific Machine Learning, blending differential-equation-based domain models with modern machine learning to deliver fast, explainable simulators. He is the lead developer of the DifferentialEquations.jl ecosystem and Pumas.jl, tooling that has driven dramatic accelerations in industry and research—from NASA launch simulations to Moderna and ongoing clinical trials predicting personalized drug dosing. With 12+ years of experience and a PhD in mathematics, he translates advanced numerical algorithms and automatic differentiation into production-quality Julia software used in pharmacometrics, climate, and HVAC modeling. Beyond coding, he shapes the SciML community through widely-used packages like ModelingToolkit.jl and DiffEqFlux.jl and educational materials for MIT courses, combining deep theory with pragmatic impact on personalized medicine.
12 years of coding experience
7 years of employment as a software developer
Research Experience for Undergraduates, High Performance Computing and Statistics, Research Experience for Undergraduates, High Performance Computing and Statistics at University of Maryland, Baltimore CountyBachelor's degree, Mathematics; Minors in Physics, Computer Science, and Economics, Bachelor's degree, Mathematics; Minors in Physics, Computer Science, and Economics at Oberlin CollegeMaster’s Degree, Mathematics, Master’s Degree, Mathematics at University of California, Irvine
Stackoverflow
18,982reputation
528kreached
335answers
53questions
julia
top-1%
package
top-5%
parallel-processing
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struct
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oop
top-5%
python
top-5%
Github Skills (63)
markdown10
algorithms10
symbolic-computation10
ziggy10
visualization10
symbolic-computing10
frank10
website-development10
ode10
pde10
testing10
solver10
machine-learning10
plot10
mathematical-optimization10
Programming languages (19)
Github contributions (5)
SciML/SciMLBook
Aug 2019 - Jan 2023
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
Role in this project:
Technical Writer & Documentation Specialist
Technical Writer & Documentation SpecialistContributions:1 release, 2 reviews, 276 commits in 3 years 5 months
Contributions summary:Chris primarily contributed to the documentation within the `sciml/scimlbook` repository, focusing on lecture notes related to optimization, dynamical systems, and automatic differentiation. Their contributions involved creating and updating HTML files that presented course content, including code examples, mathematical formulas, and performance analysis. The changes suggest a focus on explaining technical concepts related to scientific machine learning.
scientific-simulatorsscientific-machine-learningperformance-engineeringddesciml
SciML/Optimization.jl
May 2020 - Jan 2023
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
Role in this project:Back-end Developer
Back-end DeveloperContributions:159 reviews, 379 commits, 307 PRs in 2 years 9 months
Contributions summary:Chris's commits primarily focus on updating and refactoring the `src/solve.jl` file, which appears to be central to the optimization process. They modified the structure of optimization outputs, added support for constraints, and improved the handling of various optimization algorithms. Furthermore, the changes involve integrating and adapting different optimization packages and methods, including adjustments to handle callbacks and data inputs. This indicates a focus on enhancing the core functionality and flexibility of the optimization library.
optimization-methodsnonlinear-optimizationautomatic-differentiationscientific-machine-learningalgorithmic-differentiation
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