Christopher Rytting
Co-Founder at Laude Institute
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolChristopher Rytting is a co-founder and researcher with a decade of experience at the intersection of machine learning, computational economics, and large-scale scientific software. He holds a PhD in Computer Science and has transitioned academic breakthroughs—parallelized LLM training across 16 Tesla V100 GPUs and sequence transduction for mathematical invariants—into practical tools and startups from San Francisco. His applied research includes implementing and optimizing Sequential Monte Carlo algorithms for the New York Fed’s DSGE models (10x speedups) and contributing production-ready Python and Julia code for dynamic economic modeling. At NVIDIA and university labs he finetuned and scaled 1e11-parameter language models and built synthetic datasets to improve downstream tasks. Combining deep mathematical training with hands-on systems and open-source contributions, he focuses on enabling impact-driven research to pursue new paradigms in AI and economics.
10 years of coding experience
8 years of employment as a software developer
Bachelor of Science (B.S.) Mathematics (Applied and Computational Emphasis), Bachelor of Science (B.S.) Mathematics (Applied and Computational Emphasis) at Brigham Young University - ProvoDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Brigham Young University
English, Spanish
Stackoverflow
191reputation
26kreached
4answers
4questions
Github Skills (15)
bayesian-inference10
julia9
data-structure7
data-structures7
algorithm7
algorithms7
command-line-arguments6
markdown6
google-cloud-dataflow6
jupyter-notebook6
psql6
ipython6
apache-beam6
python6
papermill6
Programming languages (8)
Github contributions (5)
FRBNY-DSGE/DSGE.jl
Jun 2016 - Aug 2016
Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
Role in this project:
Data Scientist
Data ScientistContributions:45 commits in 1 month
Contributions summary:Christopher primarily contributed to the implementation and refinement of Sequential Monte Carlo (SMC) methods within the DSGE.jl repository. Their work included modifying and debugging the `mutation_RWMH.jl` function, `systematic_resampling.jl`, and `smc.jl` files. The user's commits reflect a focus on adapting the SMC algorithm, correcting errors, and optimizing its performance within the context of solving and estimating Dynamic Stochastic General Equilibrium models. This included debugging and implementing core steps in the SMC recursion to get it working within this repository's scope.
solveequilibriumdynamical-systemsbayesian-inferencedsge
BYU-PCCL/partisanbrain
Jul 2021 - May 2022
Repo for everything in the partisan brain project.
Contributions:10 reviews, 15 PRs, 73 pushes in 9 months
braineverything
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