Dave Kleinschmidt
Senior Algorithm Research Software Engineer at Beacon Biosignals
New Jersey, United States
Summary
🤩
Rockstar🎓
Top SchoolDave Kleinschmidt is a Senior Algorithm Research Software Engineer with 13 years of experience at the intersection of software, statistics, and cognitive science, currently leading quantitative tooling and algorithm infrastructure at Beacon Biosignals. He builds production-grade platforms for distributed ML training, data provenance/versioning, and large time-series batch processing, and has led efforts to bring Julia runtimes into distributed systems like Ray.io. An active open-source contributor, he has improved core Julia packages such as DataFrames.jl, GLM.jl, and Weave.jl—work that highlights deep attention to statistical model formulas, prediction APIs, and scientific reporting. His background as a cognitive scientist and former assistant professor informs a clear knack for making complex computational ideas legible to diverse audiences. He combines rigorous academic training (PhD in Brain & Cognitive Sciences) with hands-on engineering that results in tools people actually use. Notably, he bridges research and production by translating experimental statistical models into robust, reusable software.
13 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy (PhD), Brain and Cognitive Sciences, Doctor of Philosophy (PhD), Brain and Cognitive Sciences at University of RochesterBachelor of Arts - BA, Mathematics, Summa cum laude, Bachelor of Arts - BA, Mathematics, Summa cum laude at Williams College
Github Skills (27)
formula10
data-science10
dataframes10
statistics10
lm10
dataframe10
regression10
statistical-models10
parsing10
parse10
generalized-linear-models10
formulas10
literate-programming10
glm10
julia10
Programming languages (20)
Github contributions (5)
JuliaStats/GLM.jl
Aug 2014 - Jan 2023
Generalized linear models in Julia
Role in this project:
Data Scientist
Data ScientistContributions:1 release, 28 reviews, 14 commits in 8 years 6 months
Contributions summary:Dave primarily contributed to the `glm.jl` repository by implementing and testing core functionality related to generalized linear models. Their work includes the addition of a prediction function for GLMs and the integration of tests for data frame predictions. Furthermore, the user updated the code to align with API changes in dependencies like StatsBase and StatsModels, while also incorporating the use of macros in tests. These contributions enhance the usability and integration capabilities of the statistical models.
regressionstatisticsdataframesgeneralized-linear-modelsdata-science
JuliaData/DataFrames.jl
Aug 2014 - Oct 2016
In-memory tabular data in Julia
Role in this project:Back-end Developer
Back-end DeveloperContributions:2 reviews, 16 commits, 11 PRs in 2 years 1 month
Contributions summary:Dave primarily contributed to the development of the `dataframes.jl` repository by implementing and refactoring functionalities related to model frames and formulas. Their work included adding methods for regression models, supporting three-way interactions, and handling formula parsing. The user also focused on improving the codebase through cleanup, including removing unnecessary constructors, in-place methods, and streamlining the interaction term ordering to be R-compatible. Their contributions demonstrate a focus on improving formula handling and supporting more complex interactions within the library.
memorydataframesdatatabular-datadata-frame
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