Benjamin Bengfort

Founder And CEO at Rotational Labs, Inc.

Saint Paul, Minnesota, United States
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Summary

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Benjamin Bengfort is a founder and CEO with 13+ years designing distributed systems, machine learning, and NLP solutions that scale to large user bases. He balances academic rigor—PhD research in planetary-scale data systems and teaching applied data science at Georgetown—with hands-on engineering, contributing to open-source projects like Yellowbrick (a NumFOCUS-affiliated ML visualization library) and improving Python tooling such as memory_profiler. His career spans startups and academia, building recommender systems, streaming analytics, and smart IoT applications while emphasizing human-machine intelligence and knowledge sharing. Based in Saint Paul, he leads Rotational Labs and maintains a practical focus on developer experience and diagnostics for ML workflows. Notably, he authored two O’Reilly books and frequently presents at conferences like PyCon and PyData, reflecting a talent for translating complex systems into usable tools and teaching.
code13 years of coding experience
bookNA, NA at United States Naval Academy
bookDoctor of Philosophy (PhD), Doctor of Philosophy (PhD) at University of Maryland
bookUMBC
bookM.S, M.S at North Dakota State University
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Github Skills (30)

data-visualizations10
python10
k-means-clustering10
scikit10
machine-learning10
python-multiprocessing10
data-visualisation10
tsne10
scikit-learn10
memory-profiling10
multi-process10
multiprocessing10
data-visualization10
django9
natural-language-processing9

Programming languages (14)

C#JavaCTeXVueGoCommon LispHTML

Github contributions (5)

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DistrictDataLabs/yellowbrick

May 2016 - Aug 2022

Visual analysis and diagnostic tools to facilitate machine learning model selection.
Role in this project:
userData Scientist
Contributions:17 releases, 78 reviews, 346 commits in 6 years 4 months
Contributions summary:Benjamin's commits show that they were involved in adding examples of visual diagnostics using the Yellowbrick library. The specific examples include a t-SNE projection, a frequency distribution plot, and the application of K-means clustering to project data onto a lower-dimensional space. This suggests that the user was engaged in feature analysis and visualization to better understand the structure of the data and relationships among classes for potential machine learning applications.
diagnostic-toolmachine-learning-modelsmachine-learningvisual-analysismodel-selection
Monitor Memory usage of Python code
Role in this project:
userBack-end Developer
Contributions:9 commits, 1 PR, 22 comments in 8 months
Contributions summary:Benjamin primarily focused on enhancing the `memory_profiler` tool's capabilities, specifically improving the tracking of memory usage in child processes. Their contributions involved modifying the core `memory_profiler.py` script to separately record memory for child processes and integrating these changes into the `mprof` utility. Furthermore, the user merged updates from the upstream repository and improved the documentation. These modifications suggest a focus on expanding the tool's functionalities and overall user experience.
python
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