Daniel Whitenack
CEO at Prediction Guard
Lafayette, Indiana, United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Whitenack is a data scientist-turned-founder with 13 years of experience building practical, production-ready AI systems and developer tooling. As CEO of Prediction Guard he focuses on privacy-first, trustworthy AI, while also co-hosting the popular Practical AI podcast and contributing to notable open-source projects like the Go kernel for Jupyter (gophernotes) and Pachyderm. His background spans applied ML, data engineering, and backend systems—work that ranges from integrating go-ethereum components to teaching data science and evangelizing reproducible ML pipelines. He holds a PhD in mathematical/computational physics and brings a rare combination of academic rigor, hands-on engineering, and entrepreneurial experience that includes commercial real estate and product operations. An under-the-radar strength is his knack for translating complex research into usable tools and clear tutorials that help communities adopt AI responsibly.
13 years of coding experience
13 years of employment as a software developer
Bachelor of Science (B.S.) Engineering Physics, Bachelor of Science (B.S.) Engineering Physics at Colorado School of MinesDoctor of Philosophy (Ph.D.) Mathematical/Computational Physics, Doctor of Philosophy (Ph.D.) Mathematical/Computational Physics at Purdue University
Github Skills (27)
data-visualizations10
python10
jupyter10
pandas10
statistics10
machine-learning10
data-visualisation10
statistic10
csv-parser10
linear-regression10
kernel10
go10
ethereum10
blockchain10
golang10
Programming languages (10)
Github contributions (5)
gopherdata/gophernotes
Jan 2016 - Mar 2018
The Go kernel for Jupyter notebooks and nteract.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 releases, 205 commits, 53 PRs in 2 years 2 months
Contributions summary:Daniel primarily worked on implementing and modifying the core functionalities of the Go kernel for Jupyter notebooks. Their commits focused on refactoring the code to utilize a "gore" session for execution, improving the overall repl functionality and connecting it to the jupyter kernel. The changes involved restructuring the kernel file directory, debugging, and integrating the new interpreter.
golangkernelnteractzeromqdata-science
pachyderm/pachyderm
Feb 2016 - Jun 2020
Data-Centric Pipelines and Data Versioning
Role in this project:Data Scientist
Data ScientistContributions:702 commits, 195 PRs, 233 pushes in 4 years 4 months
Contributions summary:Daniel updated an example Jupyter notebook to read data from the Pachyderm File System (PFS) and perform exploratory data analysis using the `iris.csv` dataset. They modified the notebook to incorporate the `iris.csv` dataset, and included code to visualize the data. The contributions focused on demonstrating how to access and utilize versioned data within a Jupyter Notebook.
containersanalyticsdata-analysispachydermpipeline
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