Christopher Krapu
AI Application Architect
California, United States
Summary
👤
Senior🎓
Top SchoolChristopher Krapu is an AI Application Architect at NVIDIA with 11 years of experience building agentic systems and production ML solutions. He combines a strong academic foundation—a PhD in Civil and Environmental Engineering and an MS in Statistics from Duke—with applied research roles at Oak Ridge National Laboratory and IIASA to tackle noisy, real-world inference problems. Previously a senior ML engineer at Realtor.com, he has shipped consumer-facing discovery models and also taught and developed a graduate Bayesian data science course. An active contributor to the PyMC probabilistic programming ecosystem, he’s improved tutorials and numerical workflows for spatial point-process modeling (LGCP), reflecting deep hands-on expertise in Bayesian methods. Based in California, he blends systems thinking, probabilistic modeling, and production engineering to move advanced research into scalable products. A less obvious strength is his grounding in experimental lab work and photonics early in his career, which informs a practical, multidisciplinary approach to problem solving.
11 years of coding experience
10 years of employment as a software developer
MS, Statistics, MS, Statistics at Duke UniversityValley City State UniversityBA, Physics, Middle East Studies and Islamic Civilization, BA, Physics, Middle East Studies and Islamic Civilization at Macalester College
English, Spanish, Arabic
Stackoverflow
302reputation
5kreached
1answer
2questions
Github Skills (15)
statistics10
mcmc10
bayesian10
probabilistic-programming10
python10
statistic10
bayesian-inference10
numpy10
arviz9
jupyter-notebook6
ipython6
pymc6
matplotlib6
theano6
jupyter6
Programming languages (7)
Github contributions (5)
pymc-devs/pymc
Dec 2018 - Apr 2021
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:
Data Scientist
Data ScientistContributions:13 reviews, 6 commits, 12 PRs in 2 years 4 months
Contributions summary:Christopher contributed to the PyMC3 repository by adding a new notebook and data for an LGCP (Log-Gaussian Cox Process) tutorial. The contributions involved implementing a model for spatial point patterns, replacing nested loops for data processing, adjusting priors, and refining the tutorial text. Additionally, the user updated an existing conjugate sampling notebook with new text, fixed the inline image format, and modified the cartesian product to allow for more than 2D input arrays.
pythonbayesian-inferencestatistical-inferencemachine-learningprobabilistic-programming
ckrapu/gr4j_theano
Mar 2018 - Jun 2022
Contributions:3 pushes, 1 branch, 2 comments in 4 years 3 months
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