Logan Mathesen
Data Scientist, Statistician
Tempe, Arizona, United States
Summary
👤
Senior🎓
Top SchoolLogan Mathesen is a data scientist and statistician with eight years of experience bridging academic research and high-volume manufacturing analytics, currently driving statistical strategy and automated analysis at Intel Foundry. A PhD candidate in Industrial Engineering at Arizona State University and NSF Graduate Research Fellow, he specializes in black-box optimization, statistical learning, and design of computer experiments, with publications in IEEE and Winter Simulation venues and a best-student-paper finalist distinction. He has translated theory into practice through internships and co-ops at Intel, Toyota, and ViaSat, developing anomaly detection, metrology validation, and optimizer pipelines that doubled fault-finding rates in automotive model testing. At Intel he leads experimental design and process-control systems used globally, authors internal analysis tools and curricula, and shaped statistical culture by delivering thousands of hours of training. Notably, his research produced a novel Conditional Non-Homogeneous Poisson Process model to characterize finite-time search performance, reflecting a strong blend of theoretical rigor and production impact.
8 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Industrial Engineering, Doctor of Philosophy - PhD, Industrial Engineering at Arizona State University
Stackoverflow
11reputation
161reached
0answers
1question
Github Skills (18)
python7
optimization7
deep-learning6
tensorflow6
stochastic6
machine-learning6
large-language-models6
pytorch6
parallel6
matlab6
reinforcement-learning6
data-science6
ray5
hyperparameter-optimization5
llm5
Programming languages (1)
Github contributions (4)
Lmathesen/S-TaLiRO-SOAR-Optimizers
Apr 2019 - Apr 2020
Contributions:10 commits, 7 pushes, 1 branch in 1 year
Lmathesen/SOAR-Algorithmic-Framework
Apr 2018 - Jun 2019
Contributions:10 commits, 4 PRs, 8 pushes in 1 year 1 month
stochastic-optimizationsoaradaptiveoptimizationstochastic
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