Holger Teichgraeber
Senior Software Engineer, Operations Research
San Francisco Bay Area United States
Summary
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Senior🎓
Top SchoolHolger Teichgraeber is a Senior Software Engineer specializing in operations research and machine learning, with nine years of experience applying optimization to real-world systems across logistics, aviation, and energy. He combines a Stanford Ph.D. background in energy systems with hands-on production experience building and deploying ML and optimization algorithms at companies like Convoy, Archer, and DoorDash. Holger has led data science teams and consulted on energy storage optimization, model predictive control, and controller design, translating research-grade methods into cloud-based products and interactive visualization tools. His academic work produced open-source time-series and capacity-expansion packages now used in industry and research, reflecting a rare blend of rigorous research, product intuition, and deployment skill. Based in the Bay Area, he excels at bridging long-horizon stochastic planning with fast-moving operational needs, often turning complex energy and routing problems into pragmatic, scalable solutions.
9 years of coding experience
10 years of employment as a software developer
Stanford Ignite Certificate Program in Innovation and Entrepeneurship, Stanford Ignite Certificate Program in Innovation and Entrepeneurship at Stanford University Graduate School of BusinessExchange Student Research Scholar Chemical Engineering Mechanical Engineering, Exchange Student Research Scholar Chemical Engineering Mechanical Engineering at University of California, DavisBachelor of Science (B.S.) Mechanical Engineering, Bachelor of Science (B.S.) Mechanical Engineering at RWTH Aachen UniversityDoctor of Philosophy (Ph.D.) Energy Resources Engineering, Doctor of Philosophy (Ph.D.) Energy Resources Engineering at Stanford University
Github Skills (82)
energy10
clustering10
photovoltaic10
hierarchical-clustering10
ocean10
science10
julia10
unsupervised-learning10
biodiversity10
energy-data10
similarity10
energy-efficiency9
risk-assessment9
programming-language9
ecology9
Programming languages (5)
Github contributions (5)
holgerteichgraeber/TimeSeriesClustering.jl
Jul 2017 - Feb 2020
Julia implementation of unsupervised learning methods for time series datasets. It provides functionality for clustering and aggregating, detecting motifs, and quantifying similarity between time series datasets.
Contributions:3 releases, 335 commits, 63 PRs in 2 years 7 months
unsupervised-learningmotifsunsupervisedmachine-learningsimilarity
YoungFaithful/CapacityExpansion.jl
Apr 2019 - Sep 2020
Capacity Expansion Problem Formulation for Julia
Contributions:1 review, 39 commits, 24 PRs in 1 year 6 months
factorizationexpansioncapacityproblemjulia
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