Alexander Rodríguez
Assistant Professor at University of Michigan
Ann Arbor, Michigan, United States
Summary
👤
Senior🎓
Top SchoolAlexander Rodríguez is an Assistant Professor of Computer Science at the University of Michigan with a decade of experience spanning academic research and industry internships in machine learning and systems. He earned advanced degrees through research positions at Georgia Tech, Virginia Tech, and the University of Oklahoma, and has applied his expertise during internships at Google and Walmart Global Tech. His trajectory blends rigorous PhD-level research with practical engineering exposure, from instrument maintenance in energy to analytics consulting and production-focused ML internships. Based in Ann Arbor, he is building an academic program informed by real-world deployments and cross-disciplinary problem solving, with a track record of moving between research labs and large tech environments. A detail often overlooked: his early international and industry roles suggest a pragmatic, hands-on approach to bridging theory and production.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Georgia Institute of TechnologyDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Virginia TechBachelor of Science - BS, Bachelor of Science - BS at Universidad Nacional de IngenieríaMaster of Science - MS, Master of Science - MS at University of Oklahoma
Github Skills (32)
physics-informed-neural-networks10
graph-algorithms9
python9
graph-visualization9
graph-theory9
complex-networks9
graph-analysis8
deep-learning8
machine-learning8
pytorch8
gpu7
scientific-machine-learning7
neural-network6
ranking6
react5
Programming languages (4)
Github contributions (5)
epidamik/epidamik.github.io
Apr 2021 - Jun 2026
Contributions:120 pushes in 5 years 3 months
AdityaLab/EINNs
Feb 2022 - Dec 2022
[AAAI-23] Epidemiologically-informed Neural Networks
Contributions:4 commits, 6 pushes, 1 branch in 9 months
neural-networkphysics-informed-neural-networksdeep-learningmachine-learningscience-based-deep-learning
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