Jose Tapia
Engineer at T-Mobile
Bothell, Washington, United States
Summary
👤
Senior🎓
Top SchoolJose Tapia is a systems architecture engineer with nine years of hands-on experience driving 5G proof-of-concept work and RAN slicing use cases at T-Mobile. He blends academic rigor—pursuing an MSEE after a BSEE from the University of Washington Bothell—with practical skills in Unity, ARCore, C#, XCAL/XCAP, and RAN testing tools like QXDM/QCAT. Comfortable moving between low-level radio network testing and app-level AR/XR development, he’s known for tackling ambiguous problems during the pandemic-driven transition from school to industry. An active IEEE member and former tutor/teaching assistant, he brings strong communication and mentoring experience alongside a mathematician’s analytical mindset (ICPC World Finals participant). Based in Bothell, WA, he combines research-oriented thinking with production-focused execution to advance next-generation wireless capabilities.
9 years of coding experience
22 years of employment as a software developer
Bachelor's degree, Electrical and Electronics Engineering, Sophmore, Bachelor's degree, Electrical and Electronics Engineering, Sophmore at Western Washington UniversityAssociate of Science - AS, Electrical Engineer track, Associate of Science - AS, Electrical Engineer track at Cascadia Community CollegeMaster of Science - MS, Electrical and Electronics Engineering, Master of Science - MS, Electrical and Electronics Engineering at University of Washington Bothell
Spanish
Github Skills (14)
serve10
metaheuristics9
building-blocks9
simulated-annealing9
evaluator8
hyper8
optimization8
python7
heuristics5
knapsack-problem3
artificial-neural-networks3
css1
javascript1
deployer1
Programming languages (5)
Github contributions (5)
jose-tapia/1729372MC
Aug 2017 - Nov 2017
Contributions:83 pushes, 1 branch in 3 months
jose-tapia/nn-hh-umhs-cec22
May 2022 - Nov 2022
This repository contains the results from A Primary Study on Hyper-Heuristics Powered by Artificial Neural Networks for Customising Population-based Metaheuristics in Continuous Optimisation Problems presented on CEC 2022. The following version of Customising optimisation metaheuristics via hyper-heuristic search (CUSTOMHyS) was used for the proposed NN-HH model experiments.
Contributions:1 PR, 20 pushes, 3 branches in 5 months
heuristicheuristicscec-2022followinghyper-heuristic
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