Kevin Villela
Staff Research Engineer at Google DeepMind
Los Angeles, California, United States
Summary
👤
Senior🎓
Top SchoolKevin Villela is a Staff Research Engineer at DeepMind with nine years of experience building production-grade software and research systems at Google and DeepMind. He combines a strong academic foundation in computer science and mathematics from Purdue with hands-on expertise in quantum computing algorithms—contributing to Cirq by implementing and optimizing Quantum Volume routines, device-aware compilation, and readout error correction. Kevin mentors early-career researchers and offers career guidance in AI/ML while deliberately avoiding recruiter outreach or referral requests. Based in Los Angeles, he blends applied research and backend engineering to move cutting-edge algorithms toward deployable systems, and his background as a startup co-founder and mobile developer gives him practical product intuition beyond pure research.
9 years of coding experience
9 years of employment as a software developer
Master's degree Applied Computer Science, Master's degree Applied Computer Science at Purdue University Fort Wayne
Portuguese
Stackoverflow
106reputation
24kreached
2answers
2questions
Github Skills (22)
circuit10
algorithms10
quantum-algorithms10
python10
cirq10
quantum-computing10
simulation9
simulations9
sdk9
networkx8
raspberry-pi6
gamekit6
audio6
game-center6
subprocess6
Programming languages (7)
Github contributions (5)
quantumlib/Cirq
Sep 2019 - Sep 2020
An open-source Python framework for creating, editing, and invoking Noisy Intermediate-Scale Quantum (NISQ) circuits.
Role in this project:
Back-end Developer / Algorithm Engineer
Back-end Developer / Algorithm EngineerContributions:12 commits, 20 PRs, 166 comments in 1 year
Contributions summary:Kevin primarily focused on implementing and refining the Quantum Volume algorithm within the Cirq framework. Their contributions include developing the "Heavy Output Generation" component, which involved modifying the quantum_volume.py and quantum_volume_test.py files. They also added functionality to compute HOG probabilities and incorporated device-specific compilation steps, including deconstruction, routing, and optimization. Further, they added the ability to specify the device graph for compilation and introduced readout error correction mechanisms.
pythonquantum-circuitsquantum-computingcircuitsquantum-algorithms
KevinVillela/Cirq
Sep 2019 - Dec 2019
A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
Contributions:125 pushes, 10 branches in 3 months
pythonquantum-computingcircuitsscaleinvoking
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