Alec Gunny
Machine Learning Engineer at Blue Rose Research
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlec Gunny is a Machine Learning Engineer with a decade of experience building GPU-accelerated and production-ready deep learning systems, from hospital critical-care time series to gravitational wave streaming inference. He has led applied ML and solutions work at NVIDIA—contributing to high-profile projects like NVTabular where he added a TensorFlow dataloader and production examples—and conducted scalable inference research at MIT. Alec combines hands-on model and pipeline engineering with a knack for performance tuning on large tabular and time-series datasets, regularly turning research prototypes into robust tooling. Based in San Francisco and self-described as a “full time kook,” he pairs unconventional curiosity with rigorous engineering, backed by an Engineering Physics degree from UC Berkeley.
10 years of coding experience
9 years of employment as a software developer
Bachelor of Engineering (B.E.), Engineering Physics, Bachelor of Engineering (B.E.), Engineering Physics at UC Berkeley
Spanish
Github Skills (13)
deeplearning-ai10
machine-learning10
deep-learning10
tensorflow10
python10
preprocess9
feature-engineering9
preprocessing9
recommendation-system9
docker8
jupyter-notebook8
dockers8
gpu8
Programming languages (5)
Github contributions (5)
NVIDIA-Merlin/NVTabular
May 2020 - Nov 2020
NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.
Role in this project:
ML Engineer
ML EngineerContributions:15 reviews, 27 commits, 18 PRs in 6 months
Contributions summary:Alec was heavily involved in integrating TensorFlow into the NVTabular library, specifically focusing on creating a TensorFlow dataloader. Their work included adding and documenting the dataloader, developing example notebooks using the Rossmann store sales dataset and Criteo dataset, and ensuring the examples and unit tests functioned correctly. They also addressed issues related to memory initialization within the TensorFlow dataloader and contributed to documentation updates.
tsneengineeringtensorflowpreprocessingnvidia
alecgunny/ml4gw
Jul 2022 - Jan 2024
Torch utilities for doing machine learning in gravitational wave physics
Contributions:91 pushes, 54 branches in 1 year 6 months
physicsgravitationaldeep-learningtorchmachine-learning
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