Yu Hou
Software Engineer at Apple
Pittsburgh, Pennsylvania, United States
Summary
👤
Senior🎓
Top SchoolYu Hou is a software engineer with 8 years of experience who blends deep ML and AI expertise with production systems work at Apple’s CoreOS team. With dual master’s degrees from Carnegie Mellon in ECE and Engineering & Technology Innovation Management, Yu has shipped explainability, retraining pipelines, and an LLM-powered developer chatbot during an Apple internship and reduced large-scale metadata corruption risks at AWS. Their background spans speech and language research, robot learning with ViT models, and hands-on ML engineering contributions to a well-known machine learning course repository. Comfortable moving models from research to robust infrastructure, Yu brings a pragmatic focus on observability, reproducibility, and developer-facing tooling informed by diverse industry and academic experiences.
8 years of coding experience
2 years of employment as a software developer
Bachelor of Engineering - BE, Material Science and Engineering, Bachelor of Engineering - BE, Material Science and Engineering at National Taiwan UniversityMaster of Science - MS, Electrical and Computer Engineering, Master of Science - MS, Electrical and Computer Engineering at Carnegie Mellon University
Chinese, English
Github Skills (14)
mask-rcnn10
faster-rcnn10
pytorch10
machine-learning10
jupyter-notebook10
deep-learning10
python10
preprocess9
image-processing9
preprocessing9
load-data9
dataprep9
data-preprocessing9
data-loading9
Programming languages (3)
Github contributions (5)
ga642381/ML2021-Spring
Mar 2021 - Mar 2021
**Official** 李宏毅 (Hung-yi Lee) 機器學習 Machine Learning 2021 Spring
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 7 pushes in 1 day
Contributions summary:Yu's commits primarily focus on updating and refining code related to a Convolutional Neural Network (CNN) model within the context of a machine learning course. They uploaded and modified a Jupyter Notebook (HW03.ipynb) containing the CNN implementation, including dataset loading, model definition, training, and testing phases. The user also added code for downloading the dataset and made adjustments to the code and README file.
leedeep-learningmachine-learningspringhung-yi-lee
dynamic-superb/dynamic-superb
Sep 2023 - Mar 2025
The official repository of Dynamic-SUPERB.
Contributions:23 reviews, 7 PRs, 53 pushes in 1 year 6 months
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