Rowel Atienza
Quezon City, Philippines
Summary
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RockstarRowel Atienza is a Professor, Scientist, and applied AI practitioner with nine years of experience bridging academia, research, and industry consulting. He authored Advanced Deep Learning with TensorFlow 2 and Keras and co-invented state-of-the-art scene text recognition models ViTSTR and PARSeq, the latter deployed on NASA’s Astrobee aboard the ISS and integrated into Intel OpenVINO and PaddlePaddle. His publications span robotics, computer vision, and signal processing, and he was recognized among the Top 2% Scientists Worldwide (2025) based on Stanford/Elsevier metrics. Holding a PhD in Robotics from The Australian National University, he also helped shape the first MEng and PhD AI programs at the University of the Philippines. An active GitHub contributor, he shares hands-on deep learning experiments and best practices—demonstrating practical model development and experiment tracking with tools like TensorFlow and Weights & Biases. Based in Quezon City, he focuses on making AI accessible and production-ready across research and applied deployments.
9 years of coding experience
Github Skills (12)
mask-rcnn10
faster-rcnn10
pytorch10
machine-learning10
deep-learning10
tensorflow10
mlp10
wandb10
image-classification10
autoencoder9
cifar1009
keras9
Programming languages (4)
Github contributions (5)
roatienza/Deep-Learning-Experiments
Jan 2017 - Nov 2022
Notes and experiments to understand deep learning concepts
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 376 commits, 1 PR in 5 years 11 months
Contributions summary:Rowel is primarily contributing to deep learning experiments using TensorFlow. They implemented and refined a variety of machine learning models, including MLPs and CNNs, for image classification tasks. The user also created and evaluated models for object recognition and keyword spotting tasks, indicating a strong focus on applied machine learning. The user's work also includes demonstrating the use of tools such as Weights and Biases (wandb) for experiment tracking and results visualization.
pytorchnotesdeep-learningmachine-learningtensorflow
roatienza/keras
Oct 2017 - May 2018
Contributions:2 PRs, 44 pushes, 14 branches in 7 months
pythondeep-learningrecurrent-neural-networkstheanoneural-networks
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