Humam Alwassel
Artificial Intelligence Advisor at TAHAKOM
Saudi Arabia
Summary
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Rockstar🎓
Top SchoolHumam Alwassel is an Artificial Intelligence Advisor with a PhD in Computer Vision and over 11 years of experience spanning research and industry roles at DeepMind, Meta, AWS, and Amazon. He combines deep academic rigor with product-minded applied research, having led self-supervised audio-video representation work at Meta and contributed evaluation tooling to the widely used ActivityNet benchmark. As a Principal ML Scientist and now advisor in Saudi Arabia, he bridges cutting-edge vision research and deployable ML systems, with a track record of improving evaluation pipelines and parallelizing large-scale metrics. Humam’s background in CS and mathematics from Cornell and KAUST underpins a comfort moving between novel algorithms and production constraints, and he self-describes as a “deep learning hacker” who enjoys making robust, reproducible research usable in practice.
11 years of coding experience
3 years of employment as a software developer
Bachelor's degree, Double Major in Computer Science and Mathematics, Bachelor's degree, Double Major in Computer Science and Mathematics at Cornell UniversityDoctor of Philosophy - PhD, Computer Science, Specialized in Computer Vision, Doctor of Philosophy - PhD, Computer Science, Specialized in Computer Vision at King Abdullah University of Science and Technology
English, Arabic
Github Skills (6)
pandas10
python10
evaluation10
metric10
algorithm9
algorithms9
Programming languages (3)
Github contributions (5)
activitynet/ActivityNet
Apr 2017 - Jan 2021
This repository is intended to host tools and demos for ActivityNet
Role in this project:
Data Scientist
Data ScientistContributions:13 commits, 12 pushes, 20 comments in 3 years 9 months
Contributions summary:Humam primarily contributed to the evaluation framework of the ActivityNet repository. They updated the code for detection evaluation, changing the metric to average-mAP and parallelizing the evaluation process using Joblib. Furthermore, they added and improved proposal evaluation scripts, fixing an issue where the evaluation code failed for missing class labels. Their work also involved adding a notebook for generating random proposals and visualizing the evaluation metric.
hostwebapplicationactivitynetweb-applicationactivitypub
HumamAlwassel/DETAD
Jun 2018 - Nov 2021
Diagnosing Error in Temporal Action Detectors (ECCV 2018)
Contributions:1 review, 14 commits, 3 PRs in 3 years 5 months
pytorchdeep-learningeccveccv-2018temporal
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