Abduallah Mohamed
Applied Research Scientist at Meta
Greater Seattle Area United States
Summary
👤
Senior🎓
Top SchoolAbduallah Mohamed is an applied research scientist with nine years of hands-on experience building motion-aware computer vision and sensor-fusion systems, currently leading SLAM and motion-understanding research at Meta Reality Labs. He combines a strong academic foundation (MS/PhD work at UT Austin) on trajectory prediction with industrial expertise across AR/VR, autonomous driving, and surgical robotics from roles at Meta, Apple, Intuitive Surgical, Valeo, and AevaLabs. His work blends deep learning with multi-sensor fusion to produce edge-optimized, real-time perception pipelines—several contributions have led to patents and deployed prototypes. Known for taking projects from notebook prototypes to scalable systems, he balances research novelty with product-minded engineering for large-scale media and device platforms. A less obvious strength is his entrepreneurial streak—co-founding an AR cloud startup and leading algorithm teams early in his career—showing he can pivot between research, productization, and team leadership.
9 years of coding experience
6 years of employment as a software developer
Master of Science - MSc, Transportation Engineering, Master of Science - MSc, Transportation Engineering at The University of Texas at AustinBachelor of Science - BSc, Electrical, Electronics and Communications Engineering, Bachelor of Science - BSc, Electrical, Electronics and Communications Engineering at Helwan University Cairo
English, Arabic
Stackoverflow
1reputation
120reached
0answers
1question
Github Skills (105)
notebook10
python10
ipython10
prediction10
multiagent-systems10
traffic10
efficientnet10
resnet10
jupyter10
pytorch10
temporal10
trajectory-generation10
computer-vision9
memory-efficient9
deep-learning9
Programming languages (5)
Github contributions (5)
abduallahmohamed/MCRM
Aug 2018 - Aug 2018
Contributions:29 commits, 27 pushes, 1 branch in 18 days
abduallahmohamed/Social-Implicit
Nov 2021 - Oct 2022
Code for: "Social-Implicit: Rethinking Trajectory Prediction Evaluation and The Effectiveness of Implicit Maximum Likelihood Estimation" Accepted @ ECCV2022
Contributions:12 commits, 1 PR, 6 pushes in 11 months
gcnn-pedestriansgraph-convolutional-networksgraph-neural-networkshuman-trajectory-predictionmemory-efficient
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