Qasim Khan
Learning Facilitator
Austin, Texas, United States
Summary
👤
Senior🎓
Top SchoolQasim Khan is a Junior AI/ML Engineer based in Austin, Texas, pursuing an MS in Artificial Intelligence at UT Austin with nine years of practical experience across education, retail, and government roles. He combines a solid foundation in machine learning, data engineering, and distributed systems with hands-on PyTorch work—contributing fixes and improvements to the widely used pytorch/tutorials repository, including adversarial attack and attention examples. At the Texas Department of Transportation he has transitioned from an AI intern to a junior engineer, applying research-driven techniques to real-world infrastructure problems. Qasim has taught programming at the university level, which sharpens his ability to communicate complex ideas clearly to diverse audiences. He also brings interdisciplinary insight from a prior psychology degree, helping him think about human-centered aspects of AI. Curious and methodical, he focuses on bridging knowledge graphs, GraphRAG, and scalable ML pipelines to move prototypes toward production.
8 years of coding experience
Master of Science - MS, Artificial Intelligence, Master of Science - MS, Artificial Intelligence at The University of Texas at AustinBachelor's degree, Computer Science, Bachelor's degree, Computer Science at George Mason University - Volgenau School of EngineeringBachelor of Science - BS, PSYCHOLOGY, Bachelor of Science - BS, PSYCHOLOGY at Virginia Commonwealth University
Github Skills (10)
pytorch10
machine-learning10
adversarial-attacks10
python10
seq2seq9
basics9
learn-ruby-on-rails9
user-manual9
nlp8
computer-vision8
Programming languages (8)
Github contributions (5)
pytorch/tutorials
Jun 2023 - Jun 2023
PyTorch tutorials.
Role in this project:
ML Engineer
ML EngineerContributions:14 PRs, 19 comments, 1 issue in 8 days
Contributions summary:Qasim primarily contributed to the PyTorch tutorials repository by fixing and improving various tutorial examples. They addressed issues related to attention mechanisms in sequence-to-sequence translation, updated dependencies, and fixed a kernel crash in a Captum tutorial. Additionally, they improved the FGSM tutorial by fixing inconsistencies, changing the model architecture, adding normalization transforms, and denormalization code, showcasing their knowledge of adversarial attacks.
deep-learningpytorchpytorch-tutorials
dll-ncai/AI-ForestWatch
Nov 2021 - Feb 2022
Contributions:164 commits, 2 PRs, 151 pushes in 3 months
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