Avinash Madasu
AI Research Scientist at Intel Corporation
California, United States
Summary
👤
Senior🎓
Top SchoolAvinash Madasu is an AI Research Scientist based in California with nine years of experience building scalable NLP, CV and multimodal systems for enterprise and consumer applications. Currently at Intel, he architects agentic AI solutions optimized for Intel Xeon, Arc and Battlemage GPUs—specializing in long-context compression, KV-cache optimization and retrieval-oriented execution for low-latency on-device inference. His research, with 500+ citations and publications at ICLR, CVPR, NeurIPS, EMNLP and others (including best-paper awards), blends deep academic rigor with production delivery—from concept through launch. Previously he improved Samsung’s Bixby assistant (GDPR-compliant NER, intent failure detection) and contributes to open-source ML resources (e.g., updating the popular deep-learning-book repo for PyTorch). An EB-1A “Einstein Visa” awardee, he also serves as a frequent reviewer and award-winning reviewer across top AI conferences, reflecting strong community influence beyond his engineering work.
9 years of coding experience
6 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at National Institute of Technology TiruchirappalliMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of North Carolina at Chapel Hill
English, Telugu, Hindi, Tamil
Github Skills (6)
neural-network10
pytorch10
machine-learning10
jupyter-notebook10
python10
dropout9
Programming languages (5)
Github contributions (5)
rasbt/deep-learning-book
Jan 2019 - Jan 2019
Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
Role in this project:
ML Engineer
ML EngineerContributions:19 commits, 3 PRs, 13 comments in 3 days
Contributions summary:Avinash focused on updating and adapting existing code examples within the repository. They updated loss functions, model evaluation modes, and made changes to support PyTorch 1.0.0. These updates involved modifying Jupyter notebooks showcasing multi-layer perceptron implementations, incorporating dropout layers, and addressing code compatibility.
pythondata-sciencedeep-learningneural-networksmachine-learning
avinashsai/Cross-domain-sentiment-analysis
Mar 2018 - May 2018
Contributions:41 commits, 38 pushes, 1 branch in 1 month
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