Avanika Narayan
Co-founder AI at Rox
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAvanika Narayan is a Co-founder and AI engineer based in Palo Alto with nine years of software and machine learning experience and an active PhD candidacy in Computer Science at Stanford. She co-founded Rox in 2024 while continuing to contribute to production-grade open-source ML tooling — notably adding Hugging Face tokenizer truncation, integrating FEVER, GoEmotions and SST-2 datasets, and tuning text-encoder defaults in Ludwig’s low-code framework — demonstrating a rare blend of low-level NLP engineering and data-pipeline design. Her career includes ML engineering at Predibase and software and research roles across Sequoia Capital, Palo Alto Networks and Stanford Medicine, giving her both startup and academic fluency. She pairs rigorous research instincts with hands-on backend and ML systems work, able to move models from experimental datasets into robust production pipelines.
9 years of coding experience
2 years of employment as a software developer
Stanford University
Github Skills (9)
python10
natural-language-processing10
deep-learning-ai9
data-science9
machine-learning9
pandas9
deep-learning9
pytorch9
llm8
Programming languages (3)
Github contributions (5)
ludwig-ai/ludwig
Oct 2020 - Oct 2021
Low-code framework for building custom LLMs, neural networks, and other AI models
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:18 reviews, 36 commits, 31 PRs in 11 months
Contributions summary:Avanika contributed to the codebase by adding functionality to the HF tokenizer, specifically adding truncation. The user also integrated new datasets (Fever, GoEmotions, SST2) and created supporting mixins for data processing and loading, demonstrating an understanding of data handling within the Ludwig framework. Furthermore, the user modified the default values and parameters within the text encoders, indicating experience with different model architectures and configurations for NLP tasks.
fairness-mlpythonframework-learningdeep-learning-frameworknatural-language-processing
stanford-cs324/winter2023
Jan 2023 - Feb 2023
Contributions:30 commits, 30 pushes in 1 month
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