Avanika Narayan
Doctoral Candidate
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAvanika Narayan is a doctoral candidate at Stanford with nine years of experience building ML systems and production software, currently researching intelligence efficiency and local AI at SAIL. She blends academic rigor with startup and product experience—co-founding an AI startup and contributing as an ML engineer at Predibase—while earlier internships span Sequoia, Palo Alto Networks, and research at Stanford Medicine. An active contributor to Ludwig (a widely used low-code framework for building custom LLMs and neural models), she added HF tokenizer truncation, new NLP datasets, and encoder configuration improvements, highlighting practical expertise in NLP data pipelines and model engineering. Based in Palo Alto, she pairs PhD-level research with hands-on back-end and ML engineering, comfortable moving ideas from prototype to integrated codebases.
9 years of coding experience
2 years of employment as a software developer
Palo Alto High SchoolDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Stanford University
Github Skills (9)
python10
natural-language-processing10
data-science9
deeplearning-ai9
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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