Azadeh Famili

Azadeh Famili

AI / ML Engineer  ·  Arlington, VA

I build AI systems that operate in high-stakes, human-centered domains — particularly healthcare. My work lives at the intersection of large language models, retrieval-augmented generation, and neural network efficiency, with a constant emphasis on making these systems reliable enough to trust in production.


Before industry, I spent five years at Clemson studying how to make deep networks smaller and faster without sacrificing what makes them useful. Eight publications came out of that work.


Selected Work

LLM Chatbot to support sales team

End-to-end design of a production chatbot. A RAG pipeline over a vector database cut average response time by 90% across the majority of incoming queries.

ML Driven Insurance Eligibility & Case Prioritization

Built an XGBoost model to prioritize case decisions based on likelihood of approval, patient risk factors, and historical claim outcomes — then embedded it inside an automated reverification pipeline using the CMS HETS API to continuously check patient insurance eligibility and benefits ahead of treatment. Eliminated manual re-checks and reduced claim denials caused by lapsed or changed coverage, a recurring cost driver across medical device companies.

Genetic-Based Dynamic Pruning for Deep Neural Networks

Doctoral research on making deep networks dramatically smaller without losing accuracy. Pioneered genetic algorithms for dynamic pruning and quantization of DNNs, achieving significant model acceleration. Published in IEEE ICPR and seven other venues.


Publications & Notes

Background

Education

2018 – 2023 Ph.D., Computer Engineering — Clemson University
2015 – 2018 M.S., Computer Engineering — California State University Fullerton

Current Position

2024 – Machine Learning Scientist — Kerecis → Coloplast, Arlington, VA

Toolkit

Python · PyTorch · TensorFlow · LangChain · XGBoost · AWS (S3, Lambda, SageMaker, CloudFormation) · MLflow · SQL/NoSQL · Node.js · HIPAA/HL7/FHIR · ICD-10

Community

Volunteer with Arlington Neighborhood Village, providing tech support and digital literacy help to older adults in the community — troubleshooting devices, setting up accounts, and making everyday technology feel a little less daunting.

Interests

Agentic AI architectures · LLM evaluation and safety · model compression for edge inference · the intersection of clinical workflows and machine intelligence

LinkedIn  ·  GitHub  ·  Google Scholar