Michael J. Ryan

Michael J. Ryan

Masters student in NLP

Stanford University

Hi, I’m Michael Ryan and I’m a Master’s student studying Artificial Intelligence at Stanford University. I’m fortunate to be doing NLP research as a member of Dr. Diyi Yang’s SALT Lab! This Spring I am the Head Course Assistant for CS221: Artificial Intelligence: Principles and Techniques with Dr. Nima Anari, Dr. Moses Charikar, and Dr. Sanmi Koyejo.

My research interests include LLM personalization for various cultures, languages, and individuals. Right now I am looking at some of the effects of preference tuning on LLMs. I am also exploring prompt optimization strategies via DSPy. Previously I was an undergraduate researcher in Dr. Wei Xu’s NLP X Lab at Georgia Tech.

Have a look at my CV, or if you’re in a hurry, check out my resume!

  • LLM Personalization
  • Prompt Optimization
  • Simplification and Readability
  • Fairness in NLP
  • MS in Computer Science, 2025

    Stanford University

  • BSc in Computer Science (Intelligence & Systems/Architecture), 2023

    Georgia Institute of Technology


Unintended Impacts of LLM Alignment on Global Representation
Revisiting non-English Text Simplification: A Unified Multilingual Benchmark
Having Beer after Prayer? Measuring Cultural Bias in Large Language Models
Towards Massively Multi-domain Multilingual Readability Assessment


Work Experience

Microsoft Corporation
Software Engineer Intern
May 2022 – August 2022 Seattle, WA
  • Designed and programmed static analysis tool in C++ for identifying security vulnerabilities throughout Windows OS.
Microsoft Corporation
Software Engineer Intern
May 2021 – August 2021 Virtual
  • Refactored existing codebase from .NET Framework to .NET Core.
  • Ported server-specific architecture to serverless functional units using Azure Durable Functions.
Uber Inc.
Software Engineer Intern
May 2020 – August 2020 Virtual
  • Implemented end-to-end testing in GoLang for bike, scooter, and moped rentals by building a simulated 3rd party CRUD API.


Knock Knock: Neural Joke Generation and Classification
A joke generator built on GPT-2 and joke classifier built on BERT for CS4650 Natural Language Understanding.
Knock Knock: Neural Joke Generation and Classification
Sign Assist
Sign Assist is a digital American Sign Language (ASL) interpreter. Using a Kinect camera and computer vision Sign Assist tracks user movements and patterns to detect different signs. Sign Assist can output these signs as both text and speech.
Sign Assist


Honors Program Distinction in Research
Awarded for completing Honors Level Coursework and Approved Research as an undergraduate at Georgia Tech
See certificate
Outstanding Undergraduate TA for Interactive Computing
Awarded for contributions to teaching excellence at Georgia Tech School of Interactive Computing