Michael Ryan
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMichael Ryan is an incoming PhD student at Stanford’s SALT Lab focused on building AI that better collaborates with humans and on methods that leverage human feedback to improve models. With a decade of engineering experience and prior MS and BS degrees in Computer Science from Stanford and Georgia Tech, he blends rigorous research training with hands-on systems and security development across companies like Snowflake, Microsoft, and Uber. His practical contributions include static analysis tooling for Windows, cloud-native refactors, and simulation-driven testing, and he has added teleprompting and instruction-optimization components to DSPy—a framework for programming language models rather than prompting them. Based in Palo Alto, he pairs academic curiosity with production-grade implementation skills and a track record of turning research ideas into working code.
10 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Computer Science, 3.96, Bachelor of Science - BS, Computer Science, 3.96 at Georgia Institute of TechnologyMaster of Science - MS, Computer Science, 4.112, Master of Science - MS, Computer Science, 4.112 at Stanford University
Github Skills (12)
machine-learning10
nlp10
dnspy10
large-language-models10
python10
natural-language-processing10
optimization9
optimizers9
optimisation9
bayesian9
algorithm9
openai8
Programming languages (7)
Github contributions (5)
stanfordnlp/dspy
Nov 2023 - May 2026
DSPy: The framework for programming—not prompting—language models
Role in this project:
ML Engineer
ML EngineerContributions:14 reviews, 22 PRs, 35 pushes in 2 years 6 months
Contributions summary:Michael contributed significantly to the `dspy` repository by adding and modifying the `SignatureOptimizer` module within the `dspy/teleprompt` directory. These changes involve modifications to instruction optimization, including new classes and methods for generating improved instructions for large language models. The commits demonstrate the development of a teleprompting framework with Bayesian and other optimizer implementations.
language-model
XenonMolecule/QRty
Sep 2020 - Sep 2020
A webapp where you can share text and images across devices
Contributions:61 commits, 12 PRs, 50 pushes in 19 days
reactdjangoshare-textwebappshare
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