Phillip Maire

ML AI Instructor at Caltech

Los Angeles, California, United States
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Summary

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Senior
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Top School
Phillip Maire is an AI Data Scientist with a PhD in neuroscience from USC and a decade of experience translating advanced ML research into production healthcare and industry solutions. He built WhACC, a ResNetV2–LightGBM hybrid that achieves human-level video classification for rodent whisker contact and includes a GUI-enabled retraining pipeline to handle data drift—saving hundreds of lab-hours and demonstrating practical, reproducible research. His work spans vision transformers, time-series modeling, and bespoke feature engineering, and he has deployed containerized MLOps pipelines for clinical diagnostics and financial tooling. An instructor at Caltech and a consultant to startups, Phillip blends rigorous academic modeling (GLMs, LSTMs) with hands-on product delivery and novel AI alignment experiments, such as Persona Vector Immunization. He is based in Los Angeles and actively brings research-grade methods to real-world ML problems.
code9 years of coding experience
job8 years of employment as a software developer
bookDoctor of Philosophy (Ph.D.) Neuroscience, Doctor of Philosophy (Ph.D.) Neuroscience at University of Southern California
bookBachelor of Science (BS) Psychology, Bachelor of Science (BS) Psychology at University of Louisville
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Stackoverflow

Stats
372reputation
37kreached
12answers
6questions
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Github Skills (79)

python10
deep-learning10
machine-learning10
r9
qt9
gradient-boosting9
gradient9
statistics9
lightgbm9
neural-network9
parallel9
gtk9
matplotlib9
data-mining9
data-science9

Programming languages (7)

C++NimGoJupyter NotebookMATLABPythonCuda

Github contributions (5)

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hireslab/HLab_Whiskers

Aug 2017 - Dec 2020

Whisker analysis for Hires lab
Contributions:14 commits, 2 PRs, 16 pushes in 3 years 4 months
whiskerhireslab
hireslab/whacc

Apr 2021 - Sep 2022

semi-automatic and customizable pipeline for classifying whiskers contacting objects. WhACC uses a ResNetV2-Light GBM hybrid model to predict touch times in high-speed video. Included is a tracker to extract images from video, a GUI to label extracted data, and a retrain interface to customize the light GBM model head to your data.
Contributions:230 commits, 4 PRs, 148 pushes in 1 year 5 months
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