Richard Phillips
PhD Student at Cornell University
City of Ithaca, New York, United States
Summary
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Senior🎓
Top SchoolRichard Phillips is a PhD student at Cornell University with a decade of experience building machine learning systems that foreground transparency, fairness, and the downstream effects of algorithmic decisions on shifting populations. His research blends data science for public health and group decision-making with practical engineering—authoring active learning pipelines, optimizing hundreds of supervised models, and implementing data tools in Python and Rust. He has contributed to high-profile open-source benchmarks for real-world distribution shifts (integrating the SQF dataset into the WILDS benchmark) and brings hands-on experience with domain-driven tooling like RDKit and Django. Prior work in computational chemistry and large-scale DFT computations shows a rare combination of lab, HPC, and software skills that inform his interpretability work. Driven by ethical AI, he develops methods to quantify bias in model confidence and to make individual ML recommendations more interpretable for stakeholders.
10 years of coding experience
4 years of employment as a software developer
Bachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at Haverford College
Github Skills (10)
data-preprocessing10
pandas10
pytorch10
machine-learning10
python10
data-integration10
datasets10
data-set10
evaluation9
eval9
Programming languages (4)
Github contributions (5)
p-lambda/wilds
Jan 2021 - Feb 2021
A machine learning benchmark of in-the-wild distribution shifts, with data loaders, evaluators, and default models.
Role in this project:
ML Engineer
ML EngineerContributions:14 commits, 1 PR, 5 pushes in 25 days
Contributions summary:Richard integrated a new dataset pipeline for the SQF (Stop, Question, and Frisk) dataset, including data loading and preprocessing. They modified existing code, including metrics and configurations, to adapt the existing codebase to the new dataset. The user also removed and adjusted the precision at recall metric and other configurations to tune the machine learning models. Further commits included refactoring code and removing debug print statements.
dataloadermachine-learning
rlphilli/DeltaBondLength-Tool
Nov 2015 - Sep 2022
Contributions:4 pushes, 1 branch in 6 years 11 months
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