Cedrick Argueta
Graduate Researcher
New Jersey, United States
Summary
👤
Senior🎓
Top SchoolCedrick Argueta is a Princeton-based graduate researcher and PhD candidate specializing in reinforcement learning, safety, and robotics. He brings nine years of cross-institutional experience across Princeton, Stanford, NASA JPL, and The Aerospace Corporation, with hands-on work in deep RL, adversarial ML, and drone control. As a back-end developer and data scientist on Google’s BIG-bench project, he implemented the convinceme benchmark task and designed a persuasion score using questionnaire integration and JSON data, showcasing strong Python and ML evaluation skills. He excels at translating complex ML ideas into robust, production-ready pipelines and evidence-based insights. Based in New Jersey, he maintains an active research profile (cedrick.ai) that blends academic rigor with open-source contributions.
9 years of coding experience
3 years of employment as a software developer
Bachelor's degree, Computer Science, concentration in Artificial Intelligence, Bachelor's degree, Computer Science, concentration in Artificial Intelligence at Stanford UniversityHigh School Diploma, High School Diploma at Abraham Lincoln Senior High SchoolDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Princeton University
English, Spanish
Github Skills (9)
apache-bench10
machine-learning10
python10
json8
nlp7
natural-language-processing7
scikit-learn6
data-analysis6
scikit6
Programming languages (2)
Github contributions (5)
google/BIG-bench
Sep 2021 - Nov 2021
Beyond the Imitation Game collaborative benchmark for measuring and extrapolating the capabilities of language models
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:9 reviews, 113 commits, 12 comments in 1 month
Contributions summary:Cedrick primarily contributed to the development of the `convinceme` benchmark task within the `big-bench` repository. Their work involved implementing the core logic of the task, including the integration of a questionnaire to assess jury model beliefs and the calculation of a persuasion score. They leveraged and modified existing self-play task templates and incorporated JSON data for statements. The user demonstrated proficiency in Python and the application of machine learning concepts for evaluating the persuasiveness of models.
bertmachine-learningbenchmarkmeasuringbenchmarks
cdrckrgt/cs221-project
Oct 2018 - Dec 2018
Contributions:43 commits, 28 pushes in 1 month
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