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---
number: 1 <!-- leave as-is, maintainers will adjust -->
title: Investigation of Bandit Optimization for Composite Materials Design
team_leads:
- Jane Doe (University of Invention)
- John Smith (Institute of Discovery)

contributors:
- Larry Lab (University of Invention)
- David Data (University of Science)
- Rachel Research (Institute of Discovery)
github: AC-BO-Hackathon/bayes-bandits
<!-- youtube_video: lIanN0DI9R8 -->
---

This project will investigate the application of bandit optimization to the design of composite materials. We will focus on the optimization of the mechanical properties of the composite materials, such as strength, stiffness, and toughness as a function of the fiber types and matrix materials. We will compare the performance of bandit optimization with the performance of Bayesian optimization using featurization tactics for this highly discrete space.

- Aleksandrs Slivkins (2019), "Introduction to Multi-Armed Bandits", Foundations and Trends in Machine Learning: Vol. 12: No. 1-2, pp 1-286. http://dx.doi.org/10.1561/2200000068
- Dimmery, D., Bakshy, E., & Sekhon, J. (2019). Shrinkage Estimators in Online Experiments. arXiv. https://doi.org/10.48550/ARXIV.1904.12918