Scientific Concept Discovery: Using Machine Learning to Advance Scientific Research
Description
Our group focuses on the question of how to design a learning framework that promote the generalizability of machine learning models. In this project, you will focus on exploring how neural networks acquire information from the training examples and how they learn to solve various physical problems (e.g., emulation of simple quantum systems). The premise of this project is that by observing how a machine learning model learns to solve the specific task, we can learn about the underlying problem itself. As an example, by analyzing the weights of a trained neural network, you can discover non-trivial symmetries of the modeled physical system, determine the relative importance of features, or identify some non-trivial interplay between underlying physical mechanisms. Your task would be to learn various tools for interpreting deep neural networks. You will test them in practice and you will explore methods that promote model transparency and interpretability.
Awards
- Best Project Achievement
Advisors
Skills Required by the team
- Python
- PyTorch
- Tensorflow
- Bash
- Quantum Mechanics