The Murphy Lab

Shaping information with machine learning.

Hello and welcome!

We are a research group focused on designing information processing systems around lossy compression. We study the fundamentals of representation learning and information theory, and build algorithms that distill high-dimensional data into reduced descriptions.

If you’re curious about how to design and understand AI systems, consider joining! Please reach out.




Visualization can be a powerful route to building intuition around how complex systems work. Below is a visualization of a randomly initialized neural network that warps two-dimensional space. The input starts as a square and then what you’re seeing is the square after passing through the network. Try varying the number of layers (64 units each) and the activation function!




Number of layers:
Nonlinearity: tanh relu hard_sigmoid elu softsign