Kieran A Murphy

Hello and welcome to my site!

I am now an Assistant Professor in Computer Science at NJIT.

My research focuses on designing information processing systems around lossy compression. I use representation learning as a foundation and information theory as a guide, building algorithms that distill high-dimensional data into reduced descriptions.

This design-first perspective bridges machine learning and complex systems: by engineering tools that compress data in principled ways, I aim both to build new interpretability into AI and to uncover organizing principles in fields ranging from biology to physics.

If any of this resonates, please reach out!

A highly compressed view of my research trajectory:
New Jersey Institute of Technology Assistant Professor, 2025-
University of Pennsylvania Postdoc, 2021-2025
Google Research AI Resident, 2019-2021
University of Chicago PhD (Physics), 2013-2019
Lawrence Berkeley National Lab Research assistant, 2012-2013
UC Berkeley BA (Physics, computer science), 2009-2013

I enjoy visualizing minimal examples of complex systems as a route to building intuition around how they 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!