Contact: rdlvcs@rit.edu
PI: Richard D. Lange
Starting in Fall 2023, I am an assistant professor in the Department of Computer Science at RIT.
I want to understand how the brain works and use that understanding to create more capable machine learning and AI systems. My interests have bounced back and forth over the years between computer science, neuroscience, AI, machine learning, and philosophy of mind.
Here's a bit about how I got where I am now:
2013: BA in Computer Science and Engineering at Dartmouth College. Near the end of undergrad I fell in love with the idea of building intelligent systems inspired by how the brain works.
2014-2015: Started a PhD in Computer Science at the University of Rochester. Eventually realized that building "brain inspired" AI means we first need to understand brains.
2015-2020: PhD in Brain and Cognitive Science at the University of Rochester in the lab of Ralf Haefner. Studied visual perception both in theory and in humans and monkeys through the lens of Bayesian inference.
2020-2023: Postdoc at the University of Pennsylvania in Konrad Kording's lab. Studied deep neural networks and structure in the kinds of internal representations they develop.
2023 Started as Assistant Professor of Computer Science at RIT
New and ongoing research areas – get in touch if you're interested in any of the following!
Improved measures of representational similarity for neural data
Biologically-plausible backpropagation
Self-supervised representation learning from statistical calibration
Causal modeling and counterfactual learning
Currently looking for PhD students to start in Fall 2024!
Finding the right PhD program means not just finding a research area you're interested in, but also finding a program and an advisor you'll get along with. If you're interested, please apply to the RIT GCCIS PhD program or the Cognitive Science PhD program and mention my name in your application.
As is the case with many PhD programs at other institutions, funding for PhD students is guaranteed for the first year and contingent on external grant funding – either to the lab or to the student directly – going forward. While it is very unlikely that funding issues would arise, I feel it is important for prospective applicants to be aware of what goes on behind the scenes and the small but real possibility that funding issues do eventually arise.
Past research highlights
Understanding neural representations
Vision as Bayesian inference
Improving inference algorithms
Inference dynamics during decision-making