I’m a research scientist interested in improving learning, memory, and reasoning in machine learning models. My current and previous work has sought to apply ideas from probabilistic machine learning and computational neuroscience to these research directions. I currently work at the start-up Goodfire, an interpretability focused research lab. Although I love working on machine learning projects, I dabble in philosophy, which you can find on my blog.
I have an interdisciplinary educational background. I studied computational cognitive science (i.e., psychology and AI) and philosophy at University of Michigan during my undergraduate. Afterward, I was a fellow at Georgia State’s neuroscience institute, where I received a Master’s in philosophy that focused on philosophy of mind and cognitive science. After several years of pondering the nature of consciousness, I redirected my research to computational cognitive science and artificial intelligence at UC Irvine, where I received my PhD. My dissertation focused on brain-inspired learning algorithms and neural networks.
