Predictions about future reward drive many (if not all) of our thoughts and behaviors. However, important rewarding events—getting good grades on exams, establishing meaningful relationships, buying your first home—are few and far in between. To work towards these events, we must also predict which behaviors and which intermediary events will bring these rewards into fruition—that studying in a particular manner will provide us with good grades, that some types of social interactions will establish trust and depth of connection, that certain spending habits will facilitate saving for our first home.
Currently, I am a research fellow in Harvard’s Kempner Institute for the Study of Natural and Artificial Intelligence. To study these questions, I develop deep reinforcement learning based theories for human learning and generalization. You can find my recent research on my Google Scholar.
I earned my Ph.D. at the University of Michigan, where I studied deep reinforcement learning with Satinder Singh, Honglak Lee, and Richard Lewis, and was supported by the NSF GRFP and a Rackham Merit Fellowship. During my PhD, I was fortunate to spend significant time at DeepMind working with Murray Shanahan, Daniel Zoran, and Danilo Rezende. I got my first stint in ML in a CS M.S. at USC, where I worked with Yan Liu on machine learning for healthcare. Before then, I earned a B.S. in Physics at Stony Brook University where I worked with Axel Drees on computational nuclear physics.
Please feel free to contact me if you’d like to collaborate or be mentored on a research project! While my training is in machine learning, I hope to collaborate broadly with neuroscientists and cognitive scientists.
You can reach me at wcarvalho[at]g.harvard.edu
I also maintain a collection of resources for
