George Alvarez

Bio

I'm George A. Alvarez, Professor of Psychology at Harvard University, and co-director of the Vision Sciences Laboratory. I was born in Honolulu, Hawaii, and raised in Watsonville, California (go Wildcats!), where I attended public schools through high school. I'm a first-generation college student, and was fortunate to attend Princeton University (go Tigers!) as an undergraduate, Harvard University for graduate school, and MIT for my postdoctoral work. If you would like to learn more about my path to professorship, you can read this APS writeup.

In my earlier days my hobbies included sports (baseball, basketball, American football), movies, and studying film-making. These days my time is divided between running the VisionLab, teaching, and raising two kiddos, so my hobbies have gravitated towards typical dad stuff. I'm told I make a killer grilled cheese sandwich.

Algorithms for Seeing

My early research focused on characterizing and understanding limits on our ability to attend to, keep track of, and remember visual information — our visual cognitive capacities. In many cases, a deeper understanding of these limits seemed to demand a deeper understanding of visual representation formats, but these ideas were not easily testable, because our field had not yet developed scalable, performant models of visual encoding beyond relatively early visual processing stages.

Since 2012, however, we have seen a veritable explosion in the availability of highly performant vision models from the fields of deep learning, machine vision, and artificial intelligence (or is that one field?). New models with new algorithms and new abilities are released on a quarterly basis, each presenting intriguing hypotheses for the nature of visual representation in humans, and opportunities for a deeper understanding of visual cognition in both humans and machines.

Thus, my ongoing work focuses primarily on this intersection between human and machine vision. We import algorithmic and technical insights from machine vision to build models of human vision, and apply theories of human vision and the "experimental scalpel" of human vision science to probe the inner workings of deep neural networks and build more robust and human-like machine vision systems. Ultimately, I hope to contribute to the virtuous cycle between human vision science, cognitive neuroscience, and machine vision.