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Scientific Advisory
Michael Graziano, PhD
Michael Graziano is a professor of neuroscience and psychology at Princeton University. He is also a writer, composer, and occasional ventriloquist. He is the author of many books (both novels and neuroscience books), and has written for the Atlantic, the New York Times, the Wall Street Journal, and other media outlets. His research at the Princeton Neuroscience Institute has spanned topics from movement control to how the brain processes the space around the body. His current work focuses on the brain basis of consciousness. He has proposed the Attention Schema Theory of consciousness, a mechanistic explanation of how brain-based agents believe and insist they contain consciousness inside them.
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Dianbo Liu, PhD
Dianbiu Liu is a postdoc researcher in Prof. Yoshua Bengio group at MILA in Canada. He works on both fundamental machine learning research and applied projects. He has contributed to the design of deep neural architectures exploiting fundamental priors inspired by the working principles in the human brain, such as the GWT consciousness prior and the discrete representation prior.
Alex Lamb, PhD
Alex Lamb is a senior researcher at Microsoft Research NYC and was a PhD student at the University of Montreal advised by Yoshua Bengio and a recipient of the Twitch PhD Fellowship 2020. Alex's research is on the intersection of developing new algorithms for machine learning and new applications. In the area of algorithms, he is particularly interested in (1) making deep networks more modular and richly structured and (2) improving the generalization performance of deep networks, especially across shifting domains. He is particularly interested in techniques which use functional inspiration from the brain and psychology to improve performance on real tasks. In terms of applications of Machine Learning, his most recent work has been on historical Japanese documents and has resulted in KuroNet, a publicly released service which generates automatic analysis and annotations to make classical Japanese documents (more) understandable to readers of modern Japanese.
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