Drew Linsley (Google Scholar) (Website) is a Paul J. Salem senior research associate at Brown, advised by Thomas Serre. He is working on building computational models of the visual system that serve the dual purpose of (1) explaining biological function and (2) extending artificial vision. Prior to his work in the Serre lab, he completed a PhD in computational neuroscience at Boston College and a BA in Psychology at Hamilton College. His most recent paper at NeurIPS is Stable and expressive recurrent vision models.  It presents an alternative to back-propagation through time (BPTT) for recurrent vision models called "contractor recurrent back-propagation" (C-RBP), which has O(1) complexity for an N step model vs. O(N) for BPTT, and which learns long-range spatial dependencies in cases where BPTT cannot. Drew is also organizing an ICLR 2021 workshop named Generalization Beyond the Training Distribution in Brains and Machines on Friday, May 7th, 2021. Find them on the website and @ICLR_brains. Lastly, Drew is looking to work with collaborators in robotics, so feel free to reach out! Highlights from our conversation: 🧠 Building neural-inspired inductive biases into computer vision 🖼 A learning algorithm to improve recurrent vision models (C-RBP) 🤖 Creating new benchmarks to move towards generalization