David Lindell
Assistant Professor at AXL: Human Potential, AI Superpowered
Old Toronto, Ontario, Canada
Summary
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Senior🎓
Top SchoolDavid Lindell is an Assistant Professor of Computer Science at the University of Toronto and a Faculty Affiliate at the Vector Institute and AXL, specializing in "physically based intelligent sensing" that blends physics, signal processing, and AI to push the limits of visual reconstruction. With a PhD and postdoc from Stanford and 11 years of research experience, his work spans time-of-flight sensors, imaging around corners, and next-generation LiDAR, earning top honors like the Marr Prize (ICCV Best Paper), CVPR Best Student Paper, and an ACM SIGGRAPH dissertation recognition. He has attracted competitive awards including the Ontario Early Researcher Award and Google Research Scholar support, reflecting both academic impact and translational promise. Known for combining deep theoretical insight with practical systems work—from sensor design to algorithm—he leads a research group that routinely advances what can be sensed and reconstructed from limited measurements.
11 years of coding experience
3 years of employment as a software developer
Master's degree, Electrical Engineering, Master's degree, Electrical Engineering at Brigham Young UniversityDoctor of Philosophy (PhD), Electrical Engineering, Doctor of Philosophy (PhD), Electrical Engineering at Stanford University
English
Github Skills (10)
imaging10
siggraph10
scene9
volume-rendering9
pytorch6
activation-function4
yaml4
pbrt2
physically-based-rendering1
animation1
Programming languages (3)
Github contributions (5)
computational-imaging/nlos-fk
Apr 2019 - Jun 2022
Processing code for "Wave-Based Non-Line-of-Sight Imaging using Fast f-k Migration"
Contributions:13 commits, 7 pushes, 1 comment in 3 years 2 months
imaging
computational-imaging/bacon
Jan 2022 - Nov 2022
Official respository for "Band-limited Coordinate Networks for Multiscale Scene Representation" | CVPR 2022
Contributions:12 commits, 1 PR, 6 pushes in 10 months
pytorchrepresentationbandmultiscalescene
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