SEAN I YOUNG, PhD | About Me | Curriculum Vitae | Publications | Google Scholar | E-mail
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Biography. I am Instructor of Radiology at the Martinos Center, Harvard Medical
School (supported by a NIH K99/R00 Career Development Award) and Research
Affiliate with the Computer Science and Artificial Intelligence Lab (CSAIL) at the
Massachusetts Institute of Technology, where I design novel computational imaging
methods for radiology. Previously, I was a Postdoctoral Scholar in the Department
of Electrical Engineering, Stanford University, where I worked on computational
imaging and model compression. I received my PhD in electrical engineering from
the University of New South Wales, Sydney, NSW, Australia. My research expertise
lies in the design of novel methods for computational imaging and, in particular, 3D image reconstruction
and related inverse problems in medical imaging. In 2018, I received the Australian Pattern Recognition
Society (APRS)’s best paper award for my work on “fast optical flow extraction from compressed video”.
Research Statement. I seek to improve healthcare outcomes by making fundamental contributions to the
science of computational radiology. Examples of computational radiology problems I have solved include
accurate MRI of moving subjects from a single MR slice stack; registration of medical images of different
contrast to deci-voxel accuracy; and supervised image reconstruction with few labeled images. Coming from
an imaging and signal processing background, I am also interested in solving more general computational
imaging problems in hopes that the developed techniques will find use in radiology one day. Examples of
computational imaging problems I have solved include 100x faster motion estimation using filtering; non-
line-of-sight surface reconstruction using Cholesky–Wiener filtering; and 30x smaller convolutional neural
networks using transform quantization for real-time imaging. Continue reading here.
Teaching and Diversity Statements. These can be found here, and here.
Funded NIH K99/R00 Application. Due to popular demand, I have made my application available here.
Latest News
Sep 10, 2024 I gave an invited talk on “Efficient methods for computational radiology and imaging: from
image reconstruction to neural network compression” at Johns Hopkins ECE. Thanks Jerry
Prince for the invite! Talk slides can be found here.
Jun 14, 2024 My work “Foundations of large language model compression. Part 1: weight quantization”
is to be submitted soon for review. Check back here for the preprint!
May 17, 2024 A bunch of us were invited to present in Randy Buckner’s group. We had fun! I presented
my CVPR work “Fully convolutional slice-to-volume reconstruction for single-stack MRI”.
Feb 26, 2024 I’ll be presenting my work “Fully convolutional slice-to-volume reconstruction for single-
stack MRI” at CVPR 2024. Come check out our poster in the Thursday AM session!
Jan 16, 2024 I’m giving an invited talk on “Efficient methods for computational radiology and imaging”
at Cornell next week. Thanks Mert Sabuncu for the invite! Talk slides can be found here.
Dec 12, 2023 Our work on the reconstruction of dissection photographs for 3D neuropathology (led by
Harshvardhan Gazula) has been accepted for publication in eLife. See more here.
Oct 6, 2023 I am coorganizing the first edition of the Auckland–Boston Workshop on Medical Imaging
at the Auckland Bioengineering Institute. The registration link can be found here.
Jun 14, 2023 I’ll be presenting my work “Supervision by denoising” at ICCP 2023 (oral). This work will
be published in an upcoming issue of the IEEE Trans Pattern Anal Mach Intell.
