Amit Pal Singh Kohli
About me
My name is Amit Kohli, I am a 4th year PhD student at UC Berkeley advised by Professor Laura Waller.
I work primarily in the field of computational imaging and have a particular interest at the
intersection
optics and statistics. The goal of my research is to design and analyze robust computational imaging systems—ones
which enjoy algorithmic enhancements but remain impervious to the inevitability of system errors and
imperfect models.
Outside of research I enjoy listening to and making music; I play guitar and bass, and have a passion
for producing and post-processing recorded music. I also
love to be active; I dance Bhangra and play sports
including basketball, baseball, football, tennis, and golf, to name a few. I especially enjoy going to
watch the local Bay Area sports teams in-person! Lastly,
in my spare time, I love to hang out with my family, including my brother's dog, Buster.
Fields of interests: Optics, Statistics, Computational Imaging, Signal Processing, Machine Learning.
Contact me
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I am best reached via email: apkohli at berkeley dot edu.
Recent news
- November 2023 - Our paper, Random optics for improved digital aberration correction, was just accepted to Photonics West 2024 for an oral presentation. This paper demonstrates a simple way to make an imaging system entirely aberration-free!! Stay tuned for the publication.
- August 2023 - Introducing Ring Deconvolution Microscopy: An Exact Solution for Spatially-Varying Aberration Correction. This exciting new paper provides a easy-to-use method for deblurring images that have spatially-varying blur. We also released an accompanying python package called rdmpy. This paper introduces a new image deblurring method, which incorporates physical information about the imaging system.
- January 2023 - We presented SeidelNet: an aberration-informed deep learning model for spatially varying deblurring at Photonics West 2023. This paper introduces a new image deblurring method, which incorporates physical information about the imaging system.
- July 2022 - We presented Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging at ICML 2022. This paper develops a method for adding for distribution agnostic, per-pixel prediction intervals to the output of any image-to-image regression model. Check out the poster and codebase.
- June 2022 - "Linear Revolution-Invariance: Modeling and Deblurring Spatially-Varying Imaging Systems" is up on arXiv. This paper develops and analyzes an exact forward model for rotationally symmetric imaging systems; inverting it produces a fast, accurate deblurring method. Try it out yourself with the accompanying codebase!
- May 2022 - Rigorous Uncertainty Estimation for MRI Reconstruction was presented at ISMRM 2022. I had a blast collaborating with Ke Wang in Prof. Miki Lustig's group for this one! The paper applies conformal prediction to MRI reconstructions of undersampled data in order to get rigorous, image-valued prediction intervals.