Ajil Jalal
News
April 2022: I defended my Ph.D. thesis!
September 2021: Our paper on
posterior sampling for MRI has been accepted at NeurIPS 2021. We
show that posterior sampling with score-based generative models is
competitive with state-of-the-art deep learning methods. We also
show that our technique is robust to anatomy shift and shift in MRI
sampling patterns. Theoretically, we show that posterior sampling
with Gaussian measurements is extremely robust to distribution
shift, while posterior sampling with arbitrary measurements is
competitive if there's no distribution shift.
May 2021: Three new papers at ICML 2021. We prove that
Posterior Sampling is
instance-optimal for compressed sensing and we propose
new definitions for fairness in
generative processes. Code and models are available here:
link to code.
October 2020: Our paper has been selected for an oral presentation at the
NeurIPS 2020 Workshop on Deep Learning and Inverse Problems,
Vancouver, Canada. We show that conditional sampling is
provably good for solving linear problems using full
dimensional generative models.
October 2020: Our paper
shows how generative models can give performance improvements in
channel estimation, and will appear in the IEEE JSAC Series on
Machine Learning for Communications and Networks.
September 2020: Our paper has
been accepted as a poster presentation at NeurIPS 2020,
Vancouver, Canada. We develop a new robust algorithm for
compressed sensing using generative models.
May 2020: New survey paper on
deep learning techniques for inverse problems in imaging. We
introduced a new taxonomy on supervised versus unsupervised
methods.
January 2020: Invited talk at IIT Madras.
December 2019: I organized the Deep Learning and Inverse Problems
social at NeurIPS 2019, Vancouver, Canada! I generated $1000 in
funding for this social.
November 2019: Invited talk at Asilomar Conference on Signals,
Systems, and Computers, in California, USA.
About Me
I am a postdoctoral scholar at UC Berkeley working with
Prof. Kannan Ramchandran.
Before this, I spent six wonderful years at UT Austin advised by
Prof. Alex Dimakis,
and have been lucky to collaborate with
Prof. Eric Price on several
projects. I received my B.Tech. (Honors) in Electrical Engineering
from IIT Madras in 2016, where I worked closely with
Prof. Rahul Vaze,
Prof. Umang Bhaskar, and
Prof. Krishna Jagannathan.
My research explores the theory and practice of algorithms that use
deep generative models for solving signal processing problems. On the
theoretical side, my work has produced algorithms that are provably
robust and achieve optimal statistical complexities. On the practical
side, our algorithms are competitive with state-of-the-art deep
learning methods on fastMRI data, with the added benefits of
flexibility and modularity.
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