Past Updates

ICLR 2024: Presented two posters in Vienna: Learned Proximal Networks (Friday, May 10, a.m. session; project page, ICLR page) and CRATE-MAE (Thursday, May 9, a.m. session; project page, ICLR page). (May 2024)

1st Conference on Parsimony and Learning: I co-organized the inaugural Conference on Parsimony and Learning (CPAL), which took place at the University of Hong Kong from January 3–6, 2024. Thanks to all authors, speakers, organizers, and especially to the local team at HKU, whose hard work made the conference a success! Stay tuned for CPAL 2025. (Jan 2024)

(December 2023) New preprint posted on methodology for data-driven inverse problem solvers with convergence guarantees. We presented this work at the NeurIPS 2023 Learning-Based Solutions for Inverse Problems workshop.

(June 2023) We taught a short course at ICASSP 2023 in Rhodes, Greece, titled “Learning Nonlinear and Deep Low-Dimensional Representations from High-Dimensional Data: From Theory to Practice”.

(January 2023) I co-organized the third Workshop on Seeking Low-Dimensionality in Deep Neural Networks (SLowDNN). Here is a link to my tutorial.

(September 2022) I defended my Ph.D. thesis (back in June!), and started as a Research Assistant Professor at TTIC.

(May 2022) I received the Eli Jury Award from the Columbia EE Department for “outstanding achievement in the area of signal processing”.

(May 2022) We taught a short course at ICASSP 2022 in May, titled “Low-Dimensional Models for High-Dimensional Data: From Linear to Nonlinear, Convex to Nonconvex, and Shallow to Deep”. Slides are available!

(April 2022) I attended the Princeton ML Theory Summer School this summer from June 13–17.

(March 2022) New preprint released on invariance-by-design neural architectures for computing with visual data, with theoretical guarantees. Feedback is very much appreciated!

(December 2021) We presented our paper Deep Networks Provably Classify Data on Curves at NeurIPS.

(August 2021) I gave a talk about our work on the multiple manifold problem at the IMA Workshop on Mathematical Foundation and Applications of Deep Learning at Purdue. Thanks to the organizers for the opportunity to speak!

(July 2021) I will be attending the Princeton Deep Learning Theory Summer School this year.

(May 2021) We will present our paper “Deep Networks and the Multiple Manifold Problem” at ICLR 2021 on Thursday, May 6th! Conference link here, paper link here.