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2019 André Aisenstadt Recipient

CRM > Prizes > André Aisenstadt Prize > Recipient > Yaniv Plan (UBC)

2019 André Aisenstadt Prize in Mathematics Recipient
Yaniv Plan (UBC)

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Yaniv Plan

Professor Yaniv Plan will give his conference on November 15, 2019, 4:00 pm in room 6214 at the CRM - Pavillon André-Aisenstadt

Title: The role of random models in compressive sensing and matrix completion

Watch the video of the conference

Abstract: Random models lead to a precise and comprehensive theory of compressive sensing and matrix completion. The number of random linear measurements needed to recover a sparse signal, or a low-rank matrix, or, more generally, a structured signal, are now well understood. Indeed, this boils down to a question in random matrix theory: How well conditioned is a random matrix restricted to a fixed subset of R^n? We discuss recent work addressing this question in the sub-Gaussian case. Nevertheless, a practitioner with a fixed data set will wonder: Can they apply theory based on randomness? Is there any hope to get the same guarantees? We discuss these questions in compressive sensing and matrix completion, which, surprisingly, seem to have divergent answers.


Prof. Yaniv Plan obtained his PhD at Caltech in 2011 under the supervision of Emmanuel Candès, a leading figure in the area of mathematics of information and data and a cofounder of the field of compressed sensing. Between 2011 and 2014, he was an NSF Postdoctoral Fellow and a Hildebrandt Assistant Professor in Mathematics at the University of Michigan, Ann Arbour. In 2014, he joined the Mathematics Department of UBC where he is currently a tenure-track Assistant Professor and holds a Tier 2 Canadian Research Chair (CRC).

Prof. Plan's research is in the general area of mathematics of information that interacts with various fields including high dimensional data analysis, machine learning, harmonic analysis, probability, signal processing, and information theory. Prof. Plan's focus has been mostly on compressed sensing and its generalizations such as low-rank matrix and tensor recovery.

The last decade has seen the construction of an elegant and comprehensive foundation for the theory of CS. Here is a list of fundamental contributions Prof. Plan has already made in this extremely hot area at this early stage of his career.

- Theory of compressed sensing;
- Low-rank matrix completion;
- -One-bit compressed sensing;
- -High dimensional data analysis.

In summary, Prof. Yaniv Plan has an impressive record of research.