Stephen Becker

 

Associate Professor, Applied Mathematics
University of Colorado at Boulder
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I joined the department of Applied Math in fall 2014. Previously I was a Herman Goldstine Postdoctoral fellow in Mathematical Sciences at IBM Research in Yorktown Heights, NY, and a postdoctoral fellow via the Fondation Sciences Mathématiques de Paris at Paris 6. Until 2011, I was at Caltech where I did my doctoral work under Emmanuel Candès.

Our group thanks:


Proud member of the Math Alliance


Contact

News

Call for papers: special issue on Big Scientific Data and Machine Learning in Science and Engineering, submission deadline Feb 1 2022

Group Research site

Please note: this website (amath.colorado.edu/faculty/becker) is being (slowly) deprecated in favor of a more homogenous faculty website.

Yet another website is our research group website with public information about our research activities as well as our group's internal collaboration tools.

My CV is on my CU Experts Profile page.

Research interests

Broadly speaking, our group is interested in information extraction from various types of datasets. We are part of a hybrid field combining applied math with computer science and signal processing techniques. Some specific topics we research are:

Students interested in working with our group: If you are not currently a student, please apply to our undergraduate or graduate program and your application will receive full consideration.

Here's a somewhat more cohesive summary of our research:

“The Becker group focuses on optimization, signal processing, machine learning, and numerical methods. One main thrust is collaboration with engineering groups to design signal recovery algorithms for imaging devices (microscopes, ultrasound, MRI, photoacoustic imaging, brain imaging via MEG) and remote sensing (radar, visible and hyperspectral imaging from satellites). Another main area is matrix and tensor factorizations designed for streaming data, used for machine learning or scientific computation. Finally, a common factor in much of our research is optimization, linear algebra, and randomized methods. Sometimes we study these methods on their own, and sometimes we use these methods for applications. For example, we look at convergence of new stochastic optimization methods, either in terms of optimization error or (for machine learning) generalization error. Some recent optimization work considers the case when we cannot find a derivative easily, with applications to shape optimization and other PDE-constrained optimization.  Other recent work uses optimization to design statistical estimators with rigorous confidence intervals for use in quantum tomography.”

Collaborators at CU and nearby (2020 and recent years)

Specific research projects, 2018

See a high-level overview of a few of our projects from a 30 minute colloquium talk I gave in 2018: Colloquium talk slides

To give you an idea of what we're up to in 2018:

Students

See our Group Members website (updated infrequently though)

Current PhD students

Current PmD students (Professional masters Degree)

Alumni (PhD and Masters)

Interested in undergrad research with our group? Here are some programs you can use to get funding

Quick links

Recent News

A similar list of announcements is at the Announcements Page of our Google Site

Interesting links, updated at least once per decade