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 Assistant Professor 

Boise State University, Department of Mathematics

mperlmutter@boisestate.edu

 google scholar profile 

Current CV 

Who am I? I will am an Assistant Professor in the Department of Mathematics at Boise State University.  I am also an affiliated with the Computing PHD program, the Masters in Data Science Program, and the Computational and Applied Mathematics (CAM) emphasis within the mathematics major.  

Starting in the Fall of 2026, the Department of Mathematics will be offering a new emphasis on the Mathematics of Data Science within the mathematics major. The website for this emphasis is currently under construction, but if you are student who is interested in studying data science, please feel encouraged to send me an email. Information about other data science opportunities at Boise State is available here.

Within the math deparrtment, I organize the The Scientific comPUting and Data Science (SPUDS) Seminar. If you are interested in joining our mailing list, please send me an email. I also co-organize the Codes and Expansions Seminar (CodEx), "An international remote seminar on the theory and applications of harmonic analysis, combinatorics, algebra, and more"  together with John Jasper (Air Force Institute of Technology), Emily King (Colorado State University), and Dustin Mixon (The Ohio State University).  If you are interested in attending, you can join our mailing list here. (Zoom link is not publicly available to prevent zoom bombing.)

Research Interests: My current research is focused on the development, application, and analysis of machine learning algorithms as well as other areas of data science. and applied mathematics. More specifically, in recent years my primary area of research have been Geometric Deep Learning, i.e., deep learning for graph- and manifold-structured data.  This includes both (a) work on the geometric scattering transform, a predesigned, wavelet-based model of neural networks, and (b) work constructing high-performing networks for signed and/or directed graphs. Recently, I have become increasingly interested in using these methods in biomedical applications such as AI-aided drug discovery, analyzing metabolic networks,  and predicting patient outcomes from single-cell data. 

I have also worked in extensively on phase retrieval problems arising in ptychographic imaging. My work in this field has focused on developing computationally efficient, noise-robust algorithms with provable recovery guarantees for inverse problems arising from structured, locally supported, phaseless measurements.  Additionally, I have also worked on problems related to applied probability, audio denoising, data-set benchmarking,  tensor compression, and have recently begun working on on problems related to remote sensing for ecological data as well as data science for hydrology (water-energy systems).

Bio:  Prior to joining Boise State in the Fall of 2023, I was a Hedrick Assistant Adjunct Professor in the math department at UCLA from Fall 2020-Spring 2023, working under the supervision of Deanna Needell.  From Fall 2017-Summer 2020, I was a postdoc in the  Department of Computational Mathematics, Science and Engineering at Michigan State University working on problems related to Phase Retrieval and the Mathematics of Deep learning under the supervision of Matt Hirn and Mark Iwen.  From Fall 2016-Summer 2017, I was a postdoc in the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill, working on problems in high-dimensional probability under the supervision of Amarjit Budhiraja.  I did my graduate work at Purdue University working on problems on the interface of probability and harmonic analysis (martingale methods for studying singular integral operators) under the supervision of Rodrigo Bañuelos.