Greg Buzzard, Professor and Head
WelcomeThe math department plays a vital role in the educational and research missions of Purdue; we enroll thousands of students in our classes each year, and our scholarship is well-represented in top academic journals, in partnership with other departments and with industry, and in external funding. We welcome your interest in the department and invite you to explore our website and get to know us better.
Here's a short video by Eugenia Cheng, Scientist In Residence at the School of the Art Institute of Chicago, about how amazing math is and how we should all describe it that way.
Here are some examples of the many ways that math can be used in applications from nuclear waste storage to movie animation.
The Department of Mathematics currently has several faculty openings at a variety of levels and multiple fields.
See here for more information.
MA 59800 Mathematical Aspects of Neural Networks
Section 040, CRN 17703
Location: Mon and Fri classes will be in WALC 1121. Wed classes will be in WTHR 320 in conjunction with the Machine Learning Seminar.
ResearchPublications and software:
Much of my recent work is joint with Charlie Bouman in Purdue ECE. We've developed methods for dynamic sampling and reconstruction with a variety of sensing modalities. At a more fundamental level, we've developed the foundations of the Plug and Play method for image reconstruction into a formulation we call Consensus Equilibrium, which gives a principled framework for reconciling sensing data and external information, such as a preferred denoising method, even when one or both of these information sources is not easily cast in an optimization framework.
My previous work encompassed topics in cellular signaling networks and Raman spectroscopy, with earlier work on dynamics in several complex variables. The underlying unifying ideas are methods and algorithms for uncertainty quantification and reduction of uncertainty through appropriate measurement schemes. In conjunction with a number of collaborators, my work in these areas has led to theoretical advances in the construction of surrogate functions and the use of such functions for sensitivity analysis and experiment design, to extensions of the classical theory of optimal design of experiment, and to new algorithms for supervised classification. These theoretical advances have fueled a wide variety of applications, including novel experiment design and control methods for cellular-level control of immune cell response, a Raman spectroscopy system that significantly outperforms previous methods in the high-speed regime, and an adaptive approach for sampling images that forms the basis for algorithms for electron microscopy and other imaging modalities.
ConferenceApproximation Theory and Machine Learning, Purdue University, September 29 - 30, 2018
Slides and videos are available through the link above.
- The case for basic research by Eric Lander at the National Math Festival in 2015.
- Mathematics: 1,000 Years Old, and Still Hot
- What can I do with a major in... MATHEMATICS?
- Other interesting links.
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