David I Shuman

he / him / his

Professor of Data Science and Applied Mathematics

Active

David Shuman Portrait

Office

Milas Hall 359

education

  • Ph.D., Electrical Engineering: Systems, University of Michigan, Ann Arbor (2010)
  • M.S., Applied Mathematics, University of Michigan, Ann Arbor (2009)
  • M.S., Electrical Engineering: Systems, University of Michigan, Ann Arbor (2006)
  • M.S., Engineering-Economic Systems & Operations Research, Stanford University (2001)
  • B.A., Economics, with a Minor in Computer Science, Stanford University (2001)

select courses taught

  • Quantitative Engineering Analysis (QEA)
  • Probabilistic Modeling (ProbMod)

awards

  • 2016 IEEE Signal Processing Magazine Best Paper Award
  • Mathematical Association of America Project NExT Fellow, 2014-2015
  • University of Michigan College of Engineering Distinguished Achievement Award, 2010

research

  • Signal processing on graphs
  • Ranked choice voting
  • Numerical linear algebra
  • Stochastic scheduling and resource allocation problems

Dr. Shuman is currently investigating new tools, theory, and algorithms for processing high‐dimensional data on graphs, which arise in application areas such as energy, social, biological, and sensor networks, machine learning, medical imaging, ranked choice voting, genetics, and astrophysics.

With an interdisciplinary background in economics, operations research, electrical engineering, applied mathematics, and data science, he collaborates with researchers from numerous disciplines and strives to make connections between different fields, both in the classroom and in research. Dr. Shuman joined Olin from Macalester College, where he was an Associate Professor in the Department of Mathematics, Statistics, and Computer Science. In his spare time, David enjoys playing games with his two sons, playing sports, hiking, traveling, photography, and cooking.

Select Publications

Y. Chen, J. DeJong, T. Halverson, and D. I Shuman, "Signal processing on the permutahedron: Tight spectral frames for ranked data analysis," Journal of Fourier Analysis and Applications, vol. 27, art. 70, Aug. 2021.

D. I Shuman, "Localized spectral graph filter frames: A unifying framework, survey of design considerations, and numerical comparison," IEEE Signal Processing Magazine, vol. 37, no. 6, pp. 43-63, Nov. 2020.

D. I Shuman, C. Wiesmeyr, N. Holighaus, and P. Vandergheynst, "Spectrum-adapted tight graph wavelet and vertex-frequency frames," IEEE Transactions on Signal Processing, vol. 63, no. 16, pp. 4223-4235 , Aug. 2015.

D. I Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, "The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains," IEEE Signal Processing Magazine, May 2013.