Profile for Chad Vidden

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Specialty area(s)

  • Machine Learning and Data Science with Application in Business and Marketing
  • Numerical Analysis and Computational Mathematics for Solving Partial Differential Equations

Brief biography

I revieved a PhD from Iowa State University in Applied Mathematics in 2012. Prior to that I revieved a bachelors degree from Minnesota State University - Mankato in Mathematics and Computer Science.

I am currently an Associate Professor of Mathematics at the UWL where my interests include:

  • teaching undergraduate mathematics with special emphasis on applications,
  • co-organizing a student research group on Machine Learning and Data Science, 
  • researching cutting machine learning techniques and applying ideas to business and marketing,
  • and collaborating with local companies on research and student opportunities.

For more information on student research and opportunities, see LINK.

Education

Ph.D. Applied Mathematics, Iowa State University, 2013
B.S. Mathematics, Minnesota State University - Mankato, 2007

Teaching history

MTH 150, 151, 207, 208, 309, 371, 480, 498

Independent Study Projects:

  • Numerical Methods for Differential Equations
  • Sports Ranking through Linear Algebra
  • Machine Learning (Archive of student work)
  • Exoplanet Detection through Fast Fourier Transform

Professional history

Associate Professor of Mathematics, University of Wisconsin - La Crosse, August 2013 - current.
Assistant Professor of Mathematics, University of Wisconsin - Platteville, August 2012 - May 2013.
Graduate Teaching Assistant, Iowa State University, August 2007 - May 2012.
Teaching Assistant, Minnesota State University - Mankato, August 2005 - May 2006.

Research and publishing

I am currently writing a book with colleagues Song Chen and Marco Vriens to be completed in Spring 2018.

  • Practicing Analytics: A Hands-On Introduction for Marketing Executives (with R examples) 

Machine Learning, data science, business, marketing:

  • S. Chen, C. Vidden and M. Vriens. Predicting Market Shares Using Brand Density Metrics. (in press)
  • S. Chen, C. Vidden and M. Vriens. Solving Marketing Problems with Market Structure Analysis: Survey -Based Versus Big Data Apporaches. (in press)
  • S. Chen, C. Vidden and M. Vriens. Assessing the Impact of a Brand Crisis using Big Data: The case of the VW Diesel Emission Crisis. 2017 DMA Analytics Journal, Section 2: Strategic Practitioners, 2017
  • S. Chen, C. Vidden and M. Vriens. Comparing clustering methods for market seg- mentation: A simulation study. Applied Marketing Analytics, Volume 2, Num- ber 3, 225-238, 2016
  • S. Chen, C. Vidden and M. Vriens. On Finding the Best Segmentation Solution: The superior performance of latent class and latent class ensemble methods in re- covering the true number and nature of segments. Sawtooth Software Conference 2016

Numerical analysis, numerical methods for partial differential equations, finite element methods, discontinuous Galerkin methods:

  • A new approach for admissibility analysis of the direct discontinuous Galerkin method through Hilbert matrices. Numer. Methods Partial Differential Equations, 2016.
  • A new direct discontinuous Galerkin method with symmetric structure for nonlinear diffusion equations (with Jue Yan), Journal of Computational Mathematics, 2013.
  • Invariant measures for hybrid stochastic systems (with X. Garcia, J. Kunze, T. Rudelius, A. Sanchez, S. Shao, E. Speranza), Involve 2014.
  • Trading cookies in a gambler's ruin scenario (with K. Jungjaturapit, T. Pluta, R. Rastegar, A. Roiterschtein, M. Temba, B. Wu), Involve, 2013.

Important links