Chad Vidden

Assistant Professor
Mathematics And Statistics
1009 Cowley Hall
608.785.5214
cvidden@uwlax.edu

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 Assistant 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.

Current courses at UWL

MTH 207 - Calculus I
Data Science Research Group

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

Assistant Professor of Mathematics, University of Wisconsin - La Crosse, August 2013 - present.
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.

Education

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

Important links