Profile for Chad Vidden
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Chad Vidden
Professor
Mathematics & Statistics
University of Wisconsin-La Crosse
Chad Vidden
Professor
Mathematics & Statistics
Specialty area(s)
- Machine Learning, AI, and Data Science with Applications in Business and Education
- Numerical Analysis and Computational Mathematics for Solving Partial Differential Equations
Brief biography
I recieved a PhD from Iowa State University in Applied Mathematics in 2012. Prior to that I recieved 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,
- collaborating with student researchers on Machine Learning and Data Science projects,
- researching cutting machine learning techniques and applying ideas to business and education,
- and collaborating with local companies on research and student opportunities.
Education
Ph.D. Applied Mathematics, Iowa State University, 2013
B.S. Mathematics, Minnesota State University - Mankato, 2007
Career
Teaching history
MTH 150, 151, 207, 208, 308, 309, 301, 353, 371, 480, 498
Independent Study Projects:
- Numerical Methods for Differential Equations
- Sports Ranking through Linear Algebra
- Machine Learning
- 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
Textbook publication
- From Data to Decision: A Handbook for the Modern Business Analyst by M. Vriens, S. Chen, and C. Vidden, Cognella Academic Publishing, December 2018.
Machine Learning, data science, business, education:
- S. Floersch, A. Jagim, and C. Vidden. "Data Driven Training Optimization for Division III Women's Soccer" (in progress, in collaboration with UWL soccer)
- M. Vriens, N. Bosch, C. Vidden, and J. Talwar. "Prediction and profitability in market segmentation typing tools". Journal of Marketing Analytics (2022).
- S. Floersch and C. Vidden, "Why the 2020 Dodgers are the Greatest Team of all Time, at Least Statistically." UWL Journal of Undergraduate Research, XXIV, 2021.
- M. Vriens, C. Vidden, and N. Bosch. "The benefits of Shapley-value in key-driver analysis." Applied Marketing Analytics 6.3 (2021): 269-278.
- M. Vriens, C. Vidden, and J. Schomaker. "What I see is what I want: Top-down attention biasing choice behavior," Journal of Business Research, Elsevier, vol. 111(C) (2020): 262-269.
- M. Vriens, and C. Vidden. "The Linux Compete strategy: An analytics case study." Applied Marketing Analytics 5.2 (2019): 129-136.
- M. Vriens, S. Chen, and C. Vidden. "Mapping brand similarities: Comparing consumer online comments versus survey data." International Journal of Market Research 61.2 (2019): 130-139.
- Q. Burzynski, L. Frank, Z. Nordstrom, J. Wolfe, S. Chen, and C. Vidden. "Profit Optimization." UWL Journal of Undergraduate Research, XXI, 2018.
- S. Chen, C. Vidden, N. Nelson, and M. Vriens, "Topic modelling for open-ended survey responses." Applied Marketing Analytics 4.1 (2018): 53-62.
- M. Vriens, C. Vidden, S. Chen, and S. Kaulartz, "Assessing the impact of a brand crisis using big data: The case of the VW Diesel emission Crisis." DMA Annual Analytics Journal (2017): 107-118.
- C. Vidden, M. Vriens, and S. Chen. "Comparing clustering methods for market segmentation: A simulation study." Applied Marketing Analytics 2.3 (2016): 225-238.
- J. Meyers, D. Morrison, S. Chen, and C. Vidden, "Math Department Schedule Optimization." UWL Journal of Undergraduate Research, XIX, 2016.
Numerical analysis, numerical methods for partial differential equations, finite element methods, discontinuous Galerkin methods, applied mathematics:
- C. Vidden, "A new approach for admissibility analysis of the direct discontinuous Galerkin method through Hilbert matrices." Numer. Methods Partial Differential Equations, 2016.
- C. Vidden and J. Yan, "A new direct discontinuous Galerkin method with symmetric structure for nonlinear diffusion equations.", 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.
Kudos
published
published
published