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Quantitative Methods Area

Faculty

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The Faculty in Quantitative Methods Area:

  • Andy Alexander: Ph.D. from Purdue University (2014); areas of interests: parameter analysis in decision modeling, statistical quality control, contract design and analysis in manufacturing and maintenance systems, data science, big data, operations research, sports analytics, and optimization

  • Gary Evans: Ph.D. from University of California, Los Angeles (2014); areas of interest: multivariate analysis, optimization algorithms, and statistics education

  • Matthew A. Lanham: Ph.D. from Virginia Tech (2016); areas of interests: big data analytics and data science, choice modeling and demand forecasting, decision support systems, assortment planning, multi-echelon inventory optimization, and supply chain information systems

  • Yanjun Li: Ph.D. from Carnegie Mellon University (2002); areas of interests: combinatorial optimization, integer programming, polyhedral theory, polynomial-time algorithms, and complexity analysis

  • Tongseok Lim: Ph.D. in British Columbia (2016); areas of interest: (i) (Martingale–) Optimal Transport in multi-dimensions and its applications to Economics, Finance and Statistics, (ii) Analysis of Variational problems arising in Physics, Geometry and Data Science, and (iii) Cooperative Game Theory in light of combinatorial Hodge Theory. TL’s research diversity is partly reflected in the various journals he has published in. For more information, see his Homepage: https://tlim0213.github.io. TL is interested in teachings in which topics are mathematical, with keywords: Analytical, Geometric, Probabilistic, Statistical, Algebraic, Economic, Financial. Currently he teaches Predictive Analytics (MGMT474)

  • Thanh Nguyen: Ph.D. from Cornell University (2010); areas of interests: optimization, game theory, market design and its applications

  • Robert D. Plante: Ph.D. from University of Georgia (1980); areas of interests: statistical quality control and improvement with focus on robust product/process design, screening procedures for process control and improvement, statistical/process/dynamic process control models, and specialized process improvement problems

  • Will Wei Sun: Ph.D. from Purdue University (2015); areas of interests: Machine Learning: reinforcement learning (multi-armed bandits), deep learning (interpretable convolutional neural networks, deep generative adversarial network on graphs); tensor learning; non-convex optimization. Data Science: computational advertising

  • Jen Tang: Ph.D. from Bowling Green State University (1981); areas of interests: multivariate statistical analysis, bootstrap method, statistical computing, applied diffusion processes, statistical process control and engineering control, data mining, reliability and degradation tests, and stochastic models in operations research/management

  • Kwei Tang: Ph.D. from Purdue University (1984); areas of interest: data mining, quality control and management, service chain management, and inventory management

  • Mohit Tawarmalani: Ph.D. from University of Illinois at Urbana-Champaign (2001); areas of interests: mathematical programming, complexity and approximation, symbolic computing, global optimization theory, algorithms and software, applications and models in business, economics, systems, engineering design, and molecular design

  • Philip Thompson: Ph.D. from IMPA (Dec. 2015); area of interest: mathematical theory of algorithms in machine learning, high-dimensional statistics, and stochastic optimization; application of such algorithms to business problems. Current focus: estimation/prediction with contaminated data and variants of stochastic gradient descent. Have taught/is teaching: MGMT 690 - High-dimensional statistics (PhD/2 credits/X1), MGMT 305 - Business Statistics (BS/3credits/X4), MGMT 590 - High-dimensional Data Analysis (BAIM/2 credits/X2), MGMT 690 - Stochastic Iterative Methods (PhD/3credits/X1), MGMT 590 - Multivariate Analysis for Business (BAIM/2credits/Fall 2022), MGMT 389 - Using R for Business Analytics (BS/3credits/Spring 2023). Webpage: https://sites.google.com/view/philipthompson2015

  • Yichen Zhang: Ph.D. from New York University (2020); areas of interest: statistical learning theory, distributed estimation and inference, stochastic optimization, time series econometrics and their applications in financial data, and statistical process control. Please refer to https://web.ics.purdue.edu/~yichen/ for more details in the research and publications

  • Zhiwei Zhu: Ph.D. from Michigan State University (2001); areas of interests: applied statistics, decision sciences, business communication, analytics strategy and leadership, data infrastructure and governance, and business intelligence

  • Weibin Mo: Ph.D. from University of North Carolina at Chapel Hill (2021); areas of interests: statistical methodologies in machine learning, personalized decision making, causal inference, and robust optimization, with applications to revenue management and precision medicine. Please refer to https://sites.google.com/view/weibin-mo