Operations Research (OR)  Methodologies: 

• Mathematical Optimization or Prescriptive Analytics (PA):
  
  • Linear and integer programming
  • Stochastic programming (two-stage and multi-stage)
  • Non-linear programming
  • Dynamic programming
  • Neuro-dynamic programming (reinforcement learning) and machine learning
  • Heuristic optimization

• Data Science and Statistics (DS&S):
  
  • Multiple imputation
  • Bootstrapping

• Parallel Computing (PC) to efficiently solve large-scale OR problems
  
  • GPU Computing
  • MPI
  • Open MP

• Machine Learning (ML):
  
  • Supervised learning
    • Feature selection using heuristic optimization
    • Classification and feature selection methods using nearest neighbor, linear regression, ridge regression, logistic regression, support vector machines, naive Bayes, decision trees, ensemble methods
  • Unsupervised learning:
    • Classification and feature selection methods using K-means clustering and Gaussian mixture models, Principal component analysis (PCA) and Kernel PCA
  • Deep learning
  • Reinforced Learning

Applications researched using PA, PC or ML:

• • Renewable energy planning considering multiple stochastic parameters
  • Supply Chain Optimization under uncertainty (food supply chains, recycling supply chains)
  • Cybersecurity of cyber-physical systems
  • Stochastic and dynamic facility layout problems
  • Quadratic assignment problem https://claranovoa.github.io/
  • Stochastic and dynamic vehicle routing
  • Supplier selection problems for electronic supply chains
  • Semiconductor testing

Applications researched using DS&S:

• • Design and analysis of experiments with missing values
  • Bootstrapping for time studies
    

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Additive Manufacturing (AMA)

• • Design of experiments
  • Optimization
  • Materials science

Applications researched in AMA:

• • Optimizing the tensile strength of parts fabricated using Additive Manufacturing

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Engineering Education (EE):

• • Improving the recruitment and retention of female in Engineering and Computer Science
  • Improving retention of first-year undergraduate students, especially underrepresented minorities, through (a) multi-disciplinary orientations  (b)  training workshops to refine students’ Spatial Visualization and Computational Thinking Skills (c) use of 3D printing.

Applications researched in EE:

• • Identifying critical activities that  promote the success of female cohorts in engineering and computer science
  • Identifying primary motivators for freshman students to pursue STEM careers
  • Assessing the benefits of Lego robotics activities, spatial skills training, and 3D printing experiences to develop students’ spatial and computational thinking skills