About Me

I am an Assistant Professor in the Department of Operations Research at The Naval Postgraduate School. My current research focuses on the intersection of machine learning and operations research, with an additional emphasis on data-driven decision making. Specific topics of interest include robust optimization and optimization under uncertainty, risk management, and network optimization. Additionally, I have interests in machine learning and artificial intelligence research ranging from the modern (e.g. deep learning) to the fundamental (i.e. defining proper performance metrics).

Prior to my appointment at The Naval Postgraduate School, I received my PhD and M.S. in Industrial and Systems Engineering from the University of Florida and my B.S. in Mathematics from Emory University.

Publications

PUBLICATIONS                                                                

  1. M Norton, A Mafusalov, S Uryasev, “Soft Margin Support Vector Classification as Buffered Probability Minimization”, PDF (Journal of Machine Learning Research, 18, pp(1-43), (2017)
  2. M Norton, A Takeda, A Mafusalov, “Optimistic Robust Optimization With Applications to Machine Learning”, arXiv Preprint PDF (2017, Submitted, )
  3. M Norton, S Uryasev, “Error Control and Neyman-Pearson Classification with Buffered Probability and Support Vectors”, PDF (2017, Working Paper)
  4. M Norton, S Uryasev, “Buffered Probability Minimization: A New Interpretation of Robust and Regularized Support Vector Machines”, PDF (2017, Submitted )
  5. E Blair, M Norton, “The Buffered Maximal Covering Location Problem”, (2017, In Preparation)
  6. M Norton, A Mafusalov, S Uryasev, “Cardinality of Upper Average and Application to Network Optimization”, PDF (2018, Accepted, SIAM Journal on Optimization )
  7. M Norton, S Uryasev, “Maximizing AUC and Buffered AUC in Binary Classification”, PDF (2016, Submitted )
  8. W Beyeler, R Glass, P Finley, T Brown, M Norton, M Bauer, J Hobbs, “Modeling Systems of Interacting Specialists, In 8th International Conference on Complex Systems. PDF (2011)
  9. S Conrad, P Finley, W Beyeler, T Brown, R Glass, M Norton, M. Mitchell, “A General Model of Resource Production and Exchange in Systems of Interdependent Specialists”, PDF, Sandia National Laboratories Technical Report No. SAND2011-8887. (2011)

CONFERENCE PRESENTATIONS

  • SIAM Conference on Optimization, Vancouver, Canada, May 15-18, 2017
    • “Risky Robust Optimization and Application to Machine Learning and Support Vector Machines”
  • Invited Seminar at the Institute of Statistical Mathematics, Tokyo, Japan, March 23, 2017
    • “New Approaches to Binary Classification Using Risk Management and Robust Optimization”
  • (Workshop Organizer & Presenter)-Risk Management Approaches in Engineering Applications Workshop, Gainesville, FL, November 17-18, 2016
    • “Robust Buffered Probability Minimization and Application to Support Vector Machines”
  • (Session Chair)-INFORMS Annual Meeting, Nashville, TN, November 13-16, 2016
    • “Buffered Probability of Exceedance and Applications to Machine Learning”
  • INFORMS Data Mining Workshop, Nashville, TN, November 12, 2016
    • “Buffered Probability of Exceedance: A New Interpretation of Robust and Regularized Support Vector Machines”
  • International Conference on Continuous Optimization, Tokyo, Japan, August 6-11, 2016
    • “Buffered Probability of Exceedance, Support Vector Machines, and Robust Optimization”
  • SIAM Conference on Uncertainty Quantification, Lausanne, Switzerland, April 5-8, 2016
    • “Buffered Probability of Exceedance, Support Vector Machines, and Robust Optimization”
  • (Session Chair)-INFORMS Optimization Society Conference, Princeton, NJ, March 17-19, 2016
    • “Maximization of Buffered AUC and AUC in Binary Classification”
  • (Workshop Organizer & Presenter)-Risk Management Approaches in Engineering Applications Workshop, Gainesville, FL, Nov 9-10, 2015
    • “Soft Margin Support Vector Classification as Buffered Probability Minimization”
  • INFORMS Annual Meeting, Philadelphia, PA, Nov. 1-4, 2015
    • “Soft Margin Support Vector Classification as Buffered Probability Minimization”
  • ISMP International Symposium on Optimization, Pittsburg, PA, July 12-17, 2015
    • “Buffered Probability of Exceedance and Application to Machine Learning”
  • SIAM Conference on Computational Science & Engineering, Salt Lake City, UT, March 14-18, 2015
    • “Maximization of AUC and Buffered AUC in Binary Classification”
  • World Congress on Global Optimization, Gainesville, FL, Feb 22-25, 2015
    • “Maximization of AUC and Buffered AUC, and the bAUC Efficiency Frontier”
  • Risk Management Approaches in Engineering Applications Workshop, Gainesville, FL, Nov 14-17, 2014

Teaching and Useful Resources

Useful Resources

Machine Learning Textbooks

  • Sutton, Richard S., and Andrew G. Barto. “Reinforcement learning: An introduction.” (2011).
  • Bishop, Christopher M. Pattern recognition and machine learning. Springer, 2006.
  • Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
  • Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.

Big Data Textbook

  • Leskovec, Jure, Anand Rajaraman, and Jeffrey David Ullman. Mining of massive datasets. Cambridge university press, 2014.

Optimization Textbooks

  • Boyd, Stephen, and Lieven Vandenberghe. Convex optimization. Cambridge university press, 2004.
  • Bertsimas, Dimitris, and John N. Tsitsiklis. Introduction to linear optimization. Vol. 6. Belmont, MA: Athena Scientific, 1997

 

Online Courses

Machine Learning

Coding

I am an Assistant Professor in the Department of Operations Research at The Naval Postgraduate School. My current research focuses on the intersection of machine learning and operations research, with an additional emphasis on data-driven decision making. Specific topics of interest include robust optimization and optimization under uncertainty, risk management, and network optimization. Additionally, I have interests in machine learning and artificial intelligence research ranging from the modern (e.g. deep learning) to the fundamental (i.e. defining proper performance metrics).

Prior to my appointment at The Naval Postgraduate School, I received my PhD and M.S. in Industrial and Systems Engineering from the University of Florida and my B.S. in Mathematics from Emory University.