Dr. Nandan Sudarsanam
Assistant Professor
Office: DoMS 306
Phone: 44 2257 4580 (O)
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.


Academic Background

  • Ph.D., Massachusetts Institute of Technology (MIT), Cambridge, MA, 2008 (Engineering Systems Division)
  • M.S. Oklahoma State University, Stillwater, OK, 2005 (Industrial Engineering & Management)
  • B.E, Shanmugha College of Engineering, Thanjavur, India, 2003 (Mechanical Engineering)

Areas of Interest

  • Experimentation
  • Machine learning/ Data Mining
  • Applied Statistics
  • Algorithmic and Heuristic approaches to problem solvin


Applied Statistics and Engineering Journals

  • Sudarsanam, Nandan, Balaji Pitchai Kannu, and Daniel D. Frey, (2019) "Optimal replicates for designed experiments under the online framework." Research in Engineering Design.1-17. Springer.

  • Sudarsanam, Nandan, and Balaraman Ravindran, (2018) "Using Linear Stochastic Bandits to extend traditional offline Designed Experiments to online settings." Computers & Industrial Engineering 115: 471-485.

  • Sudarsanam, N., and Frey D. D., (2011), “Using Ensemble Techniques to advance Adaptive-One-Factor-at-a-Time Experimentation”, Quality and Reliability Engineering International, Vol. 27, Is 7, pg 947-957.

  • Frey, D. D., and Sudarsanam, N., (2006), “Adaptive One-factor-at-a-time Method for Robust Parameter Design: Comparison with Crossed Arrays via Case Studies”, ASME Journal of Mechanical Design, Vol. 130, Is. 2, pp. 02140-14.

  • Li, X., Sudarsanam, N., and Frey, D. D., (2006), “Regularities in Data from factorial experiments”, Complexity, Vol.11, Is. 5, pp 32-45.


AI / Machine Learning / Data Mining

  • Sudarsanam, N., Kumar, N., Sharma, A., and Ravindran, B. (2019) "Rate of Change Analysis for Interestingness Measures". To appear in Knowledge and Information Systems (KAIS), Springer Verlag.
  • Mukherjee, S., Naveen, K. P., Sudarsanam, N., & Ravindran, B. (2018). Efficient-UCBV: An Almost Optimal Algorithm Using Variance Estimates. In Thirty-Second AAAI Conference on Artificial Intelligence.
  • Philip, D. J., Sudarsanam, N., & Ravindran, B. (2018). Improved Insights on Financial Health through Partially Constrained Hidden Markov Model Clustering on Loan Repayment Data. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 49(3), 98-113.
  • Mukherjee, S., Naveen, K. P., Sudarsanam, N., and Ravindran, B. (2017) "Thresholding Bandits with Augmented UCB". In the Proceedings of the Twenty Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017).
  • Philip, D., Sudarsanam, N., & Ravindran, B. (2018, January). A Partial Parameter HMM Based Clustering on Loan Repayment Data: Insights into Financial Behavior and Intent to Repay. In Proceedings of the 51st Hawaii International Conference on System Sciences.


  • Kumar, A. and Sudarsanam N., 2019, “Automated Kano Model Categorization of Aspects from Online Ratings”, International Conference on Computers and Industrial Engineering, (CIE-48), Auckland, New Zealand. December 2-5.
  • Sudarsanam, N., et al., 2017, "Optimal sample size for A/B tests using cumulative regret", Conference on Reinforcement Learning and Decision Making (RLDM), Ann Arbor, Michigan, June 11-14.

  • Sudarsanam N., and Philip D., 2016, "Quantifying and Predicting Prepayments in the Microfinance Environment", NSE-IFMR Finance Foundation Conference on Household Finance, Mumbai, India, March 14-15.

  • Sudarsanam, N., et al., 2015, "Bootstrapped Linear Bandits", Conference on Reinforcement Learning and Decision Making (RLDM), Alberta, Canada, June 7-10.


  • Rackson Asset Management Llc, New York, NY – 2009-2013: Quantitative Research at a high frequency, algorithmic trading environment
  • Bank of America, Boston, MA – Winter 2008
  • Ford Motor Company, Detroit, MI – Summer 2006

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