Phone: 44 2257 4580 (O)
- 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)
- Experimentation
- Machine learning/ Data Mining
- Applied Statistics
- Algorithmic and Heuristic approaches to problem solving
Applied Statistics and Engineering Journals
- Sudarsanam, N., Chandran, R., & Frey, D. D. (2020), "Conducting non-adaptive experiments in a live setting: a Bayesian approach to determining optimal sample size" ASME Journal of Mechanical Design, 142(3).
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Sudarsanam, Nandan, Balaji Pitchai Kannu, and Daniel D. Frey, (2019) "Optimal replicates for designed experiments under the online framework." Research in Engineering Design 30, no. 3 : 363-379.
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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.
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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.
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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.
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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
- Muralidharan, V., Sudarsanam, N., & Ravindran, B. (2021). Inferring customer occupancy status in for-hire vehicles using PU Learning. In 8th ACM IKDD CODS and 26th COMAD (pp. 290-298).
- Sudarsanam, Nandan, Nishanth Kumar, Abhishek Sharma, and Balaraman Ravindran, (2020),"Rate of change analysis for interestingness measures." Knowledge and Information Systems 62, no. 1: 239-258.
- 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.
Other Areas
- Venkatraman, K., Vijayalakshmi, V., Sudarsanam, N., & Manoharan, A. (2020). "Designing Dynamic Interventions to Improve Adherence in Pediatric Long-term Treatment–The Role of Perceived Value of the Physician by Primary Caregivers". Health Communication, 1-16.
- 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.
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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.
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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.
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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