IIT Madras

Department of
Management Studies
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Dr. Nandan Sudarsanam
Faculty Profile

Dr. Nandan Sudarsanam

Professor
Ph.D. (Engineering Systems) — MIT, Cambridge, MA (2008)
M.S. (Industrial Eng. & Mgmt.) — Oklahoma State University (2005)
B.E. (Mechanical Engineering) — Shanmugha College of Engineering (2003)
nandan@iitm.ac.in +91-44-2257 4580 Office: DoMS 306

Areas of Interest

  • Experimentation
  • Machine Learning & Data Mining
  • Applied Statistics
  • Algorithmic & Heuristic Approaches
  • Specializing in heuristic approaches to problem-solving, including bandit algorithms and efficient policy search.

Industry Experience

  • Quantitative Research, Rackson Asset Management Llc, New York (High Frequency Trading) (2009 – 2013).
  • Intern / Researcher, Bank of America, Boston, MA (Winter 2008).
  • Intern / Researcher, Ford Motor Company, Detroit, MI (Summer 2006).

Publications

  • Paramasivan, K., Raj, B., Sudarasanam, N., & Subburaj, R. (2023). Prolonged school closure during the pandemic time in successive waves of COVID-19 - vulnerability of children to sexual abuses. Heliyon, 9(7), e17865.
  • Rammohan, S., Marathe, R.R. & Sudarsanam, N. (2023). Recent advancements in revenue management of taxi services: a systematic review. Management Review Quarterly. DOI Link ↗
  • Sudarsanam, N., Kumar, A., & Frey, D. D. (2022). Quantifying the maximum possible improvement in 2^k experiments. Research in Engineering Design, 33(4), 367-384.
  • Narayanaswami, S. K., Sudarsanam, N., & Ravindran, B. (2022). An Active Learning Framework for Efficient Robust Policy Search. 9th ACM IKDD CODS and 27th COMAD.
  • Paramasivan, K., ... & Sudarsanam, N. (2022). Relationship between mobility and road traffic injuries during COVID-19 pandemic—The role of attendant factors. PLoS One. DOI Link ↗
  • Sudarsanam, N., Chandran, R., & Frey, D. D. (2020). Conducting non-adaptive experiments in a live setting: a Bayesian approach. ASME Journal of Mechanical Design.
  • Sudarsanam, N., and Ravindran, B. (2018). Using Linear Stochastic Bandits to extend traditional offline Designed Experiments to online settings. Computers & Industrial Engineering.

Selected Presentations

  • "Automated Kano Model Categorization of Aspects from Online Ratings", International Conference on Computers and Industrial Engineering (CIE-48), Auckland, 2019.
  • "Optimal sample size for A/B tests using cumulative regret", Conference on Reinforcement Learning and Decision Making (RLDM), Ann Arbor, Michigan, 2017.
  • "Bootstrapped Linear Bandits", Conference on Reinforcement Learning and Decision Making (RLDM), Alberta, Canada, 2015.