My research involves integration of statistics, operations research and information theory to develop advanced statistical models and statistical monitoring methods, with a focus on advancing the science of health care delivery.
- Das, D., and Zhou, S. “Detecting Entropy Increase in Categorical Data Using Maximum Entropy Distribution Approximations”, accepted in IISE Transactions ( Selected as feature article in IIE Magazine) (previously named IIE Transactions).
- Sir, M. Y., Nestler, D., , Hellmich, T., Das, D., Laughlin, M., Dohlman, M., Pasupathy, K.S., “Data-driven Optimization for Multi-disciplinary Staffing in Mayo Clinic Improves Patient Experience”, accepted, Interfaces.
- Das, D., Zhou, S., Chen, Y., and Horst, J. (2015) “Approximate Likelihood Inference for Monitoring Multivariate Count Data” International Journal of Production Research, vol. 54(21), pp. 6579-6593.
- Das, D., Chen, Y., Zhou, S. and Sievenppiper, C. (2016) “Monitoring multiple binary data stream using a hierarchical model structure” 2015. Quality Reliability Engineering International, vol. 32(4), pp. 1307-1319.
- Das, D. and Zhou, S. (2015) “Statistical Process Monitoring Based on Maximum Entropy Density Approximation and Level Set Principle.” IIE Transactions, vol 47(3), pp. 215-229. ( Selected as feature article in IIE Magazine)
- Das, D., Zhou, S. and Lee, J. D.,(2012)
Differentiating Alcohol-Induced Driving Behavior Using Steering Wheel Signals.” Intelligent Transportation Systems, IEEE Transactions on
} vol. 13 (3), pp. 1355-1368.
- Prasoon, R., Das, D., Tiwari, M. K. and Wang, L., (2011) “An algorithm portfolio approach to re-configurable set-up planning”, International Journal of Computer Integrated Manufacturing, vol. 24 (8), pp. 756-768.