|Office:||Alter Hall 335|
|Research Interests:||Nonparametric Data Science and United Statistical Algorithms|
Dr. Mukhopadhyay works in both theoretical and applied side of Statistical data science. The ultimate goal of
his research is to develop a unified Statistical theory for data analysis that can lead to polyefficient and
versatile algorithms. Keeping the end in mind, he has launched a new and exciting discipline–“Nonparametric
Data Science” for progressive unification of fundamental statistical learning tools. Under this new framework,
significant number of statistical problems have been tackled to date, including: statistical spectral graph
analysis, large-scale mode identification for discovery science, unified multiple testing, nonparametric copula
dependence modelling, non-linear time series modelling, high-dimensional k-sample modelling, generalized
empirical Bayes modelling, and nonparametric distributed learning for massive data.
- Mukhopadhyay, S. and Fletcher, D. (2018) Bayesian Modeling via Frequentist Goodness-of-fit. Nature Scientific Reports.
- Bruce, S., Li, Z., Yang, H., and Mukhopadhyay, S. (2018) Nonparametric Distributed Learning Architecture for Big Data: Algorithm and Applications. IEEE Transactions on Big
- Mukhopadhyay, S. (2017) Large-Scale Mode Identification and Data-Driven Sciences. Electronic Journal of Statistics.
- Mukhopadhyay, S. and Nandi, S (2017) LPiTrack: Eye Movement Pattern Recognition Algorithm and Application to Biometric Identification. Machine Learning.
- Mukhopadhyay, S. (2016) Large-Scale Signal Detection: A Unifying View. Biometrics
- Parzen, E. and Mukhopadhyay, S. (2013) United Statistical Algorithms, LP comoment, Copula Density, Nonparametric Modeling. 59th ISI World Statistics Congress (WSC), Hong Kong.
- Lahiri, S.N. and Mukhopadhyay, S. (2012) Penalized Empirical Likelihood method for High Dimension. Annals of Statistics, 5, 2511-2540.
- 2016: Best Paper Award, JSM ASA Section on Nonparametric Statistics
- 2016: Best Paper Award, JSM ASA Section on Statistical Computing
- 2014: Winner IEEE International Biometric Eye Movements Verification and Identification Competition.
- 2011: Best presentation by Google at SIAM International Conference on Data Mining
- 2010: Best Student Paper, Section on Nonparametric Statistics of ASA, JSM, Vancouver, Canada.
Fox School of Business, Temple University
- Stat 8001, Probability & Stat Theory I
- Stat 8002, Probability & Stat Theory II
- Stat 2103, Business Statistics