“*Difficulties in identifying problems have delayed statistics far more than difficulties in solving problems*” — John W. Tukey

*My Research Aim & Scope in one line: “United Statistical Science = LP Nonparametric Harmonic Analysis.” *

**Nonparametric Data Science: **

How can we develop a consistent and unified framework of data analysis (the foundation of data science) that would reveal the *interconnectedness* among different branches of statistics? This question is the driving force behind my research program. I have been developing one such candidate theory that will pave the way for a *progressive* unification of fundamental statistical learning tools. Our theory has given birth to a new and exciting discipline for 21st-century statistics, called “Nonparametric Data Science,” which does not yet have a large literature, and is slowly gaining ground.

“*Assuming that a unified foundation is inevitable, what will it be? I think the general refusal in our field to strive for a unified perspective has been the single biggest impediment to its advancement*” –Jim Berger (2000).

We seek to focus on one important field of statistics at a time with a goal to *simplify, unify and generalize* them using our “Nonparametric Data Science” theory and tools. Under this *new* framework, significant number of statistical problems have been tackled to date, including: statistical spectral analysis of graphs (Mukhopadhyay, 2017d), large-scale mode identification for discovery science (Mukhopadhyay, 2017a), unified multiple testing (Mukhopadhyay, 2016), nonparametric copula dependence modeling (Parzen and Mukhopadhyay, 2013b), non-linear time series modeling (Mukhopadhyay and Parzen, 2017; Mukhopadhyay and Nandi (2017), high-dimensional data modeling (Mukhopadhyay and Wang, 2017c) and nonparametric distributed learning (Bruce et al., 2016; Mukhopadhyay, 2017b). These findings strongly indicate that the theory of “United Statistical Algorithms” may be just around the corner.

Throughout, my goal has been to judiciously balance both the *discipline of statistics* (developing a unified general theory of statistics) and *profession of statistics* (applied data analysis, Interdisciplinary collaboration and consultation) to become a “*whole*” 21st century statistician. I am also designing an educational program called `Nonparametric Data Science’ — a series of well-connected training modules with the goal of broadly preparing students and applied researchers [PDF], which will be disseminated freely online.

**Recent Projects**:

- Mixed Data Science [Details]
- Foundations of Graph Data Science [Details]
- Bayes
*via*Goodness-of-fit [Details] - United Multiple Testing [Details]
- Statistics, Big Data, and Parallelism: Towards A Unified Framework [Details]