Department / Division
- Assistant Professor of Statistics
- Allen Hall 422
Research interests: Turing’s Formula and its Statistical Implications, Nonparametric Estimation of Entropy and Mutual Information, Nonparametric Estimation of Tail Probability, Nonparametric Estimation of Biodiversity Indices, Data Analytics from Entropic Perspective.
Author Profile on Google Scholar
Modern data science faces at least two fundamental issues: 1) High Dimensionality and 2) Discrete and Non-ordinal Nature. The generality of the data space suggests that possible data values may not have a natural order among themselves. For example, different gene types in the human genome, different words in the text, and different species in an ecological population. Such issues would force a fundamental transition from the platform of random variables (on the real line) to the platform of random elements (on a general set or an alphabet). On such an alphabet, various familiar and fundamental concepts of Statistics and Probability no longer exist, for example, moments, characteristic functions, correlation, tail. My research interest is to tackle these issues in perspective of Information Theory and characterize information from non-ordinal alphabets.
Degree: Ph.D. in Statistics, 2019, The University of North Carolina at Charlotte