My research mainly focuses on developing theory and methods in the fields of statistics and machine learning, to understand complex high-dimensional data. Specifically, it includes developing new hypothesis testing, clustering and density estimation algorithms and applying them to science and technology.
I have on-going research projects in clustering and applications of statistical machine learning methodology to high-energy physics. My work on clustering proposes a novel clustering algorithm that comes equipped with a significance guarantee. Meanwhile, my work in high-energy physics proposes tests to detect the presence of new physics particles in particle physics datasets. Recently, I have been researching different anomaly detection algorithms and different ways to interpret, understand and characterize the performance of a classification algorithm.
Previously, I have received a Ph.D. in Statistics from the Department of Statistics and Data Science at Carnegie Mellon University, under the supervision of Professor Larry Wasserman. I have also obtained a Masters in Machine Learning from the Machine Learning Department at CMU and a Bachelors and Masters in Statistics from the Indian Statistical Institute, Kolkata.
For more details about my research please see: http://wwwf.imperial.ac.uk/~pchakrav/.