## Summary

I am a Chapman Fellow in Mathematics in the Statistics Section of the Department of Mathematics at Imperial College London.

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/.