​About Me :
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​I am a researcher who wishes to contribute to Statistical Science and Machine Learning. Currently, I am working at IIIT Bangalore. I have obtained my B.Sc. from Bidhannagar College and M.S. + Ph.D. degree from Indian Statistical Institute, Kolkata. During my Ph.D., I have contributed to statistical machine learning (pattern classification and nonparametric regression) with applications to business analytics, quality control, software defect prediction and macroeconomics.

Motivation :

​I am highly inspired by the ideologies of the Professor PC Mahalanobis, who is considered as the 'Father of Indian Statistics' and the founder of the Indian Statistical Institute & Operational Research Society of India. I fully agree with his thoughts to define "Statistics as a key Technology" and "Statistics as a universal tool of inductive inference". Statistics has its applications ranging from natural science, technology to social sciences and human welfare. I would like to thank (Late) Prof. C.A. Murthy for all the support during initial years of my Ph. D. He taught us Pattern Recognition and Neural Networks at ISI Kolkata. His constant support and encouragement in the early days of Ph. D motivated me to pursue my research works in Statistical Machine Learning. 

Research Area : 

​The field of "Statistics" is constantly challenged by the problems that science and industry bring to its door.​ Vast amounts of data are being generated in many fields, and the statistician's job is to make sense of it all, which includes extraction of important patterns and trends and understand "what the data says". We call this "learning from data" and this can roughly be summarized in the following steps: (a) observe a phenomenon; (b) construct a model for that phenomenon; (c) make predictions using the model. The field of statistics and machine learning are two approaches toward the common goal of learning about a problem from data. 'Statistical Learning' refers to a set of tools for modeling and understanding complex data sets that blends statistics with parallel developments in machine learning

I am broadly interested in Statistics and Machine Learning. My research works involve developing statistical methodologies for "data-driven problems" from various applied disciplines (e.g., Business, Medicine, Biology, Epidemiology, Software, Quality Engineering, Macroeconomics, to name a few). My primary research interest lies in Statistical Learning (both supervised and unsupervised learning) with a particular emphasis on hybrid representation learning, imbalanced learning, and nonparametric learning. My secondary research interests focus on Time Series Forecasting and Statistical Analysis of Networks. I am trying to contribute in both theoretical, methodological and applications aspects on the above-mentioned topics (motivated from applied problems).
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