I am born and brought up in India, Asia. I completed my Masters’ degree in Mathematics from the Indian Institute of Technology, Bhubaneswar and then spent one and a half months at the Indian Institute of Technology, Guwahati as a Junior Research Fellow within the Department of Mathematics.
The course structure of my masters’ degree emphasized greatly the scientific computing and optimization techniques. Therefore it allowed me to learn several numerical methods for solving differential equations, as well as fuzzy optimization techniques including genetic algorithms, the pso algorithm, portfolio optimization etc.
Earlier, during my undergraduate studies, I had statistics as my minor subject and it intrigued me a lot. I continued to pursue my interest in this subject and gained some further knowledge on inference theory and regression analysis, which are one of the most important tools to deal with large scale data with uncertainty. The concept and vast real-life application of imprecise probability attracted me the most.
Till now, my research focused on large scale data handling with the help of tensor train decomposition. I have worked on the problems of stochastic modeling, and data analysis within a Bayesian framework. I mostly worked on numerical schemes for random processes such as Monte-Carlo method and stochastic-Galerkin method. During my master dissertation I looked forward to the stochastic collocation method that solely depends on the backbones of MC method and SG method. It helps to form the system with uncertain parameter as a d-dimensional tensor which eventually leads to tensor train decomposition to get rid of the ‘curse of dimensionality’.
Besides the aforementioned topics, I also had the opportunity to learn some basics of aero-dynamics. I spent two months at Tata Institute of Fundamental Research, Center for Applicable Mathematics. Here I worked on the theory of optimal control which lead to the lunar soft landing problem. During this phase I gained some valuable knwoledge on the mathematics behind the space-crafts under the guidance Prof. Mythily Ramaswamy.
Right now I am working on the UTOPIAE ESR9 position under the supervision of Dr. Einbeck and Dr. Troffaes. My task is to develop a methodology, based on imprecise probability, to obtain statistical inference for highly dimensional data with limited structural knowledge. The problem is tackled from a unified Bayesian-frequentist viewpoint with the help of sparse model representation through appropriate priors or penalties. The existing LASSO estimators will be taken as starting point for this research.
Basu, Tathagata, Einbeck, Jochen & Troffaes, Matthias, A sensitivity analysis and error bounds for the adaptive lasso, in Irigoien, I., Lee, D.-J., Martinez-Minaya, J. & Rodriguez-Alvarez, M.X. eds, International Workshop on Statistical Modelling. Bilbao, Universidad del Pais Vasco, 278-281, 2020.
Basu, Tathagata, Troffaes, Matthias C. M. & Einbeck, Jochen, Binary Credal Classification Under Sparsity Constraints, in Lesot, Marie-Jeanne, Vieira, Susana, Reformat, Marek Z., Carvalho, Joao Paulo, Wilbik, Anna, Bouchon-Meunier, Bernadette & Yager, Ronald R. eds, Information Processing and Management of Uncertainty in Knowledge-Based Systems. Lisbon, Springer, 82-95, 2020.
Troffaes, Matthias C. M. & Basu, Tathagata, A Cantelli-type inequality for constructing non-parametric p-boxes based on exchangeability, in Bock, Jasper De Campos, Cassio P. de Cooman, Gert de Quaeghebeur, Erik & Wheeler, Gregory eds, Proceedings of Machine Learning Research 103: ISIPTA’19. Ghent, PMLR, 386-393, 2019.
Basu, Tathagata, Einbeck, Jochen & Troffaes, Matthias C. M., Uncertainty Quantification in Lasso-Type Regularization Problems. In Optimization Under Uncertainty with Applications to Aerospace Engineering. Vasile, M. (editor). Springer Nature, 2020 (in press).
Krpelik, Daniel & Basu, Tathagata, Introduction to Imprecise Probabilities. In Optimization Under Uncertainty with Applications to Aerospace Engineering. Vasile, M. (editor). Springer Nature, 2020 (in press).
Basu, Tathagata, Troffaes, Matthias & Einbeck, Jochen, Bayesian Variable Selection Under Prior Ignorance. In UQOP: International Conference on Uncertainty Quantification Optimisation. 2020 (submitted)