Dr. Josephine M. Mitchell Scholarship

University of Alberta • 2018

Pundit RD Sharma Memorial Graduate Award

University of Alberta • 2017

University of Alberta Master's Recruitment Scholarship

University of Alberta • 2017

Awarded to 20-odd masters students in the entire Faculty of Graduate Studies and Research.

Summer Research Program Fellowship

Indian Academy of Sciences • Summer 2016

Visiting Student Research Program Fellowship

TIFR Mumbai and TIFR-CAM Bangalore • Summer 2016

RMO Awardee

Regional Mathematics Olympiad • 2013

I cracked the Regional Mathematics Olympiad, Bengal Zone 2013.

KVPY SX Awardee

Kishore Vaigyanik Protsahan Yojana • 2013

I received the prestigious KVPY scholarship awarded by the Department of Science and Technology and the Indian Institute of Science Bangalore.

Infomatrix Computer Project Competition

Bucharest, Romania • 2006

I Represented India, and was the youngest participant at this event organised by the Ministry of Education, Research and Youth, and Lumina institutii de invatamant, Bucharest.

Marcinkiewicz Strong Law of Large Numbers for Products of Long Range Dependent and Heavy Tailed Linear Processes

Prof. Michael Kouritzin, University of Alberta • May 2018 - Aug 2019

Several big datasets we encounter, especially in network, financial and environmental series, is often not only big but also heavy-tailed and long-range dependent. This combination of big, heavy and long data can often be modeled well by multivariate linear processes but is lethal for most classical statistics. Long memory pretty much rules out any use of strong mixing; heavy tails makes use of moments impossible without techniques like truncation, and the two-sided nature of the processes means we need to worry about the future as well as the past. Recently, Marcinkiewicz strong laws of large numbers (MSLLN) were established for showing consistency and optimal polynomial rates of convergence for this kind of data for parameter estimation, adaptive machine learning, and econometrics. I generalized the MSLLN results of well known autocovariance case to multiple products of Heavy-Tailed and long range dependent data. I have found that a MSLLN for even products of linear processes with symmetric innovations converge at a faster rate than those for odd products.

Extensions of Morphological Operators to Weighted Graphs and applications to Morphological Interpolation

Prof. BS Daya Sagar, ISI Bangalore • Summer 2017

This project was conducted in Systems Science and Informatics Unit (SSIU) of ISI, under the KVPY fellowship. I extended notions of dilations, erosions, opening, closing, and other Morphological operators to vertex and edge weighted graphs, and formulated a notion of medians in these graphs via morphological operators. I also formulated a method of interpolation using successive medians, following ideas from supervisor's previous papers, and simulated on various datasets in Python.

Special Topics in Analysis and Partial Differential Equations

Prof. K Sandeep, TIFR-CAM • Summer 2016

I worked under Prof. K Sandeep for 2 months. It was primarily an advanced comprehensive reading project in areas related to Partial Differential Equations, where I reviewed basic concepts of Functional Analysis, Operator Theory and Geometric Analysis, then studied Frechet and Gateux derivatives. Then, I conducted a comprehensive study of stability theory and distribution theory, with emphasis on distribution solutions of general PDEs from analytic as well as geometric point of view.

A Proof of Strong Law of Large Numbers for Bernoulli random variables, and Fekete's Lemma

Prof. Yogeshwaran Dhandapani, ISI Bangalore • Summer 2015

Long Range Dependence and Heavy Tails in Data

Graduate Colloquium, University of Alberta • Fall 2018

Environmental Data Science Bootcamp

University of Chicago • September 2019

International Conference on Stochastic Partial Differential Equations

University of Alberta • September 2018

Graduate Teaching and Learning Program

University of Alberta • September 2018

PIMS-AMI Workshop on Applied Harmonic Analysis and Statistical Learning

University of Alberta • August 2018

Advanced Instructional School on Representation Theory of Finite Groups

Chennai Mathematical Institute • Summer 2017

MATH 101 (Calculus II)

University of Alberta

MATH 209 (Calculus III)

University of Alberta

STAT 265 (Statistics I)

University of Alberta

STAT 266 (Statistics II)

University of Alberta

STAT 309 (Complex Analysis)

University of Alberta