PhD Candidate, Tulane University
I am a math PhD student at Tulane University, USA. I am also a freelance software developer. I enjoy solving online coding challenges. In my spare time, I read books and play classical guitar. Here is my RÉSUMÉ.
– Topological Data Analysis
– Computational Topology
– Applied Algebraic Topology
– Big Data Analysis
– Computational Geometry
My research primarily revolves around the interface of mathematics and computer science. More specifically, my research is motivated by theoretical problems arising in topological data analysis (TDA), computational and applied algebraic topology, and computational geometry. I am also interested to solve real-life problems using tools from algebraic topology and geometry. My research interest also extends to applying TDA to other fields of science and developing computational libraries and software.
Download my ACADEMIC CV and RESEARCH STATEMENT here.
For my PhD thesis, I am working under the supervision of Prof. Carola Wenk at Tulane University and Brittany Terese Fasy at Montana State University.
The following is a list of my research groups/topics that I am currently involved in.
List of my publications:
Download my TEACHING STATEMENT here.
List of some of the courses I taught:
The course covered sampling methods, probability theory, random variables, sampling distribution, confidence intervals, hypothesis testing, and linear regression.
I had been a big fan of
quite some time. Who wouldn't be when it comes to presenting slides full of
math symbols? Although the math looked fancy and the audience was happy,
the $\LaTeX$-based framework had also disappointed me quite
often. I found the framework too restrictive to customize; my slides looked
exactly like others'!
Features, that were lacking in Beamer during the time I broke up with it, were shining in RevealJS. Since then, I have been using it, customizing it, and relishing it.
List of my talks and presentations:
The Gromov-Hausdorff distance between any two metric spaces was first introduced by M. Gromov in the context of Riemannian manifolds. This distance measure has recently received an increasing attention from researchers in the field of topological data analysis. In applications, shapes are modeled as abstract metric spaces, and the Gromov-Hausdorff distance has been shown to provide a robust and natural framework for shape comparison. In this talk, we will introduce the notion and address the difficulties in computing the distance between two Euclidean point-clouds. In the light of our recent findings, we will also describe an O(n log n)-time approximation algorithm for Gromov-Hausdorff distance on the real line with an approximation factor of $5/4$.LINK
Most of the modern technologies at our service rely on 'shapes' in some way or other. Be it the Google Maps showing you the fastest route to your destination eluding a crash or the 3D printer on your desk creating an exact replica of a relic; shapes are being repeatedly sampled, reconstructed, and compared by intelligent machines. With the advent of modern sampling technologies, shape reconstruction and comparison techniques have matured profoundly over the last decade.LINK