# Sushovan Majhi

Postdoc Research Fellow, UC Berkeley

I am currently a postdoc reseacher and MIDS lecturer at the School of Information, University of California, Berkeley.

Welcome to my homepage. The site showcases my research and software projects, ocassional tutorials, sporadic rants, and more.

As the odds of landing on my site at random are one in 1.9 billion (really!), you are probably looking for something, and Google just got too generous. Nonetheless, feel free to browse, find mistakes, and leave your valuable comments.

Find my CV here.

## Education

• Doctor of Philosophy in Mathematics
Tulane University, New Orleans, USA
2020
• Master of Science in Mathematics
Tata Institute of Fundamental Research, Bangalore, India
2012
• Bachelor of Science (Hons. in Mathematics)
Ramakrishna Mission Vidyamandira, Calcutta University, India
2009

## Research

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 topology, and computational geometry. I am also interested in solving real-life problems using tools from algebraic topology and geometry. My research interest also extends to applying TDA to other fields of science, like statistical finance and dynamical systems.

Research Interests:

• Topological Data Analysis
• Computational Topology
• Applied Algebraic Topology
• Computational Geometry
• Statistical Finance

## Publication

#### PhD Thesis

• Title: Topological Methods in Shape Reconstruction and Comparison
2020

#### Preprints

• 2022
Distance Measures for Geometric Graphs
• 2022
Vietoris–Rips Complexes of Metric Spaces Near a Metric Graph.
Submitted to: Journal of Applied and Computational Topology

#### Journals

• 2022
On the Reconstruction of Geodesic Subspaces of $\pmb{\mathbb R^n}$.. International Journal of Computational Geometry and Applications
With: Brittany Fasy, Rafal Komendaczyk, and Carola Wenk
• 2022
Approximating Gromov-Hausdorff Distance in Euclidean Space.. Computational Geometry: Theory and Applications (accepted)
With: Jeffrey Vitter and Carola Wenk
• 2021
A Sentiment-Based Modeling and Analysis of Stock Price During the COVID-19: U- and Swoosh-Shaped Recovery.. Physica A: Statistical Mechanics and Its Applications 592: 126810, 2021 [https://doi.org/10.1016/j.physa.2021.126810.]
With: Anish Rai, AjitMahata, Md Nurujjaman, and Kanish Debnath

#### Peer-reviewd Conferences and Workshops

• 2018
Threshold-based graph reconstruction using discrete Morse theory. Fall Workshop on Computational Geometry, New York, NY, November 2018
With: Brittany Terese Fasy and Carola Wenk
• 2017
Topological and Geometric Reconstruction of Metric Graphs in $\mathbb R^n$. Fall Workshop on Computational Geometry*, New York, NY, October 2017
With: Brittany Terese Fasy, Rafal Komendaczyk, and Carola Wenk

## Talks and Presentation

I had been a big fan of Beamer for 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:

• Aug 1, 2023
A Taste of Topological Data Analysis (TDA): Reconstruction of Shapes
ICFAI, Tripura
Abstract: Topological data analysis (TDA) is a growing field of study that helps address data analysis questions. TDA is deemed a better alternative to traditional statistical approaches when the data inherit a topological and geometric structure. Most of the modern technologies at our service rely on 'geometric shapes' in some way or the other. Be it the Google Maps showing you the fastest route to your destination 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. In this talk, we will catch a glimpse of how some of the famous topological concepts---like persistent homology, Vietoris-Rips and Cech complexes, Nerve Lemma, etc---lend themselves well to the reconstruction of shapes from a noisy sample.
• Sep 30, 2021
A Taste of Topological Data Analysis (TDA): Reconstruction of Shapes
Hunter College, New York
Abstract: Topological data analysis (TDA) is a growing field of study that helps address data analysis questions. TDA is deemed a better alternative to traditional statistical approaches when the data inherit a topological and geometric structure. Most of the modern technologies at our service rely on 'geometric shapes' in some way or the other. Be it the Google Maps showing you the fastest route to your destination 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. In this talk, we will catch a glimpse of how some of the famous topological concepts---like persistent homology, Vietoris-Rips and Cech complexes, Nerve Lemma, etc---lend themselves well to the reconstruction of shapes from a noisy sample.
• Aug 8, 2019
Shape Reconstruction
Tulane University
Abstract: 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.
• Dec 3, 2016
Music, Machine, and Mathematics
Tulane University
• Sep 8, 2015
The Mathematical Mechanic
• Sep 1, 2021
Shape Comparison and Gromov-Hausdorff Distance
Tulane University
Abstract: 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 $5/4$.
• Apr 16, 2016
Computational Complexity