Sushovan Majhi

Visiting Assistant Professor
George Washington University, D.C.

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I am currently a visiting assistant professor of Data Science at GWU.

Prior to GWU, I was a postdoc researcher and MIDS lecturer at the University of California, Berkeley.

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

Find my CV here.

What’s New

Aug 23, 2023: Presented at the Applied Algebraic Topology Research Network (AATRN) seminar. Watch it here.
Aug 3, 2023: Presented my paper at CCCG.
Aug 1, 2023: Joined George Washington University in D.C. as a visiting assistant professor of data science.
Jun 24, 2023: After two years of waiting, my paper on Gromov–Hausdorff approximation is finally published by Journal of Computational Geometry.
Jun 23, 2023: Arrived in Gainesville, Florida to visit Henry Adams at UFL.
Jun 2, 2023: Two papers got accepted to CCCG!!
May 26, 2023: The preprint of an exciting paper on Latschev's theorem in up on the arxiv.
May 15, 2023: My paper on Vietoris–Rips near a metric graph is now published by Journal of Applied Topology.
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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
  • Pattern and Shape Matching
  • Statistical Finance

To know more, visit my RESEARCH page.

Teaching

My teaching interests span a broad spectrum of fields—including foundations of data science, statistics, machine learning, computer science, topological data analysis. Here are some courses I have taught:

  • Introduction to statistics (undergraduate, Tulane University)
  • Statistics for data science (graduate, UC Berkeley)
  • Topological data analysis (graduate, NIT Sikkim, India)
  • Data mining (graduate, George Washington University)
  • Computer science foundations (graduate, George Washington University)
  • Algorithm design (graduate, George Washington University)

Software and Computing

I am a coding hobbyist. I enjoy solving online coding challenges. Although Java is my favorite programming language, I usually code in JavaScript, Python, and R. Some of them are listed here.

Shape Reconstruction

To complement my research, I implemented my topological reconstruction algorithm for planar metric graphs in this library. The library is written in JavaScript and made available to users as a web-app.

title: ShapeReconstruction
webapp
Github

Invited Talks and Presentations

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 Reveal JS. Since then, I have been using it, customizing it, and relishing it. Although, I prefer to edit the source code for my slides in Quarto and output them in Reveal JS format.

List of my talks and presentations:

  • Aug 1, 2023
    A Taste of Topological Data Analysis (TDA): Reconstruction of Shapes
    ICFAI, Tripura
    Links: [url] 
    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
    Links: [url] 
    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
    Links: [url] 
    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
    Links: [pdf] 
  • Sep 8, 2015
    The Mathematical Mechanic
    Graduate Colloquium, Tulane University
    Links: [pdf] 
  • Sep 1, 2021
    Shape Comparison and Gromov-Hausdorff Distance
    Tulane University
    Links: [url] 
    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
    Graduate Colloquium, Tulane University
    Links: [pdf] 
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