Sushovan Majhi
Assistant Professor of Data Science
The George Washington University
Samson Hall 313, Washington, D.C.
s.majhi@gwu.edu
(+1) 202-994-1235
What is the shape of this thing? Topology's oldest question—and one that statistics has been almost-asking for a century. Posed correctly, it turns out to be the right question for data more often than is generally conceded.
I am a mathematician—Calcutta, Bangalore, New Orleans, Berkeley, now Washington—collecting, with each city, a different lesson in how shape is recovered from noise. The papers make those lessons rigorous: when a Vietoris–Rips complex remembers the manifold it was sampled from, when a Reeb graph can be talked into reconstructing a road network from GPS traces, and on those rare occasions when a Gromov–Hausdorff distance can be talked into being computed at all. The collaborations export the proofs to finance, climate, fluid mechanics, and biology—disciplines where the data is unrepentantly real.
Open to advising the curious and the obstinate. Write.
Dispatches from the Field
From the press, 4 May 2026 — A new preprint, A Closed-Form Adaptive-Landmark Kernel for Certified Point-Cloud and Graph Classification, with Atish Mitra, Žiga Virk, and Pramita Bagchi.
From the press, 15 Mar 2026 — Interpretable Classification of Time Series Using Euler Characteristic Surfaces posted to the arXiv; under review at Nature Scientific Reports.
Newport News, 6 Mar 2025 — Spoke on Topological Stability at the Spring Topology and Dynamics Conference, Christopher Newport University.
Seattle, 9 Jan 2025 — Presented Predicting the Onset and Withdrawal of the Indian Monsoon using Persistent Homology at the Joint Mathematics Meetings.
New Orleans, 17 Nov 2024 — Returned to Tulane to speak on lower bounds for the Gromov–Hausdorff distance.
Tufts, 14 Nov 2024 — Talk at the Fall Workshop on Computational Geometry, on lower bounds for the Gromov–Hausdorff distance.
Mandi & Chennai, 10 Oct 2024 — Lectured on a taste of topological data analysis at IIT Mandi and Vellore Institute of Technology during a fortnight in India.
Athens, 14 Jun 2024 — Presented Demystifying Latschev’s Theorem for Manifold Reconstruction at SoCG 2024.
Bozeman, 28 Mar 2024 — Spoke at Montana State University on Latschev’s theorem and quantitative manifold reconstruction.
AATRN, 23 Aug 2023 — A talk at the Applied Algebraic Topology Research Network seminar on Latschev’s theorem; recording on YouTube.
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Research
Two halves of the mathematical foundations of data science, with topology as the lens. On one side, theorems about when a finite sample remembers the shape it was drawn from. On the other, the disreputable data that wanted to know in the first place. The two halves keep each other honest—and, on bad days, mutually embarrassed.
The theoretical side develops provable methods for shape, graph, and manifold reconstruction. The objects of study are simplicial complexes built from finite samples—Vietoris–Rips, Čech, alpha—and the questions are about when, and how faithfully, such complexes recover the topology and geometry of an unknown ground truth. The tools are inherited from algebraic topology, metric geometry, and computational geometry; all three are considerably older than the data they have lately been asked to analyse.
“The theorems decide when, and how faithfully, a finite sample remembers the manifold it was drawn from.”
The applied side carries the methods into finance, climate, fluid mechanics, and biology—wherever the data is unrepentantly high-dimensional but is suspected, often correctly, of living on something simpler. Recent collaborations have hunted monsoon onsets, the topology of the polar vortex, two-phase flow regimes, and the moods of the stock market.
Seven projects, in summary. Click a card for collaborators, papers, and the long-form excuse for the title.
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The full programme—projects, publications, and ongoing collaborations—lives on the research page.
Recent Papers
The three most recent preprints, listed in the order in which they have most recently surprised their author. The full account—journals, conference proceedings, and the thesis—is on the publications page.
A Closed-Form Adaptive-Landmark Kernel for Certified Point-Cloud and Graph Classification
submitted toFoundations of Computational Mathematics·2026
A Closed-Form Persistence-Landmark Pipeline for Certified Point-Cloud and Graph Classification
submitted toJournal of Machine Learning Research·2026
Detecting Regime Transitions in Dynamical Systems via the Mixup Euler Characteristic Profile
submitted toChaos: An Interdisciplinary Journal of Nonlinear Science·2026
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Students and Mentees
Six students between GWU and NIT Sikkim. Each has been issued a corner of the programme—kernels, descriptors, reconstruction, dynamics—and instructed to send dispatches.
Doctoral students
- Buddha Nath Sharma Ph.D., NIT Sikkim Topological data analysis for time series and dynamical systems.
Master's students
- Alexander D. Silberman, Chinaza Belolisa, Madeline Bumpus M.S. Data Science, GWU Topological methods for recommender systems.
- Sayam Palrecha M.S. Data Science, GWU Topological data analysis for finance.
- James Moukheiber M.S., University of Zurich Topological data analysis for geographic information systems.
Undergraduate students
- Edward Bae B.S. Computer Science, GWU Topological methods for recommender systems.
- Abby Stein B.S. Data Science, GWU Topological methods for petroleum flow analysis.
Past mentees
- Khush Shah, Shikha Kumari M.S. Data Science, GWU Geometric graph reconstruction.
- Anish Rai Ph.D., NIT Sikkim Topological data analysis for finance.
Teaching
Foundations to topology, taught at four institutions across three countries. The students change; the limits do not.
Currently teaching Algorithm Design for Data Science and the Undergraduate Capstone at GWU, Spring 2026. The full record is on the teaching page; the teaching statement carries the philosophy — or, failing that, the alibi.
Service
Editorial and committee work—the unpaid half of the profession, and the half in which the field actually gets organised.
- Undergraduate Advisor GWU Data Science · 2024–current
- AMS Special Session co-organiser JMM Washington, D.C. · 2026 · Seattle 2025 · AMS Fall Sectional Tulane 2025 Sessions on Topological Data Analysis for Non-linear Dynamics, Topological and Geometric Shape Reconstruction, Climate Science at the Interface between TDA and Dynamical Systems, and Applied Topology and Topological Data Analysis.
- Ph.D. thesis reader Tulane 2024 · GWU Mathematics 2025 Will Tran, Distortion and Curvature in the Shape Reconstruction Problem; Peiqi Yang, Stochastic Approximation on Manifolds and Topological Data Analysis.
- Journal & conference reviewer SIMODS · DCG · FoDS · SoCG · WADS · others Reviewed for SIAM Journal on Mathematics of Data Science, Discrete and Computational Geometry, Foundations of Data Science, the Symposium on Computational Geometry, and several others.
The complete ledger—including the SIAM Graduate Student Chapter at Tulane and the data-science webinar series at UC Berkeley—has been quietly preserved in the CV.
Talks
Invited talks, conferences, and seminars—newest first, on the working assumption that the most recent argument is the one most likely still to hold.
Spring Topology and Dynamics, Christopher Newport University, Mar 2025 — Topological Stability
Joint Mathematics Meetings, Seattle, Jan 2025 — Predicting the Onset and Withdrawal of the Indian Monsoon using Persistent Homology
Tulane University, New Orleans, Nov 2024 — Lower Bounding the Gromov–Hausdorff Distance
Fall Workshop on Computation Geometry, Tufts University, Nov 2024 — Lower Bounding the Gromov–Hausdorff Distance
Indian Institute of Technology, Mandi, India, Oct 2024 — A Taste of Topological Data Analysis (TDA): Reconstruction of Shapes
Vellore Institute of Technology, Chennai, India, Oct 2024 — A Taste of Topological Data Analysis (TDA): Reconstruction of Shapes
Symposium on Computational Geometry (SoCG), Athens, Greece, Jun 2024 — Demystifying Latschev's Theorem for Manifold Reconstruction
Montana State University, Mar 2024 — Demystifying Latschev's Theorem for Manifold Reconstruction
Applied Algebraic Topology Research Network (AATRN), Aug 2023 — Demystifying Latschev's Theorem for Manifold Reconstruction
The 34th Canadian Conference on Computational Geometry, Aug 2023 — Graph Move's Distance
Fall Workshop on Computational Geometry, North Carolina State University, Oct 2022 — Similarity Measures for Geometric Graphs
ICFAI, Tripura, Jan 2022 — A Taste of Topological Data Analysis (TDA): Reconstruction of Shapes
Hunter College, New York, Sep 2021 — A Taste of Topological Data Analysis (TDA): Reconstruction of Shapes
Tulane University, Jan 2020 — Shape Comparison and Gromov-Hausdorff Distance
Tulane University, Aug 2019 — Shape Reconstruction
Graduate Colloquium, Tulane University, Dec 2016 — Music, Machine, and Mathematics
Graduate Colloquium, Tulane University, Apr 2016 — Computational Complexity
Graduate Colloquium, Tulane University, Sep 2015 — The Mathematical Mechanic
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Education
Calcutta, Bangalore, New Orleans — fourteen years of mathematics across three institutions, with a Berkeley postdoctoral coda for the slow art of saying it plainly.
- Ph.D. in Mathematics Tulane · New Orleans · 2014–2020 Advised by Carola Wenk. Computational geometry, computational topology, topological data analysis, differential geometry.
- M.S. in Mathematics TIFR · Bangalore · 2009–2012 Differential equations, probability theory, complex and functional analysis, measure theory.
- B.S. (Hons.) in Mathematics Ramakrishna Mission Vidyamandira · Calcutta University · 2006–2009 Real analysis, linear algebra, numerical analysis, statistics, physics.
- Postdoctoral Research Fellow UC Berkeley · School of Information · 2021–2023 Data science research and the MIDS lecture series.