Mathematician Data Scientist Author

Noah Giansiracusa (PhD in math from Brown University) is an assistant professor of mathematics and data science at Bentley University. Noah's research interests include algebraic geometry (the abstract study of systems of polynomial equations and their solutions), machine learning (especially topological and geometric data analysis), empirical legal studies, phylogenetics, and misinformation.

Noah is the author of How Algorithms Create and Prevent Fake News, about which Paul Romer, Nobel Laureate and former Chief Economist of the World Bank, said "It's a joy to read a book by a mathematician who knows how to write... There is no better guide to the strategies and stakes of this battle for the future." Noah's public writing appears in Barron's, Boston Globe, Wired, Slate, and Fast Company, he's been interviewed in Slate, TechCrunch, Tech Policy Press, and he has been quoted in Washington Post, Financial Times, Forbes, FiveThirtyEight, U.S. News, Agence France-Presse (AFP), and New Delhi TV.

Currently working on my second book: Robin Hood Math: How to Fight Back When the World Treats You Like a Number, with a Foreword by Nobel Prize-winning economist Paul Romer. Planning to auction the proposal fall 2022.


Follow me on Twitter @ProfNoahGian

How Algorithms Create and Prevent Fake News is available from Amazon and the publisher Apress (a division of Springer Nature) and other book sellers.

"It's a joy to read a book by a mathematician who knows how to write, even when it tells the discouraging tale of a business model targeted digital advertising that is hijacking the tech sector and destroying its soul. With no hype, little jargon, and precise explanations, the author describes both the conquests the ad-tech empire made by deploying more powerful algorithms and the preparations rebels are making to fight algorithms with algorithms. There is no better guide to the strategies and stakes of this battle for the future."

—Paul Romer, Nobel Laureate, University Professor in Economics at NYU, and former Chief Economist at the World Bank

"By explaining the flaws and foibles of everything from Google search to QAnon and by providing level-headed evaluations of efforts to fix them Noah Giansiracusa offers the perfect starting point for anyone entering the maze of modern digital media."

—Jonathan Rauch, senior fellow at the Brookings Institution and contributing editor of The Atlantic

"Noah’s book brings the refreshingly impartial, data-driven content one gets from a lucid mathematics professor. The scale of reach enabled by technology means algorithms are the only viable operational approach. Mastering their limitations and moderating the commercial interests they serve is the responsibility of all those who work in data science. The effects of algorithms on the fabric of society could be comparable to carbon emissions on global warming. We have a chance to act early. Highly recommended reading."

—Leda Braga ("the most powerful woman in hedge funds"), founder and CEO of Systematica Investments

"You can read a hundred writers bemoaning the pollution of the infosphere; Noah Giansiracusa is one of the few who dares takes you behind the curtain to see the gears and guts of the fake news machine, and the competing algorithms that aim to counterbalance it."

Jordan Ellenberg, bestselling author of How Not To Be Wrong and Shape and John D. MacArthur Professor of Mathematics, University of Wisconsin

"A wonderful book! Very approachable, very informative, a very important contribution to understanding the interaction of computing and misinformation."

—Grady Booch, Chief Scientist for Software Engineering at IBM Research

"The issues surrounding AI and misinformation are some of the most complicated – and important – we face. Giansiracusa helps us understand and confront them."

Jonathan Zittrain, George Bemis Professor of International Law and Professor of Computer Science, Harvard University

"Imagine Genghis Khan with an AK-47
that's what cutting-edge technology and computer algorithms have put in the hands of the next generation of liars, tyrants, and autocrats. Through the use of social media bots, deep fakes, and computer-assisted writing, fake news is now much more threatening and insidious than the "yellow journalism" of old; through the weaponization of technology, you won't even know that it's happening. Numerous TV pundits decry the assault on truth these days, but how many really understand its deep roots in information technology? To fight back, we not only have to go after the liars, but also the truth-assault weapons they have at their fingertips. Read this book to understand just how scary things have gotten over the last decade, but also how those of us who want to defend truth, facts, and evidence can employ the tools of technology to fight back."

—Lee McIntyre, author of Post-Truth

"Misinformation and deepfakes are the unique social-technological challenges of the era of social media and deep learning that every information consumer should be aware of. The book by Noah Giansiracusa provides a comprehensive yet concise account of this complex multi-facet problem, from its very cause to its impact and potential solutions. The book strikes a superb balance between readability and accuracy in the description of the core technologies."

—Siwei Lyu, SUNY Empire Innovation Professor of Computer Science and Engineering, University at Buffalo.

"AI is ushering in breakthroughs in just about every industry. Yet there is a dark side: fake news. So what can be done? Well, Noah Giansiracusa’s book is the answer. He provides an engaging look at fake news – along with the cutting-edge technologies like GPT-3 and deepfake GANs – and shows the various tools that can fight it. This book should be a priority for anyone looking at AI."

Tom Taulli, author of Artificial Intelligence Basics: A Non-Technical Introduction

Review-essay in the Los Angeles Review of Books by Pulitzer Prize finalist Nicholas Carr on Jonas Bendiksen's "Book of Veles" and the deepfakes chapter of my book.

Review for the Math Association for America (MAA) by Bill Wood

Short review in the Notices of the American Mathematical Society (AMS) by Katelynn Kochalski

Recommended summer reading by the MAA

Upcoming/Recent Speaking Engagements

  • Roundtable discussion on natural language generators at the Helix Center in NYC (Oct 15-16, 2022)

  • Keynote address at conference on Trust and Disinformation at Stuttgart, Germany (Sep 1-2, 2022)

  • "Separating Fact From Fiction" conference at Copper Mountain Community College (Apr 30, 2022)

  • Open Data Science Conference in Boston, tutorial on deepfakes (Apr 19, 2022)

  • Harvard University Math Department seminar on The Mathematics of Misinformation (Apr 6, 2022)

  • Book discussion event with Columbia University's Anya Schiffrin -- video here (Mar 30, 2022)

  • Media literacy curriculum development activities at Endicott College (Mar 25, 2022)

  • Guest appearance in course Technology, Media, & Democracy team taught by Mor Naaman (Cornell Tech), Justin Hendrix (NYU), David Carroll (The New School), Emily Bell (Columbia), Luke DuBois (NYU), Mark Hansen (Columbia), Jeremy Caplan (CUNY) (Feb 12, 2022)

  • San Francisco Bar Association Barristers Panel on Algorithmic Bias (Jan 11, 2022)

  • Open Data Science Conference, Responsible AI track: Workshop on Detecting Deepfakes (Nov 17, 2021)

  • Bentley Alumni association: Fake news: The Math Behind the Myths (Oct 20, 2021)

  • Systematica Investments company off-site, organized by Leda Braga (Oct 2, 2021)

To inquire about booking me, please email:



  • Op-ed in Barron's: "What Was Lost in the Debate about Sentient AI", with Paul Romer

  • Op-ed in Boston Globe: "Facebook Could Make Its Algorithms Truly Work for You"

  • Op-ed in Slate: "The Destabilizing Effects of Even Low-Quality Deepfakes"

  • Op-ed in Slate: "Google Needs to Defund Misinformation"

  • Op-ed in Wired: "Facebook Uses Deceptive Math To Hide Its Hate Speech Problem"

  • Excerpt from my Fake News book published in Fast Company

  • Article in Tech Policy Press: "In the Name of Openness"

  • Bentley Magazine article: "Five Ways to Tell (News) Fact from Fiction

Featured in:

Quoted in:


  • 2022) Started working on my second book: Robin Hood Math: How to Fight Back When the World Treats You Like a Number, with a Foreword by Nobel Prize-winning economist Paul Romer. Planning to auction the proposal fall 2022.

  • 2021) My book "How Algorithms Create and Prevent Fake News" on machine learning and misinformation is now available (see info above).

  • 2021) Paper with collaborators Han-Bom Moon, Alessio Caminata, and Luca Schaffler answering a question of Lior Pachter and David Speyer from 2004 on the tropical geometry of phylogenetic trees has appeared in PNAS.

  • 2020) Won a Bentley University Innovation in Teaching Award for my class on data, algorithms, and fake news.

  • 2020) Signed a literary agent at Ayesha Pande Literacy to help develop popular nonfiction book projects.

  • 2019) Moved to the Boston area to solve our 2-body problem!

  • 2019) Ran the first iteration of Math and Law Day for high school students, including a talk about sentencing by Obama-appointed Federal Judge Restrepo.

  • 2018) Joshua Mundinger (undergraduate mentee, Swat '18) awarded an NSF graduate fellowship to attend U. Chicago for his PhD.

  • 2018) Chul Moon (graduate mentee, UGA '18) completed his statistics PhD in TDA and accepted a tenure-track offer at Southern Methodist University.


Pure Math

Algebraic Geometry

Fibonacci, golden ratio, and vector bundles

Mathematics 9 no. 4 (2021).

Chow quotients of Grassmannians by diagonal subtori

with Xian Wu. Proceedings of the Facets in Algebraic Geometry conference in honor of William Fulton's 80th birthday, to appear (2020).

Equations for point configurations to lie on a rational normal curve

with Alessio Caminata, Han-Bom Moon, and Luca Schaffler. Advances in Mathematics 340 (2018), 653-683.

Modular interpretation of a non-reductive Chow quotient

with Patricio Gallardo. Proceedings of the Edinburgh Mathematical Society 61 no. 2 (2018), 457-477.

A simplicial approach to the effective cone of \bar{M}_{0,n}

with Brent Doran and Dave Jensen. International Mathematics Research Notices no. 2 (2017), 529-565.

Projective linear configurations via non-reductive actions

with Brent Doran. Preprint on arXiv.

The dual complex of \bar{M}_{0,n} via phylogenetics

Archiv der Mathematik 106 no. 6 (2016), 525-529.

Factorization of point configurations, cyclic covers and conformal blocks

with Michele Bolognesi. Journal of the European Mathematical Society 17 (2015), 2453-2471.

On Kapranov's description of \bar{M}_{0,n} as a Chow quotient

with W.D. Gillam. Turkish Journal of Mathematics 38 (2014), 625-648.

GIT compactifications of M_{0,n} and flips

with Dave Jensen and Han-Bom Moon.Advances in Mathematics 248 (2013), 242-278.

Conformal blocks and rational normal curves

Journal of Algebraic Geometry 22 (2013), 773-793.

The cone of type A, level one conformal blocks divisors

with Angela Gibney. Advances in Mathematics 231 (2012), 798-814.

GIT compactifications of M_{0,n} from conics

with Matthew Simpson. International Mathematics Research Notices no. 14 (2011), 3315-3334.

Tropical Geometry/Matroids

Point configurations, phylogenetic trees, and dissimilarity vectors

with Alessio Caminata, Han-Bom Moon, and Luca Schaffler.Proceedings of the National Academy of Sciences (PNAS) 118 n. 12 (2021).

Matroidal representations of groups

with Jacob Manaker. Advances in Mathematics 366 (2020).

A module-theoretic approach to matroids

with Joshua Mundinger and Colin Crowley. Journal of Pure and Applied Algebra 224 no. 2 (2020), 894-916.

The universal tropicalization and the Berkovich analytification

with J.H. Giansiracusa. Kybernetica, special volume on tropical mathematics, to appear.

A Grassmann algebra for matroids

with J.H. Giansiracusa.Manuscripta Mathematica 156 no. 1 (2018), 187-213.

Equations of tropical varieties

with J.H. Giansiracusa.Duke Mathematics Journal 165 no. 18 (2016), 3379-3433.


Experimental study of energy-minimizing point configurations on spheres

group project led by Henry Cohn. Experimental Mathematics 18 no. 3 (2009), 257-283.


Topological Data Analysis/Machine Learning:

Persistent homology machine learning for fingerprint classification

with Bob Giansiracusa and Chul Moon. Proceedings of the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019), Boca Raton, FL, USA, 2019, 1219-1226.

Persistence terrace for topological inference of point cloud data

with Chul Moon and Nicole Lazar. Journal of Computational and Graphical Statistics 27 no. 3 (2018), 576-586.

Math and Law:

Branching on the bench: Quantifying division in the Supreme Court with trees

Constitutional Political Economy (2022),

An evolutionary view of the U.S. Supreme Court

Mathematical and Computational Applications 26 no 2 (2021), 1-31.

Computational geometry and the U.S. Supreme Court

with Cameron Ricciardi. Mathematical Social Sciences 98 (2019), 1-9.

Spatial analysis of U.S. Supreme Court 5-to-4 decisions

with Cameron Ricciardi. Preprint on arXiv.

Geometry in the courtroom

with Cameron Ricciardi. American Mathematical Monthly 125 no. 10 (2018), 867-877.

Teaching the quandary of statistical jurisprudence: a review-essay on the book Math on Trial

Journal of Humanistic Mathematics 6 no. 2 (2016), 207-224.


The mathematics of misinformation

Notices of the American Mathematical Society, to appear.

Trust your instincts when opportunity arises

Notices of the American Mathematical Society 68 no. 3 (2021), 372-375.

From Poland to Petersburg: The Banach-Tarski paradox in Bely's modernist novel

with Anastasia Vasilyeva. Annals of Language and Literature 4 no. 3 (2020), 1-8.

Mathematical symbolism in a Russian literary masterpiece

with Anastasia Vasilyeva. The Mathematical Intelligencer 40 no. 3 (2018), 2-11.

When mathematical reasoning gets murky

The Phoenix (op-ed in Swarthmore student newspaper, response to John Fan).

Finding, and sharing, mathematical beauty in the world

Wisaarkhu Special volume 1 topic 2 (2020).

Policy aware geospatial data

with P. Kishor and O. Seneviratne.Accepted in, but not presented at, ACMGIS 2009. arXiv.CS/1304.5755

Research Overview

Algebraic geometry is rooted in a classical question: how do we solve a system of polynomial equations? From this perspective, it is a non-linear extension of linear algebra, so there is no surprise that it applies to areas as diverse as physics, engineering, biology, and statistics. Throughout the 20th century, however, algebraic geometry was an engine of modernity. The language of abstraction championed by Grothendieck opened the door to what is presently one of the most active areas of algebraic geometry: moduli spaces. A recurring theme in my research is the particular moduli space M_{0,n}, a compact manifold parameterizing configurations of n points on the Riemann sphere. This has been a remarkably fecund testing ground; many phenomena found in more complicated moduli spaces are manifest here in a combinatorial and concrete manner.

Tropical geometry and matroids have become another branch of my research, primarily the development of modern Grothendieck-style algebro-geometric foundations of these subjects. Tropical geometry, viewed as a combinatorial approach to algebraic geometry, was developed mostly in the past fifteen years, yet it has already yielded striking applications in variety of subjects. This has led me to the study of idempotent algebras, and in particular to an idempotent module-theoretic framework for matroids.

Topological Data Analysis (TDA) is a collection of tools and ideas aimed at using computational algebraic topology to quantify the higher-dimensional "shape" of data. Much recent work in the field has been in finding optimal ways to integrate TDA methods with machine learning algorithms, in essence allowing classification and prediction based on geometric, not just statistical, structure in data. My work here has primarily been focused on this TDA-ML interface.

Voting theory offers an interesting combination of pure mathematics and data science. Axiomatic voting preference models lead to precise predicted behavior that can be measured against actual voting data. I am combining computational geometry and phylogenetic tools with empirical legal studies methodology to study Supreme Court judicial voting data in order to gain insight into the divisions and alignments of the Court. I am also interested in exploring more general interactions between mathematics and the legal system, such as Bayesian statistics in evidence weighting.

I am also interested, more as a casual curiosity, in interactions between mathematics and literature, for instance of the kind summarized here.