Skip to main content

Welcome to the Golovin reseach group

I am an Assistant Professor in the Department of Mathematics at Bar-Ilan University. In the years 2021–2025 I was a Phillip Griffiths Assistant Research Professor at the Mathematics department as well as at the Rhodes Interdisciplinary Initiative at Duke University, working with Prof. Ingrid Daubechies. In 2021 I completed my Ph.D. at the Department of Applied Mathematics, School of Mathematical Sciences, Tel Aviv University, under the supervision of Prof. David Levin and Prof. Yoel Shkolnisky.

Learn more about Math in Cultural Heritage (HAVI): here.

Learn more about literacy in the Iron age here 

PublicationGoogle ScholarContact Info: shira.golovin at biu.ac.il

 

Contact Us

Ongoing Projects

With the dissemination of high-dimensional data, we wish to study its underlying geometry and answer long-debated questions in the fields that acquired the data. Although data can originate from various domains (e.g., images, surfaces), a reasonable assumption is that they lie on a manifold. At the heart of these challenges lies a fundamental question: how can we extract meaningful structure from thisdata? Real-world datasets are rarely clean - they are shaped by noise, outliers, and uneven sampling - and each new application introduces fresh scientific questions.

Explore All Ongoing Projects

Recent Publications

Anaya, Alisha ; Ravier, Robert ; Faigenbaum-Golovin, Shira et al. / Registration-based workflow for shape study. In: Anatomical Record. 2026.
Faigenbaum-Golovin, Shira ; Levin, David. / Mind the gap : hole-filling and reconstruction of high-dimensional manifolds from noisy scattered data. In: Sampling Theory, Signal Processing, and Data Analysis. 2025 ; Vol. 23, No. 1.
Faigenbaum-Golovin, Shira ; Kipnis, Alon ; Bühler, Axel et al. / Critical biblical studies via word frequency analysis : Unveiling text authorship. In: PLoS ONE. 2025 ; Vol. 20, No. 6 June.
Faigenbaum-Golovin, Shira ; Levin, David. / Manifold reconstruction and denoising from scattered data in high dimension. In: Journal of Computational and Applied Mathematics. 2023 ; Vol. 421.

Updates

New Paper alert 2

Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis, Gary Choi, Khanh Dao Duc, Shira Faigenbaum-Golovin, Karen Habermann, Emmanuel Hartman, Christoph von Tycowicz, Chi Zhang, Wenjun Zhao, Felix Zhou, arXiv preprint arXiv:2606.17022

New Paper alert

Critical biblical studies via word frequency analysis: Unveiling text authorship. Appeared in PLOS One.
Curious about our recent PLOS One study but short on time to read it? Check out this podcast episode (generated by NotebookLM), where we unpack the method, findings, and their potential significance for biblical scholarship: podcast.

New Project alert

Join us in the project "From Molecules to Masterpieces: AI-Powered Insights into Cultural Heritage", supported by Schmidt Sciences for their generous support provided through the Humanities and Artificial Intelligence Virtual Institute (HAVI).