About

Björn Sandstede is the Alumni-Alumnae University Professor of Applied Mathematics in the Division of Applied Mathematics at Brown University. His research focuses on applied dynamical systems, data science, and computational and mathematical biology. Before moving to Brown, he held faculty positions at The Ohio State University and the University of Surrey. Sandstede has received numerous awards including an Alfred P Sloan Research Fellowship, the SIAM JD Crawford Prize, a Royal Society Wolfson Research Merit Award, the Elsevier Jack Hale Award, and teaching and mentoring awards from Brown University. He was also selected as a Fellow of the Society for Industrial and Applied Mathematics. At Brown, he has served as Department Chair for nine years and as Director of Brown's Data Science Initiative for two years.

Research topics

• Artificial Intelligence
• Machine Learning
• Data Mining
• Computer Science
• Theoretical computer science
• Programming language
• Algorithm

Selected publications

• Cell type-specific gene regulatory network inference from single cell transcriptomics with ctOTVelo

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-14

  articleOpen access

  Inferring gene regulatory networks (GRNs) from gene expression is a crucial task for understanding functional relationships. Gene expression data (transcriptomics) provide a snapshot of gene activity, encoding information about gene regulatory relationships. However, gene regulation is a dynamic process, modulating across time and with different cell types. Temporal GRN inference methods aim to capture these dynamics by utilizing time-stamped transcriptomics, gene expression data of similar samples captured across discrete timepoints, or pseudotime transcriptomics, computationally ordering cells based on an inferred trajectory. These methods can estimate constant or temporal gene regulatory relationships, but may not capture finer, cell type specific relationships. We propose ctOTVelo, an extension to our previous work to account for cell type specificity during GRN inference. ctOTVelo incorporates cell type labels or proportions when inferring the GRN from single cell transcriptomics data. Our methods achieve state-of-the-art performance in GRN prediction in time-stamped and pseudotime-stamped transcriptomics. Furthermore, ctOTVelo is able to generate cell type specific GRNs, allowing cell type resolution analysis of gene regulatory relationships.

  PublisherDOI

• Localized synchronous patterns in weakly coupled bistable oscillator systems

  Physica D Nonlinear Phenomena · 2025-01-23

  articleSenior author
  PublisherDOI

• Efficient numerical computation of spiral spectra with exponentially weighted preconditioners

  IMA Journal of Numerical Analysis · 2025-11-11

  articleSenior author

  Abstract The stability of nonlinear waves on spatially extended domains is commonly probed by computing the spectrum of the linearization of the underlying partial differential equation about the wave profile. It is known that convective transport, whether driven by the nonlinear pattern itself or an underlying fluid flow, can cause exponential growth of the resolvent of the linearization as a function of the domain length. In particular, sparse eigenvalue algorithms may result in inaccurate and spurious spectra in the convective regime. In this work we focus on spiral waves, which arise in many natural processes and which exhibit convective transport. We prove that exponential weights can serve as effective, inexpensive preconditioners that result in resolvents that are uniformly bounded in the domain size and that stabilize numerical spectral computations. We also show that the optimal exponential rates can be computed reliably from a simpler asymptotic problem posed in one space dimension.

  PublisherDOI

• Branches of localized patterned states

  ArXiv.org · 2025-07-16

  preprintOpen access1st authorCorresponding

  Motivated by theoretical analyses of spatially localized structures with arbitrarily long periodic plateaus, we provide a framework of assumptions that simplifies their analysis and leads to a topological criterion for when localized patterned structures lie on a discrete stack of loops or on a single unbounded branch. The framework proposed here also connects closely with continuation algorithms that are often used to verify the hypotheses that guarantee the emergence of localized patterned states.

  PublisherOA PDFDOI

• Quantitative metrics for trait and identity distributions

  ArXiv.org · 2025-08-11

  preprintOpen accessSenior author

  Understanding the role of demographic diversity in group settings requires effective quantitative metrics. Intersectional feminist theory has highlighted that demographic identities can intersect in complex ways, but most metrics used to study these traits are one-dimensional. In their paper "Diversity, identity, and data" (2025), Topaz et al. introduced two novel metrics that capture multiple aspects of demographic identities among group members: "intersecting diversity" and "shared identity". We present a mathematical framework to provide probabilistic interpretations for both metrics. Using these interpretations, we prove that these two measures are anti-correlated and establish tight bounds on their possible combined values, demonstrating that there is no clear "optimal" point that maximizes both metrics. We apply these metrics in three case studies on Hollywood movies, the television show "Survivor", and a random sample of North American companies in which we explore their bounds and anti-correlation as well as their relationship to group performance in these settings. By formalizing the mathematical structure for these metrics and demonstrating their empirical relevance, we provide a foundation for researchers across the social sciences, mathematics, and related fields to more precisely quantify distributions of intersecting traits within groups and better understand their implications for group dynamics and performance.

  PublisherOA PDFDOI

• Urban contact patterns shape respiratory syncytial virus epidemics with implications for vaccination

  Science Advances · 2025-11-26 · 2 citations

  articleOpen access

  Urban environments may alter the landscape of disease transmission with implications for control. Yet, it is unclear whether urban-rural differences exist in the dynamics of childhood respiratory diseases, given specific mixing patterns in younger age groups. Here, we leverage county-level data on respiratory syncytial virus (RSV) from the United States to reveal an urban-rural gradient in both the intensity and age structure of the RSV epidemic, where urban locations experience more prolonged epidemics with higher burden in infants (under 1 year of age). We develop a mechanistic epidemiological model to show that these differences can be explained by daycare utilization rates in children under 5. Using our model to consider control measures, we find that expanding seasonal immunization access in urban and rural areas may limit the risk of off season RSV epidemics.

  PublisherDOI

• Most Probable Escape Paths in Perturbed Gradient Systems

  SIAM Journal on Applied Dynamical Systems · 2025-05-05 · 1 citations

  articleSenior author
  PublisherDOI

• SCOT+: a comprehensive software suite for single-cell alignment using optimal transport

  Bioinformatics Advances · 2025-12-03

  articleOpen access

  Abstract Summary New advances in single-cell multi-omics experiments have allowed biologists to examine how various biological factors regulate processes in concert on the cellular level. However, measuring multiple cellular features for a single cell can be quite resource-intensive or impossible with the current technology. By using optimal transport (OT) to align cells and features across disparate datasets produced by separate assays, Single Cell alignment using Optimal Transport+ (SCOT+), our unsupervised single-cell alignment software suite, allows biologists to align their data without the need for any correspondence. SCOT+ implements a generic optimal transport solution that can be reduced to multiple different previously studied OT optimization procedures including SCOT, SCOTv2, SCOOTR, and AGW for single cell, each of which provides state-of-the-art single-cell alignment performance. Outside of giving a unified framework to interact with prior formulations, the generality of SCOT+ optimization naturally gives rise to a new OT loss, Unbalanced Augmented Gromov-Wasserstein (UAGW), and a corresponding optimizer. With our user-friendly website and tutorials, this new package will help improve biological analyses by allowing for more accurate downstream analyses on multi-omics single-cell measurements. Availability and implementation Our algorithm is implemented in Pytorch and available on PyPI and GitHub (https://github.com/scotplus/scotplus). Additionally, we have many tutorials available in a separate GitHub repository (https://github.com/scotplus/book_source) and on our website (https://scotplus.github.io/).

  PublisherOA PDFDOI

• SCOT+: A Comprehensive Software Suite for Single-Cell alignment Using Optimal Transport

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-05-27 · 1 citations

  preprintOpen access

  Summary: New advances in single-cell multi-omics experiments have allowed biologists to examine how various biological factors regulate processes in concert on the cellular level. However, measuring multiple cellular features for a single cell can be quite resource-intensive or impossible with the current technology. By using optimal transport (OT) to align cells and features across disparate datasets produced by separate assays, Single Cell alignment using Optimal Transport+ (SCOT+), our unsupervised single-cell alignment software suite, allows biologists to align their data without the need for any correspondence. SCOT+ has a generic optimal transport solution that can be reduced to multiple different OT optimization procedures, each of which provide state-of-the-art single-cell alignment performance. With our user-friendly website and tutorials, this new package will help improve biological analyses by allowing for more accurate downstream analyses on multi-omics single-cell measurements. Implementation and Availability: Our algorithm is implemented in Pytorch and available on PyPI and GitHub (https://github.com/scotplus/scotplus). Additionally, we have many tutorials available in a separate GitHub repository (https://github.com/scotplus/book_source) and on our website (https://scotplus.github.io/).

  PublisherOA PDFDOI

• Data-Driven Continuation of Patterns and Their Bifurcations

  SIAM Journal on Applied Dynamical Systems · 2025-05-23

  articleSenior author
  PublisherDOI

Recent grants

• Dynamics near coherent structures

  NSF · $600k · 2009–2014

• Nonlinear stability of patterns

  NSF · $342k · 2014–2018

• RTG: Integrating Dynamics and Stochastics (IDyaS)

  NSF · $2.1M · 2012–2019

• Foundations of Model Driven Discovery from Massive Data

  NSF · $1.5M · 2017–2022

• Spiral Waves and Target Patterns

  NSF · $350k · 2021–2024

Frequent coauthors

• Arnd Scheel

  University of Minnesota

  44 shared

• Katherine M. Kinnaird

  31 shared

• Alexandria Volkening

  Purdue University West Lafayette

  31 shared

• Ruth Wertz

  Valparaiso University

  30 shared

• Karl Schmitt

  Trinity Christian College

  30 shared

• Linda L. Clark

  25 shared

• Melissa McGuirl

  Brown University

  24 shared

• Rebecca Santorella

  23 shared

Education

• Doctor of Philosophy, Fachbereich Mathematik

  Universität Stuttgart

  1993

• Master of Science, Institut für Mathematik

  Universität Heidelberg

  1990

Awards & honors

• Alfred P Sloan Research Fellowship
• SIAM JD Crawford Prize
• Royal Society Wolfson Research Merit Award
• Elsevier Jack Hale Award
• Fellow of the Society for Industrial and Applied Mathematics