About

Vincenzo Vitelli is a professor in the Department of Physics, the James Franck Institute, and the College at The University of Chicago. His research areas include condensed matter physics, soft matter physics, and quantum information. He has been recognized for his contributions to the scientific community, being named an AAAS fellow in 2026 and a CNRS Ambassador in 2025. Additionally, he is involved in various research groups and institutes such as the Enrico Fermi Institute, Kadanoff Center, Kavli Institute, and the Chicago Quantum Exchange. His work encompasses a broad range of topics within physics, and he is actively engaged in advancing scientific understanding through his research and collaborations.

Research topics

• Physics
• Quantum mechanics
• Classical mechanics
• Biological system
• Chemistry
• Theoretical physics
• Statistical physics
• Computer Science
• Artificial Intelligence
• Biology
• Mathematics
• Chemical physics
• Thermodynamics
• Materials science
• Optoelectronics
• Mathematical analysis
• Biophysics
• Optics
• Nanotechnology
• Mechanics
• Mathematical physics

Selected publications

• Informational blueprints reveal condition-dependent gene regulatory architectures

  arXiv (Cornell University) · 2026-05-18

  preprintOpen accessSenior author

  While coding regions in the genome have a direct interpretation in terms of protein products, significant fractions are non-coding and yet control essential biological functions. Unlike the genetic code, there is no "lookup table" that identifies where regulatory proteins, known as transcription factors (TFs), bind. Here, we extract these binding sites by distilling sequences of nucleotide letters into collective coordinates (hyperletters) representing the binding sites that are active under specific environmental conditions. Going beyond local information footprints between individual bases and expression levels, our $\textit{information blueprint}$ algorithm compresses the global information by optimising filters that simultaneously scan an entire promoter sequence. Inspired by renormalisation-group techniques, we identify TF binding sites as coarse-grained variables combining groups of correlated mutations with the highest collective impact on gene expression. We validate our approach on experimental data for $\textit{E. coli}$ and discover novel regulatory elements illustrating its deployment at scale across growth conditions.

  PublisherDOI

• Nonreciprocal many-body physics

  ArXiv.org · 2026-02-11

  articleOpen accessSenior author

  Reciprocity is a fundamental symmetry present in many natural phenomena and engineered systems. Distinct situations where this symmetry is broken are typically grouped under the umbrella term "nonreciprocity", colloquially defined by: the action of A on B $\neq$ the action of B on A. In this review, we elucidate what nonreciprocity is by providing an introduction to its most salient classes: nonvariational dynamics, violations of Newton's third law, broken detailed balance, nonreciprocal responses and nonreciprocity of arbitrary linear operators. Next, we point out where to find these manifestations of non-reciprocity, from ensembles of particles with field mediated interactions to synthetic neural networks and open quantum systems. Given this breadth of contexts and the lack of an all-encompassing definition, it makes it all the more intriguing that some general conclusions can be gathered, when distinct definitions of nonreciprocity overlap. We explore what these universal consequences are with a special emphasis on collective phenomena that arise in nonreciprocal many-body systems. The topics covered include nonreciprocal phase transitions and non-normal amplification of noise and perturbations. We conclude with some open questions.

  PublisherOA PDF

• Supporting Data for "Measurement-Induced Phase Transitions in Informational Active Matter"

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-07

  datasetOpen accessSenior author

  This repository stores data associated with the publication "Measurement-Induced Phase Transitions in Informational Active Matter" (https://arxiv.org/abs/2302.07402).Figures of that publication were generated with the scripts stored under figures/, which point to other directories containing data.Data-containing directories have associated metadata (stored under directory/data/) as well as the run script that generated the outputs (run.py).Note that raw simulation output files are not included due to size limitations.In some cases, a jupyter notebook (*.ipynb) file is included which collects data in preparation for figure generation.The central code modifications made to the Hoomd-Blue engine are saved in cpp_files/.Note that the scripts contained here are not intended to be runnable after downloading this repository, but are rather included as a record of how data was generated and processed.Clarifications or requests should be directed towards Bryan VanSaders (vansaders@drexel.edu).

  PublisherDOI

• Learning functional groups in complex microbiomes.

  PubMed · 2026-03-03

  article

  From soil to the gut, communities composed of thousands of microbes perform functions such as carbon sequestration and immune system regulation. Here, we introduce a data-driven approach that explains how community function can be traced to just a few groups of microbes or genes. In gut communities, our neural-network based clustering algorithm correctly recovers known functional groups. In the ocean metagenome, it distills ~500 gene modules down to three sparse groups highlighting survival strategies at different depths. In soils, it distills ~4400 bacterial species into two groups that enter a mathematical model of nitrate metabolism. By combining interpretable ML with strain isolation and sequencing experiments, we connect the metabolic specialization of each group to community-wide responses to perturbations. This integrated approach yields simple structure-function maps of microbiomes, allowing the discovery of molecular mechanisms underlying human and environmental health. More broadly, we illustrate how to do function-informed dimensionality reduction in biology.

  Publisher

• Learning functional groups in complex microbiomes

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

  articleOpen access

  ABSTRACT From soil to the gut, communities composed of thousands of microbes perform functions such as carbon sequestration and immune system regulation. Here, we introduce a data-driven approach that explains how community function can be traced to just a few groups of microbes or genes. In gut communities, our neural-network based clustering algorithm correctly recovers known functional groups. In the ocean metagenome, it distills ~500 gene modules down to three sparse groups highlighting survival strategies at different depths. In soils, it distills ~ 4400 bacterial species into two groups that enter a mathematical model of nitrate metabolism. By combining interpretable ML with strain isolation and sequencing experiments, we connect the metabolic specialization of each group to community-wide responses to perturbations. This integrated approach yields simple structure-function maps of microbiomes, allowing the discovery of molecular mechanisms underlying human and environmental health. More broadly, we illustrate how to do function-informed dimensionality reduction in biology. Abstract Figure Graphical Abstract An integrated ML and experimental pipeline to discover functional groups and their dynamics in complex microbiomes and beyond. (a) First, our Soft Clustering Function Informed (SCiFI) algorithm identifies functional groups directly from species abundances data using neural networks. Crucially, the learned functional groups are informed by a chosen community function. (b) Descriptions of the system in terms of the original high-dimensional abundances of individual species are prohibitively complex (left). By contrast, a description in terms of the few functional groups identified by SCiFI leads to a simple structure-function map (right). (c) Next, the identified groups can be directly input as variables into predictive mathematical models for the dynamics of the community. (d) The last step of our pipeline relies on the identified groups comprising only a small number of species. This sparsity enables targeted experiments that interrogate isolated species (e.g. with whole-genome sequencing or phenotyping) shedding light on the mechanistic underpinnings of the structure-function map with potential applications beyond microbiomes, from gene expression to neuronal activity.

  PublisherDOI

• The fourfold way to rupture in active solids

  Nature Materials · 2026-03-27

  articleSenior author
  PublisherDOI

• Supporting Data for "Measurement-Induced Phase Transitions in Informational Active Matter"

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-07

  datasetOpen accessSenior author

  This repository stores data associated with the publication "Measurement-Induced Phase Transitions in Informational Active Matter" (https://arxiv.org/abs/2302.07402).Figures of that publication were generated with the scripts stored under figures/, which point to other directories containing data.Data-containing directories have associated metadata (stored under directory/data/) as well as the run script that generated the outputs (run.py).Note that raw simulation output files are not included due to size limitations.In some cases, a jupyter notebook (*.ipynb) file is included which collects data in preparation for figure generation.The central code modifications made to the Hoomd-Blue engine are saved in cpp_files/.Note that the scripts contained here are not intended to be runnable after downloading this repository, but are rather included as a record of how data was generated and processed.Clarifications or requests should be directed towards Bryan VanSaders (vansaders@drexel.edu).

  PublisherDOI

• Informational blueprints reveal condition-dependent gene regulatory architectures

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-20

  articleOpen accessSenior authorCorresponding

  While coding regions in the genome have a direct interpretation in terms of protein products, significant fractions are non-coding and yet control essential biological functions. Unlike the genetic code, there is no “lookup table” that identifies where regulatory proteins, known as transcription factors (TFs), bind. Here, we extract these binding sites by distilling sequences of nucleotide letters into collective coordinates (hyperletters) representing the binding sites that are active under specific environmental conditions. Going beyond local information footprints between individual bases and expression levels, our information blueprint algorithm compresses the global information by optimising filters that simultaneously scan an entire promoter sequence. Inspired by renormalisation-group techniques, we identify TF binding sites as coarse-grained variables combining groups of correlated mutations with the highest collective impact on gene expression. We validate our approach on experimental data for E. coli and discover novel regulatory elements illustrating its deployment at scale across growth conditions.

  PublisherDOI

• Informational blueprints reveal condition-dependent gene regulatory architectures

  ArXiv.org · 2026-05-18

  articleOpen accessSenior author

  While coding regions in the genome have a direct interpretation in terms of protein products, significant fractions are non-coding and yet control essential biological functions. Unlike the genetic code, there is no "lookup table" that identifies where regulatory proteins, known as transcription factors (TFs), bind. Here, we extract these binding sites by distilling sequences of nucleotide letters into collective coordinates (hyperletters) representing the binding sites that are active under specific environmental conditions. Going beyond local information footprints between individual bases and expression levels, our $\textit{information blueprint}$ algorithm compresses the global information by optimising filters that simultaneously scan an entire promoter sequence. Inspired by renormalisation-group techniques, we identify TF binding sites as coarse-grained variables combining groups of correlated mutations with the highest collective impact on gene expression. We validate our approach on experimental data for $\textit{E. coli}$ and discover novel regulatory elements illustrating its deployment at scale across growth conditions.

  PublisherOA PDF

• Soft Matter: Concepts, Phenomena, and Applications

  Springer Link (Chiba Institute of Technology) · 2026-03-16

  article
  DOI

Recent grants

• Collaborative Research: DMREF: Designer 3D Mesoscale Materials Synthesized in the Self-Assembly Foundry

  NSF · $600k · 2021–2025

Frequent coauthors

• Michel Fruchart

  University of Chicago

  77 shared

• Colin Scheibner

  46 shared

• Nitin Upadhyaya

  University of Chicago

  39 shared

• Anton Souslov

  University of Bath

  39 shared

• Leopoldo R. Gómez

  University of Cundinamarca

  36 shared

• William T. M. Irvine

  Fermi National Accelerator Laboratory

  26 shared

• Jayson Paulose

  University of Oregon

  25 shared

• A. M. Tichler

  Consejo Nacional de Investigaciones Científicas y Técnicas

  23 shared

Labs

• Vincenzo Vitelli's LabPI

  Academic research in Topological Mechanics, Mechanical Metamaterials, and Topological Metamaterials

Awards & honors

• Association for the Advancement of Science fellows in 2026
• CNRS Ambassador
• Harper Dissertation Fellowship
• Suzuki Postdoctoral Fellowship Award