Postdoctoral Fellow · Oden Institute, UT Austin

Númi Sveinsson Cepero

I build AI and simulation tools that turn medical images into patient-specific models of the heart and blood vessels.

  • Aorta
  • Pulmonary artery
  • Left ventricle
  • Right ventricle
  • Left atrium
  • Right atrium

A MeshGrow model joining the vessels to the heart. Drag to rotate, or show the CT scan it was built from.

I’m a Postdoctoral Fellow at the Center for Computational Medicine at the Oden Institute for Computational Engineering and Sciences at UT Austin, working with Prof. Charley Taylor on computational and AI-driven methods for cardiovascular and respiratory modeling and digital twin development.

I completed my Ph.D. with Prof. Shawn Shadden at UC Berkeley, where my research combined biomechanics, machine learning, and computational modeling to better understand cardiovascular health. My current work extends these ideas to the intersection of cardiovascular and respiratory modeling, artificial intelligence, and simulation, in collaboration with the Cardiovascular Biomechanics Computation Lab at Stanford University and the Shadden Lab at UC Berkeley. I also contribute to SimVascular, an open-source platform for patient-specific blood flow simulation, and the Vascular Model Repository, a public resource for sharing high-quality vascular data and models.

I’m passionate about collaborative, translational research and about using engineering and data-driven approaches to address real medical challenges.

Outside the lab, I enjoy exploring history, traveling, gardening, and cooking with friends.

Research interests

Computational medicine, cardiovascular and respiratory modeling, medical image analysis, deep learning, digital twins

Education

Ph.D. Mechanical Engineering · UC Berkeley, 2025

B.S. Mechanical Engineering · University of Iceland, 2019

Aorta and iliac arteries traced automatically through an abdominal CT volume

From images to anatomy

Deep learning methods that construct simulation-ready vascular and cardiac models from CT and MR images, replacing time-consuming manual modeling.

Read how SeqSeg works →
A vascular model simplified step by step into a reduced-order network of vessels and junctions

Simulation in minutes

Automated reduced-order pipelines that go from a medical image to a patient-specific blood flow simulation within minutes, making larger studies practical.

See MIROS on GitHub →
Simulated blood flow streamlines through a patient-specific heart and aorta

Digital twins

Current work at UT Austin extends these methods across the cardiovascular and respiratory systems, with a focus on clinical translation.

Read about the lab →

Selected work

All publications →
MeshGrow model of the heart chambers joined to the aorta and its branches

JRSM Cardiovascular Disease · 2026

MeshGrow: simulation-ready heart and vessel models from medical images

A framework that builds combined cardiac and vascular meshes, ready for patient-specific simulation.

Vascular models produced by SeqSeg

Annals of Biomedical Engineering · 2025

SeqSeg: learning local segments for automatic vascular model construction

A deep learning method that traces vessels step by step, and generalizes to vessels not annotated in its training data.

Open tools

All software →

Speaking

All talks →
  • Gave a keynote talk on “Scalable Automation of Cardiovascular Geometric Modeling for Patient-Specific Simulation” at WCCM-ECCOMAS 2026 in Munich, Germany. Presented in...
  • Oral presentation at WCB 2026 in Vancouver
  • New Paper Published: MeshGrow in JRSM Cardiovascular Disease
  • Abstract “Scalable Automation of Cardiovascular Geometric Modeling for Patient-Specific Simulation” has been accepted for oral presentation at WCCM-ECCOMAS 2026 in Munich, Germany...

Latest writing

All writing →

Let's connect

I'm glad to hear from researchers, clinicians and students working on computational medicine, whether about a collaboration, a talk, or a question about the tools.

Get in touch