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M.S.

University of California, Berkeley

Mechanical Engineering

Ph.D.

University of California, Berkeley

Mechanical Engineering

• Biomechanics
• Machine Learning
• Scientific Computing
• Biofluid Dynamics
• Finite Element Method
• Deep Learning for Image Analysis

Doctoral Transfer

Boston University

Biomedical Engineering (Ph.D. coursework)

Visiting Scholar

Stanford University

International Honors Program

B.S.

University of Iceland

Mechanical Engineering

Research Intern

Kerecis

Worked on material property analysis for medical biomaterials.

• Collaborated on R&D for regenerative medical materials.

Graduate Researcher

University of California, Berkeley — Mechanical Engineering Department

Developed deep learning–based methods for automatic vascular model construction and trajectory-oriented vessel tracking in medical imaging. Integrated physics-based modeling and machine learning to enable patient-specific cardiovascular simulations.

• Developed SeqSeg: a deep learning framework for coronary artery segmentation
• Created MeshGrow: an automated pipeline for unified cardiac and vascular mesh generation
• Contributed to SimVascular open-source cardiovascular modeling software
• Mentored undergraduate and MEng teams in medical image analysis projects

Undergraduate Researcher

University of Iceland — Department of Medicine

Developed computational models for new lung experimental technologies.

Automatic Vascular Model Construction from Medical Imaging Using Deep Learning

University of California, Berkeley

Ph.D. Dissertation. Explores deep learning for automated vascular model construction.

Integrated Framework for Unified Cardiac and Vascular Mesh Construction from Medical Images

Functional Imaging and Modeling of the Heart (FIMH 2025)

N. Sveinsson Cepero, A. Narayanan, and S. C. Shadden. Introduces MeshGrow, a unified framework for simulation-ready cardiovascular mesh generation.

SeqSeg: Learning Local Segments for Automatic Vascular Model Construction

Annals of Biomedical Engineering

N. Sveinsson Cepero and S. C. Shadden. Presents a deep learning method for segmenting and reconstructing vascular models from medical imaging data.

Reviewer for MICCAI Conference (Medical Image Computing and Computer Assisted Intervention)

Reviewed scientific papers in medical image analysis and AI for healthcare.

Member, Departmental Panels, UC Berkeley Mechanical Engineering

Contributed to departmental curriculum and research initiative discussions.

Board Member and Vice President, SINE - Association of Icelandic Students Abroad

Organized initiatives for Icelandic students abroad and represented the community in international education forums.

Board Member, School of Engineering and Natural Sciences, University of Iceland

Amnesty International Youth Council, Iceland

UC Berkeley Engineering Ph.D. Commencement Speaker

UC Berkeley College of Engineering

UC Berkeley Mechanical Engineering Department Scholarship

University of California, Berkeley

Landsbankinn Graduate Scholarship

Landsbankinn, Iceland

UC Berkeley Summer Fellowship

UC Berkeley Mechanical Engineering Department

CVID Student Presenter Award

Cardiovascular Implant Durability Conference

Leif Eiriksson Fellowship

Leif Eiriksson Foundation

Baccalaureate Commencement Speaker

University of Iceland

SeqSeg

Deep learning–based framework for automated segmentation of coronary arteries from CT images.

MeshGrow

Framework for unified cardiac and vascular mesh construction from medical images.

SimVascular

Open-source cardiovascular modeling software. Contributed features and documentation.

MIROS

Toolkit linking patient-specific medical imaging data to reduced-order hemodynamic simulations.

Vascular Model Repository

Online platform for sharing and visualizing vascular geometries and simulation data.