Curriculum vitae

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Computational scientist with 7+ years of expertise developing predictive, patient-specific models in biomedical engineering, vascular biomechanics, and device-adjacent workflows. I integrate mechanistic simulation — FEA, CFD, FSI, growth and remodeling, multiphysics transport — with scientific ML techniques (constitutive neural networks, symbolic regression) and computer-vision pipelines for MRI, IVUS, catheterization, and clinical data.

Education

Ph.D., Materials Science — University of Minnesota–Twin Cities (2018–2024) Thesis: Multiscale, multiphysical phenomena in arterial remodeling. Advisor: Victor Barocas.

M.Sc., Mechanical Engineering — PUC-Rio (2016–2018) Dissertation: Numerical investigation of viscoelastic liquid curtain breakup. Advisor: Marcio da Silveira Carvalho.

B.Sc., Mechanical Engineering — PUC-Rio (2010–2015) Undergraduate thesis: Modeling of asphaltene deposition.

Experience

Postdoctoral Scholar — Stanford University, Department of Pediatrics

Palo Alto, CA · 02/2024 – present

  • Leads hybrid mechanistic + AI/ML workflows to optimize tissue-engineered vascular graft (TEVG) design, integrating large-animal experimental data with patient-specific FEA / CFD / FSI simulations.
  • Applies scientific ML — constitutive artificial neural networks (CANNs) and symbolic regression — to develop interpretable growth-and-remodeling laws for native veins and TEVGs.
  • Contributes to SimVascular (C++), adding modules for mesh generation, soft-tissue constitutive mechanics, and fluid–solid–growth (FSG) multiphysics coupling.
  • Builds Python / C++ pipelines for multimodal data (MRI, IVUS, catheterization) feeding downstream ML, statistical analyses, and mechanistic simulations.
  • Built end-to-end IVUS computer-vision pipelines (frame extraction → probe masking → contour segmentation → 3D reconstruction → STL/mesh).

Graduate Research Assistant — University of Minnesota, Biomedical Engineering

Minneapolis, MN · 09/2018 – 01/2024

  • Developed multiscale predictive models (hemodynamics, vascular mechanics, mass transport, growth and remodeling) for aortic aneurysm progression, patient-specific blood rheology, and TEVG performance — C++, Python, MATLAB, Ansys.
  • Built reproducible HPC simulation workflows on Linux / SLURM, generating synthetic and simulation-informed datasets for predictive model benchmarking.
  • Applied stress-based clustering, HMM-based domain segmentation, sensitivity analysis, and uncertainty quantification for automated parameter-space exploration.
  • Published 8+ peer-reviewed articles and a book chapter; presented at international conferences.

Research Scientist — Laboratory of Micro Hydrodynamics, PUC-Rio

Rio de Janeiro, Brazil · 01/2018 – 09/2018

  • Developed and verified FEA / CFD solvers for multiphase flow, viscoelasticity, and compressibility modeling.
  • Built internal benchmarks for solver validation and code optimization.

Project Services Intern — Royal Dutch Shell

Rio de Janeiro, Brazil · 2014 – 2015

  • Managed schedules, cost tracking, and performance reporting for deepwater project controls.
  • Created a subsea cost benchmarking database improving estimation accuracy by 15% for future field developments.

Skills

AI/ML & scientific ML — deep learning, symbolic regression, neural networks (intermediate) Mechanistic modeling — FEA, CFD, FSI, growth and remodeling (advanced) Programming — Python (advanced), C++ (intermediate), MATLAB (advanced), Ansys/Comsol (intermediate) Infrastructure — HPC / cluster computing, Linux, SLURM (advanced)

Awards, fellowships & leadership

  • 2025 — CHIP T32 Training Grant
  • 2025 — Rising Star in Computational and Data Science (Oden Institute, UT Austin)
  • 2024 — Baker Postdoctoral Fellowship (Stanford)
  • 2023 — Equity and Inclusion Leadership Showcase
  • 2023 — President’s Student Leadership and Service Award
  • 2022 — AHA Predoctoral Fellowship (rank 8%)
  • 2021 — CEMS TA Award
  • 2021 — Graduate Student Coordinator, CSE Women and BIPOC Initiatives
  • 2021 — Chair, DEI Working Group
  • 2018 — Second place, ABCM-EMBRAER Best M.Sc. Dissertation
  • 2017 — Faperj Nota 10 Fellowship