Hi, I'm Gabrielle

PhD Candidate in Healthcare AI

Exploring the frontiers of medical technology through innovative research and artificial intelligence to advance patient care.

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About Me

My introduction

Biomedical engineer and Ph.D. candidate harnessing AI in healthcare to enhance diagnostics and treatment, reduce health inequities, and advance medical imaging technologies.

300+ Healthcare
Stakeholder
Interviews
7+ Years
Research
4+ Years
Strategic
Leadership
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Qualification

My personal journey
Education
Work
Leadership + Service

BS in Biomedical Engineering, Neural Interfaces

University of Utah
2014 - 2018

MS in Biomedical Engineering, Neural Interfaces

University of Utah
2018 - 2020

PhD in Bioengineering, Machine Learning

UCSF-UC Berkeley
2021 - Present

Undergraduate Research Assistant

Neural Information Lab, University of Utah
2014 – 2017

Undergraduate Research Assistant

Mechanisms of Synaptic Functions Lab, University of Utah
2016 – 2018

Product Development Engineering Intern

Ortho Development, SLC, UT
2017 – 2018

Data Science Impact Fellow

Sorenson Impact Center, SLC, UT
2018

Graduate Researcher

Computer Vision in Anesthesiology Lab, University of Utah
2018 – 2021

Software Development Intern

Compassion International, Colorado Springs, CO
2020

Technical Lead

A.I. Healthcare Venture, NSF National I-Corps Program, Winter Cohort
2021

PhD Researcher

Musculoskeletal Quantitative Imaging Research Group, UCSF, CA
2022 – Present

Volunteer Lab Technician

Orthotics and Prosthetics Department, Shriners Hospital for Children - SLC, UT
2014 – 2016

Director of Research + Project Development

Project Embrace nonprofit - SLC, UT
2016 – 2018

College of Engineering Ambassador

University of Utah - SLC, UT
2017 – 2019

Chief Development Officer

Project Embrace nonprofit - SLC, UT
2018 – 2022

Research Projects

What i'm working on

ML-Driven
Biomarker Discovery
in Osteoarthritis Imaging

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ML-Driven Biomarker Discovery
in Osteoarthritis Imaging

  • Initiated and led a research project utilizing quantitative MRI (qMRI) to construct a 100-Dimensional Interpretable Feature Space, aiding in the early detection and progression tracking of Knee Osteoarthritis with the Osteoarthritis Initiative (OAI) data.

  • Developed and published an open-source software tool for sharing research outcomes, thereby improving accessibility and fostering collaboration in the scientific domain.

  • Conducted an oral presentation of our comprehensive study at the International Society for Magnetic Resonance in Medicine (ISMRM) 2023, contributing to the advancement of new research pathways in the understanding of osteoarthritis mechanisms.

  • Identified and statistically validated new morphological biomarkers through advanced machine learning techniques, providing critical insights into the risk factors and progression patterns of osteoarthritis.

  • GitHub Slide Deck Video Preprint

mskSAM -
Foundation Model
for Musculoskeletal MRI

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mskSAM -
Foundation Model
for Musculoskeletal MRI

  • Leading an ongoing investigation into the adaptability of the Segment Anything Model (SAM) for diverse musculoskeletal MRI cases, analyzing its performance across varying anatomies, imaging sequences, and patient demographics.

  • Evaluating SAM's application in zero-shot and fine-tuning scenarios to uncover the model's nuanced strengths and weaknesses in medical imaging, contributing to the knowledge base of foundation models.

  • Pioneering a multi-faceted study to develop a generalizable system for musculoskeletal MRI segmentation, aimed at facilitating easy adaptation by clinical research groups for unique imaging needs.

  • Presenting the findings and progress of the study at the 32nd Annual Meeting of ISMRM in Singapore, 2024, as part of the session on the future of AI in MRI.

  • GitHub Abstract

Knee MRI Transformation: Self-Supervised Models for Enhanced Diagnostic Workflows

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Knee MRI Transformation: Self-Supervised Models for Enhanced Diagnostic Workflows

  • Addressing a significant healthcare challenge by optimizing clinical radiology workflows through Self-Supervised Learning (SSL) models, aiming to enhance diagnostic efficiency for knee MRI.

  • Investigating state-of-the-art methods for extracting robust feature representations from knee MRI data, intending to improve performance in downstream tasks within a limited label paradigm.

  • Conducting thorough research to overcome the scarcity of expert annotations in medical imaging, enhancing the development of models that are generalizable and clinically relevant.

  • Embarking on the creation of workflow-specific anomaly segmentation models for radiology and orthopedic surgery, addressing the unique needs of these distinct clinical environments.

  • Slide Deck Video

Portfolio

Previous work

Smart Airway Management

Computer vision for real-time intubation guidance.

Demo

Project Embrace

Distributing healthcare devices to underserved patient populations.

Slide Deck

Ultrafast Endocytosis

Investigating Calcium's Pivotal Role in Neurotransmitter Release.

Poster

Let's Work Together

Join me in creating AI solutions to alleviate healthcare provider burnout, extend care to underserved communities, and enhance patient quality of life. Let's make a positive impact together.

Contact Me

Academic Works

Publications and Awards
Publications + Presentations
Awards

MskSAM - Foundation model for Musculoskeletal MRI Segmentation

Hoyer, G; Tong, M. Towards a Generalizable Foundation Model for Multi-Tissue Musculoskeletal MRI Segmentation. In Proceedings of the 32nd Annual Meeting of ISMRM, Singapore, 2024. Session: The Future of AI in MRI: Emerging Technologies & Directions. 8045. Awarded Magna Cum Laude.

  • GitHub Abstract

    • Quantitative MRI Interpretable 100D Feature Space

    Hoyer G et al. Quantitative MRI Interpretable 100D Feature Space of Knee Osteoarthritis. In Proceedings of the 31st Annual Meeting of ISMRM, Toronto, Ontario, Canada, 2023. Session: ML/AI New Ideas. 993.

  • GitHub Slide Deck Video Preprint

    • Comparison of Procedural Distancing in Airway Management

    Hoyer et al. Comparison of Procedural Distancing of Primary and Advanced Intubation Techniques. In Proceedings of the Difficult Airway Society Annual Meeting, UK, 2020.

    Collection and Strategic Use of Intubation Data

    Hoyer et al. Automatic Video Laryngoscope Archiving System, First Pass Rates in Archived Intubations. In Proceedings of the Society for Technology in Anesthesia Annual Meeting, Austin, TX, 2020.

    Investigating Beta-Coherence in Parkinsonian Rats: Self-Directed Motion Effects

    Dorval A, Polar C, Hoyer G. Cortico-Subthalamic Beta-Coherence Suppression During High-Intensity, Self-Directed Motion in a Parkinsonian Rat Model. In Proceedings of the 9th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE Engineering in Medicine and Biology Society, San Francisco, CA, USA, 2019. Research posters paper - 1 page. Submission No. 491.

  • Poster

    • UCSF Discovery Fellowship

    2023 – Present

    Discovery Fellows are chosen for their leadership potential, excellence in research, community-mindedness, and communications skills. The role entails advocating for UCSF's foundational and biomedical science programs while championing innovative research within healthcare.

    NIH T32 BioE Training Grant

    2021 – 2022

    NSF National I-Corps Program Grant

    2021

    The National Science Foundation's Innovation Corps (I-Corps™) program offers experiential education to equip researchers with valuable insights into entrepreneurship, business startup, and industry demands and challenges. I-Corps serves as a catalyst for turning inventions into real-world impact, combining scientific exploration with industrial discovery in a rigorous, relevant, and evidence-based environment. This program empowers researchers to accelerate the transition of lab concepts to market-ready solutions, contributing to a national innovation network that transforms scientific research into societal benefits.

    NASA Space Grant Fellowship

    2019 – 2021

    Initiated in 1989, the National Space Grant College and Fellowship Project, or Space Grant, is a national network of colleges and universities aimed at expanding opportunities for Americans to engage in NASA’s aeronautics and space projects. This includes supporting science and engineering education, research, and public outreach efforts to enhance understanding and participation in space exploration and related fields.

    Chevron Scholarship for Women in Stem

    2018 – 2019

    The Chevron Scholarship for Women in Science, Technology, Engineering, and Math (STEM) fields supports self-identified women majoring in STEM. Created for the Women's Resource Center, this scholarship aims to encourage and assist women pursuing education and careers in science, technology, engineering, or math.

    Bioscience Undergraduate Research Scholar

    2015 – 2017

    Awarded by the Department of Biology at the University of Utah, this recognition supports and acknowledges outstanding undergraduate students engaged in bioscience research. The program aims to foster a deeper understanding and passion for scientific inquiry among bioscience students through active participation in research projects and initiatives.

    NSF Research Experiences for Undergraduates Grant

    2015 – 2016

    Associated with the Neural Information Lab and Dr. Chuck Alan Dorval, the NSF Research Experiences for Undergraduates (REU) program encourages active research participation by undergraduate students in areas funded by the National Science Foundation. This initiative aims to provide undergraduates with hands-on research experience, fostering a deeper engagement and understanding in their field of study.

    ACCESS Program for Women in STEM Scholar

    2014 – 2015

    A scholarship award and first-year experience designed specifically for incoming fresh-women with a keen interest in the sciences, including math, biology, chemistry, or physics. The ACCESS Program supports and encourages women embarking on their journey in STEM fields, aiming to provide them with foundational support, mentorship, and engagement in their chosen disciplines.

    Skills

    My technical growth

    Research and Analytics

    More than 8 years

    Experimental Design

    90%

    Statistical Data Analysis

    80%

    Predictive Analytics

    80%

    Statistical Modeling

    75%

    Technical Writing

    90%

    Oral Communication

    95%

    Independent Research

    95%

    Biomedical Informatics

    85%

    Technical Skills

    More than 5 years

    Python Data Analysis Stack

    95%

    MATLAB

    90%

    JavaScript

    80%

    Machine Learning

    85%

    Computer Vision

    75%

    PyTorch

    90%

    Distributed Computing

    85%

    MRI Physics

    70%

    Healthcare Innovation & Strategic Implementation

    More than 5 years

    Product Development

    90%

    Clinical Development

    65%

    Regulatory Compliance

    80%

    Project Management

    90%

    Public Speaking

    95%

    Stakeholder Communication

    90%

    Team Leadership

    90%

    Engineering Design

    90%

    Version Control with Git

    80%

    Contact Me

    Get in touch

    Call Me

    801-631-0750

    Email

    gabbie.hoyer@ucsf.edu
    gabrielle_hoyer@berkeley.edu

    Location

    Department of Radiology and Biomedical Imaging, UCSF
    1700 4th St., Suite 201
    San Francisco, CA 94158
    *field required
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