Gabriel Adriano

Aurora, CO · (720) 737-7937 · gabriel.m.adriano17@gmail.com

As a biomedical engineer with expertise in computer vision and machine learning, I have developed skills in processing and analyzing large-scale medical imaging data using techniques such as PointNet-based segmentation and CNN-based modeling. My experience spans software development, research, and analysis, with a focus on applying technical expertise to drive innovation and improve outcomes in medical imaging contexts. I am well-versed in programming languages like Python and MATLAB, and have worked with frameworks like PyTorch and OpenCV to design and implement efficient algorithms for image analysis.

Experience

Software Quality Engineer I

Medtronic

As a Software Quality Engineer at Medtronic, I played a key role in ensuring regulatory compliance with FDA and third-party audits, and collaborated with cross-functional teams to plan and execute upgrades to medical devices.

  • Supported FDA and third-party audits, ensuring compliance with 21 CFR Part 820 and ISO 13485 quality system standards.
  • Collaborated with cross-functional engineering and regulatory teams to plan and implement upgrades to biomedical software and hardware, maintaining compliance by updating risk management, verification, and design control documentation in accordance with IEC 62304 and IEC 82304.
  • Developed a Python-based log analysis tool that automated root cause investigations for device failures, increasing troubleshooting efficiency by 30%.
  • Built a data program GUI using ttkbootstrap to analyze five years of historical complaint records, automatically identifying and correcting missing data to enhance trend analysis and CAPA accuracy.
November 2024 - Present

Software Engineer I

Becton, Dickinson and Company (BD)

As a Software Engineer at BD, I developed and deployed innovative solutions to improve efficiency and reduce testing cycle time in firmware design verification.

  • Designed and deployed Python-based HTTP and MQTT servers using http.client and paho-mqtt, validating data communication workflows, and reducing firmware design verification cycle time.
  • Integrated three legacy flow measurement systems using socket programming in Python, C++, IAR Embedded Workbench, enabling real-time data visualization and unified system monitoring.
  • Authored unit test cases using Pytest to verify firmware and API functionality within continuous integration workflows.
  • Managed Git repositories for release and build management of Python-based servers, coordinating software distribution to users.
December 2023 - December 2024

Engineer Intern

BD

As an Engineer Intern at Becton, Dickinson and Company (BD), I contributed to the development of high-quality firmware releases through rigorous testing and analysis.

  • Conducted firmware failure testing and root cause analysis using Agile methodologies; collaborated across engineering, QA, and product teams to enhance performance and resolve critical defects.
  • Performed verification and validation (V&V) testing for firmware releases, generating formal test documentation to support FDA 510(k) submissions
  • Executed roaming and TLS 1.2/1.3 security protocol testing, validating system interoperability and ensuring secure data transmission in embedded environments
December 2022 - December 2023

Research Assistant

New York University, NYU Video Lab

As a Research Assistant at NYU Video Lab, I contributed to the development of innovative solutions for stroke patient rehabilitation.

  • Contributed to the development of a Python-based sensor-less motion tracking system for stroke patient rehabilitation, leveraging pose and hand estimation algorithms to monitor exercise performance. =
  • Benchmarked pose estimation frameworks (MediaPipe, OpenPose) for accuracy utilizing HPC computing and bash scripting.
January 2023 - May 2023

R&D Intern

BD

As an R&D Intern at Becton, Dickinson and Company (BD), I applied my expertise in computer vision and algorithm optimization to drive innovation and improve performance.

  • Analyzed and optimized computer vision algorithms utilizing YOLOv5 for object detection in biomedical imaging applications
  • Developed and deployed Kotlin-based solutions to improve algorithmic performance and system reliability across Android and embedded platforms
  • Collaborated with international teams and third-party developers to implement algorithmic changes, validate performance improvements, and ensure compliance with system-level integration standards
May 2022 - August 2022

Medical Research Support Tech III

Medtronic
  • Segmented vertebrae DICOM images from spinal CT scans using ITK-SNAP, generating high-quality ground truth datasets for training and validation of deep neural networks in surgical navigation applications.
  • Collaborated with biomedical engineers and data scientists to ensure accurate anatomical labeling and consistency across large-scale imaging datasets.
October 2020 - May 2022

Education

New York University

Masters of Science
Biomedical Engineering
Merit Graduate Scholarship 2021-2023

GPA: 3.444

September 2021 - May 2023

University of Colorado - Denver

Bachelors of Science
Bioengineering
Minor: Mathematics, Computer Science
Dean’s List Fall 2017

GPA: 3.369

August 2017 - May 2021

Skills

Programming Languages & Tools
  • Programming / IDE: Python, MATLAB, R, SQL, C++, IAR Embedded Workbench, VS Code, Visual Studio
  • Libraries: PyTorch, TensorFlow, OpenCV, Numpy, Pandas, Jupyter, ttkbootstrap, paho-mqtt, http.client, Flask
  • Imaging & Data Tools: ITK-SNAP, DICOM, NIfTI
  • Software Engineering: Git, CI/CD, Agile Scrum
  • Research & Analysis: Signal Processing, Computer Vision, Statistics, Model Evaluation
Workflow
  • Risk Managment
  • Verification & Validation Test Case Planning
  • Cross Functional Teams
  • Agile Development & Scrum

Projects

PointNet for Body Part Segmentation

  • Implemented a PointNet model using Pytorch to segment body parts within 3D point clouds of humans in motion.
  • Utilized the PointNet architecture, which consists of a feature transformation layer and a symmetric function layer, to learn effective point-wise features and spatial relationships between points.
  • Trained and evaluated the model on various metrics, including, precision, recall, and F1-score, achieving state-of-the-art results in body part segmentation.

CNN vs. Linear Regression for Tumor Light Absorption

  • Used Jupyter Notebook and TensorFlow to investigate the relationship between absorbance coefficients and breast cancer tumor light absorption via Convolutional Neural Networks (CNN) and linear regression.
  • Preprocessing data using Pandas and Numpy, trained and evaluation model via TensorFlow to compare predictive accuracy between traditional linear regression models and deep learning approaches.
  • Generated insights to inform non-invasive optical diagnostic techniques and improve tumor characterization methods.

Real-time CPR Training Device

  • Developed an Arduino based data acquisition device with MATLAB GUI to develop a real-time CPR training device using accelerometers and an MQTT server to accurately track compression rate, depth, and hands-off time.
  • Collaborated with a pediatric physician and a team of computer science students to integrate device data into a mobile app, improving usability and trainee feedback.
  • Designed and implemented a data visualization GUI and feedback algorithms using MATLAB, enabling real-time performance tracking and skill assessment for trainees.
Block Diagram

Inventory Management Using Image Recognition

  • Built a MATLAB OpenCV-based image recognition program to automatically identify prosthetic components.
  • Integrated detected components into an inventory management system, improving tracking accuracy and reducing manual data entry errors.