Education Experience Projects Contact

Ryan Stone

I design innovative medical solutions through biomedical engineering, bridging healthcare challenges with cutting-edge technology and data-driven insights.

  1. MATLAB
  2. Python
  3. SolidWorks
  4. LabVIEW
  5. AutoCAD
  6. C++
  1. MATLAB
  2. Python
  3. SolidWorks
  4. LabVIEW
  5. AutoCAD
  6. C++
  1. CAD/FEA
  2. 3D Printing
  3. Signal Processing
  4. Data Analysis
  5. Biomaterials
  6. Medical Imaging
  1. CAD/FEA
  2. 3D Printing
  3. Signal Processing
  4. Data Analysis
  5. Biomaterials
  6. Medical Imaging

EDUCATION

August 2020 -May 2024

BS in Biomedical Engineering
Georgia Institute of Technology
Atlanta, GA

  • GPA: 3.7/4.0 | Dean's List: Fall 2021, Spring 2022, Fall 2023
  • Relevant Coursework: Biomechanics, Medical Imaging Systems, Biomaterials, Biosignal Processing, Tissue Engineering, Biomedical Instrumentation, Computational Bioengineering, Medical Device Design.

Fall 2016 -Spring 2020

North Atlanta High School
Atlanta, GA

  • Advanced Placement Scholar with Distinction | National Honor Society Member
  • Completed AP Biology, AP Chemistry, AP Physics, and AP Calculus BC with scores of 4 or higher.

EXPERIENCE

June 2023 -August 2023

Biomedical Engineering Intern • Medtronic

  • Assisted in the design and testing of cardiac monitoring devices, conducting finite element analysis (FEA) simulations to optimize device durability and performance.
  • Collaborated with cross-functional teams to develop test protocols for implantable devices, ensuring compliance with FDA 21 CFR Part 820 and ISO 13485 standards.
  • Analyzed biosignal data from ECG sensors using MATLAB and Python to improve signal-to-noise ratio by 18%, enhancing diagnostic accuracy.

January 2023 -May 2024

Undergraduate Research Assistant • Georgia Tech Tissue Engineering Lab

  • Conducted research on biocompatible scaffolds for cartilage regeneration, performing cell culture techniques including stem cell differentiation and viability assays.
  • Utilized 3D bioprinting technology to fabricate tissue scaffolds with controlled porosity and mechanical properties, achieving 92% cell viability.
  • Presented research findings at the Biomedical Engineering Society (BMES) Annual Meeting, contributing to a manuscript submitted for peer review.

September 2022 -December 2022

Clinical Engineering Volunteer • Emory University Hospital

  • Shadowed clinical engineers in the maintenance and calibration of medical equipment including ventilators, infusion pumps, and patient monitoring systems.
  • Assisted with preventive maintenance procedures and equipment troubleshooting, gaining hands-on experience with biomedical instrumentation in a hospital setting.
  • Developed strong communication skills by interfacing with clinical staff to ensure medical devices met safety and operational standards.

RECENT PROJECTS

Smart Prosthetic Hand with Feedback

Project 1

Led a senior design team to develop a low-cost myoelectric prosthetic hand with integrated pressure sensors and haptic feedback. The device utilized EMG signal processing algorithms in MATLAB to control five independently actuated fingers. KEY FEATURES: 3D-printed components designed in SolidWorks to reduce manufacturing costs by 65% compared to commercial alternatives. Arduino-based control system with real-time force feedback. Successfully demonstrated grip force control with 85% accuracy during user testing. Project received First Place at Georgia Tech Capstone Design Expo.

Portable EEG Seizure Detection System

Project 1

Developed a wearable EEG headset for real-time epileptic seizure detection using machine learning algorithms. Collected and preprocessed biosignal data from multiple channels, implementing digital filters in Python to remove artifacts and noise. Trained a convolutional neural network (CNN) achieving 91% classification accuracy in distinguishing seizure events from normal brain activity. The system featured Bluetooth connectivity for data transmission to a mobile application, providing caregiver alerts within 3 seconds of seizure onset. This project demonstrates integration of biomedical instrumentation, signal processing, and machine learning to address critical healthcare challenges in neurological monitoring.