Resume

University of Texas at Austin

Bachelor of Science in Biomedical Engineering, Computational Track

Relevant Coursework: Embedded Systems, Biomedical Instrumentation, Circuits, Systems & Signals, Differential Equations & Linear Algebra, Intro to Computational Engineering Design, Statistics, Numerical Methods

Edge AI Team Lead

Longhorn Neurotech · Austin, TX

• Directed a team of student engineers to optimize neural networks for deployment on embedded systems (Raspberry Pi series).
• Designing a reusable quantization pipeline using ExecuTorch and an inference backend to support keyword spotting and future neuroprosthetic control applications.
• Developing a low-latency Python audio acquisition module for USB audio devices, using circular buffering to stream live audio into an on-device inference engine.

AI/ML Developer

Longhorn Neurotech · Austin, TX

• Developed machine learning models for Brain-Computer Interfaces (BCI) aimed at controlling a prosthetic arm, using architectures such as CNNs and Capsule Networks.
• Improved BCI model accuracy from 60% to 80% by tuning hyperparameters with Optuna and using Captum to reduce overfitting.

Research Intern

Recanzone Laboratory, UC Davis · Davis, CA

• Developed an interactive 3D feature visualization UI for open-source neuroscience software, allowing users to inspect neural features through real-time camera and projection controls.
• Implemented real-time rotation and transformation logic using linear algebra, enabling mouse-based inspection of neural spike data.

Researcher and Programmer

Functional Optical Imaging Lab, UT Austin · Austin, TX

• Developed a real-time Laser Speckle Analysis UI in Python, interfacing with Basler cameras and Arduinos for automated laser intensity control.
• Enhanced speckle pattern evaluation by implementing contrast analysis algorithms to maximize measurement precision.
• Refactored contrast adjustment scripts from MATLAB to Python, enabling integration with the real-time Laser Speckle Analysis UI.

Cloud to Edge Heart Rate Monitor

• Architected a pipeline that retrieves heart rate datasets from Cloud Storage to a Raspberry Pi 5, utilizing it as a patient simulator.
• Developed a UART communication protocol to stream simulated patient data from the Pi 5 to a Pi Pico for low-latency processing.
• Built a custom C-based inference engine on the Pico's dual-core Cortex-M0+ architecture to execute quantized neural networks on the incoming data stream in order to classify the patient's heart condition.

Embedded Multiplayer Racer (ARM Cortex-M0+)

• Earned 'Best Embedded Design' in a class-wide competition for excellence in hardware-software integration.
• Built a bare-metal multiplayer racing game in C for a TI MSPM0 microcontroller, implementing collision detection, velocity-based physics, and sprite rendering.
• Programmed register-level drivers for UART, ADC, and DAC to interface with analog joysticks and audio peripherals, bypassing standard libraries for efficiency.
• Designed a custom UART protocol to synchronize player coordinates and game events between two microcontrollers in real-time.

ECG PCB Project

• Designed a custom Electrocardiogram PCB in Autodesk Fusion 360, creating the schematic for physiological monitoring.
• Engineered the analog signal chain, implementing instrumentation amplifiers and active filtering (bandpass/notch) to reject 60Hz noise and isolate cardiac signals.
• Coordinated component selection and fabrication logistics, optimizing the Bill of Materials for cost and assembly efficiency.