I’m a software engineer and researcher passionate about building intelligent, user-centered solutions. With a background in full-stack development and applied machine learning, I enjoy exploring how artificial intelligence can be leveraged to solve real-world problems.
My work spans across designing iOS applications for accessibility, developing scalable IoT systems on AWS, and building AI models for computer vision and natural data classification.
I thrive at the intersection of software engineering and AI: translating complex data into actionable, meaningful applications. Whether it’s deploying cloud-based systems, training neural networks, or prototyping accessible mobile apps, I’m driven by curiosity and a commitment to developing solutions that make an impact.
Work Experience
- Led the end-to-end design and prototyping of an iOS communication app in Swift for non-verbal children that require speech assistance.
- Lead user requirement gathering with PMs and Speech Pathologists to design the back-end SQLite database to support personalized communication workflows for various use-cases.
- Led the research and evaluation process to select a secure remote development solution for collaborative code access. Established criteria around security, speed, cost, and accessibility.
- Led the migration of 3 primary codebases to secure, cloud-based version control on AWS, improving development workflow for 5 engineers working with the codebase.
- Designed and implemented an LSTM based AI model to predict radio wave behavior.
- Performed and documented black box testing to validate system functionality and support reliable software delivery.
- Debugged and maintained critical legacy code in C, contributing to stability and performance improvements.
Projects
Portfolio Website
A personal portfolio built with SvelteKit and Tailwind CSS, featuring responsive design and dynamic content.
AI Fungi Identifier
Enchancing Mushroom Safety through Backprogated Multi-Layer Perceptron Neural Network Identification
IoT Water Monitor
Remote monitoring system for residential rainwater harvesting tanks, aimed at improving water management for home gardeners.
AI Vehicle Detection
A comparative study of deep learning models for vehicle detection and segmentation, analyzing trade-offs between accuracy and inference speed.
Inverse Assembler
A command-line disassembler built from scratch in 68K Assembly to translate machine code into human-readable instructions.
Image Deep Learning
A Convolutional Neural Network in MATLAB for ASL letter classification, achieving 2nd place in a Kaggle competition.
Skills
C
C++
Python
Java
Swift
Cuda
Bash
ARM Assembly
Motorola 68000 Assembly
MATLAB
Arduino
JavaScript
TypeScript
HTML
CSS
RESTful APIs
Svelte
Flask
React
Next.js
Node.js
Tailwind CSS
JSON
AWS
Azure
Vercel
Docker
Git
MySQL
PostgreSQL
DynamoDB
Supabase
TensorFlow
PyTorch
Keras
scikit-learn
Pandas
NumPy
NLTK
Seaborn
Matplotlib
Google Gemini API
Visual Studio Code
Figma
Qualtrics
ArcGIS Pro
LaTeX
Education
