A music stem seperaration app for macOS using Computer Vision and Machine Learning techniques.
Blending expertise across AI, app development, and electronics, I excel at leveraging cutting-edge technology to craft and deliver innovative products that resonate with users and address real-world needs. My approach is deeply rooted in a product-centric philosophy, focusing on creating solutions that are not just technologically advanced but also intuitive, accessible, and highly relevant to everyday life. This commitment to innovation is exemplified in my work on products like Undertones AI, an AI-powered music editing tool, which simplifies complex audio processing tasks into user-friendly interactions.
An open source macOS & iOS app for delightful note taking.
Graphnote redefines note-taking with its innovative graph-style structure, focusing on relationships to organize information intuitively. With this project I demonstrate my ability to use complex state-of-the-art algorithms such as Conflict Free data Types (CRDTs), graph theory, etc. My speciality is taking research and applying it to real world applications.
A hardware project that displays album artwork on a a matrix LED display. It syncs with Spotify, and Photos. This project spans: hardware (raspberry pi + custom circuitry), mobile development, and backend dev with nodeJS.
I made this project because I enjoy engineering and I needed a pet project for the week. My ability to work in new domains, and tie together end-to-end solutions to products has beed demonstrated with this product.
Introducing an AI-assisted flowchart application for iPad with Apple Pencil support.
Utilizing machine learning algorithms for shape detection and recognition, Flowy streamlines the process of mocking and rendering pixel-perfect flowcharts into a single step.
A custom dataset was created and used to train a logistic regression binary classifier for detecting strokes of interest. Once a series of strokes has been classified as a target, the shape is classified with a custom convolutional neural network. This combination keeps the models small and allows for a multi-step approach, with a lightweight model running first and the heavier classifier only running inference if necessary.
My focus
Application development: A comprehensive background in app development, spanning from web to mobile, or from backend to embedded, gives me a breadth and generalists sense. But while also maintaining specialized knowledge in: iOS, macOS & web, means I can dig deep into the problems associated with specific platforms, giving me the ability to develop software solutions from end-to-end for a variety of domains. Computer Vision: Having built many projects involving vision technologies: neural nets, geometric methods, etc, I have become well versed in the field of computer vision. My specialization is taking research and turning it into state-of-the-art applications. Embedded Engineering: I build electronics projects and embedded projects as a hobby. From bluetooth enabled guitar pedals, to a computer vision based, tracked robot, I find satisfaction in building with atoms as well as bits!
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Undertones AI
Graphnote
