I’m Aaron Casserly, a Python software engineer focused on AI and full-stack development. I design and build AI-powered applications using Next.js, FastAPI, PyTorch, and Scikit-Learn, translating theory into practical tools. My work spans data structures and algorithms, reinforcement learning, clustering, CNNs, and high-performance computing.
I’m actively involved as an AI trainer and domain expert with Alignerr, Outlier, Pareto, and Amazon Mechanical Turk, applying math, physics, and software knowledge to improve model behavior and data quality.
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Contributed to full stack AI application utilizing Next.js frontend and FastAPI Python backend which interacts with OpenAI image generation model. The app is an event planning tool which recommends venues for the user’s selected event type (Wedding, Birthday, etc) given location constraints. The user can then select a theme for the event and utilize images in that theme or uploaded images to generate original visualizations of scenes from the event (say a wedding reception) via image-gpt-1. The main areas where I contributed were creating a backend script that takes the user input from the frontend to send a multimodal prompt to the model to generate the scene image, generalizing that such that the user can generate multiple scene types, and integrating the backend venue recommendation code contributed by another member with the frontend interface. Skilled with utilization of GitHub Copilot to enhance the efficiency of the development process. Video of product demo here (https://www.twine.net/signin
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