Agent Capabilities is a self-serve build environment inside Maven AGI's Agent Designer that allows technical teams to quickly create, test, and deploy new agent behaviors. It gives organizations a structured, consistent way to define what agents do and when they do it—using custom actions and triggers. I contributed to building the tooling that makes this possible, enabling customers to extend their AI agents across connected systems without custom development or months-long engineering cycles.

Quotient is a modern bill-splitting platform designed to eliminate friction in group payments. Users can upload or scan receipts, automatically extract line items using AI-powered OCR, and split expenses intelligently across participants. The system generates clean Venmo deep-links and optional SMS reminders to streamline repayment.

Built with Next.js 14, Supabase (Postgres + pgvector), and Tailwind CSS v4, Quotient leverages Google Gemini for receipt parsing and structured data extraction. The backend is designed for scalability and low-cost operation, with secure user authentication and a clean, mobile-first UI optimized for real-world usage. The goal is to create a frictionless financial coordination layer for everyday group transactions.

Kairos is an AI-powered weekly planner that intelligently breaks down your high-level goals into actionable calendar events. It leverages a multi-agent orchestration system—comprising fitness, nutrition, and scheduling agents—to generate personalized plans based on your preferences, constraints, and historical data. The front end is built with Next.js and Tailwind CSS for a responsive, glass-like UI, while the FastAPI backend handles agent coordination, RAG-based context retrieval, and integration with Google Calendar. Dockerized and deployed on Google Cloud Run, Kairos uses PostgreSQL with pgvector embeddings to store user profiles and plan references, ensuring scalable, secure, and privacy-focused time management automation.

The Kansas and Minnesota Departments of Transportation initiated a Road Usage Charge Pilot to explore the feasibility of replacing the gas tax with a road usage charge. The goal was to develop web applications that would facilitate data collection, provide program information, and help participants track their mileage and fuel consumption.

After seeing an advertisement for a LED Display that shows various screens such as the time, current weather conditions, and fun animations. I decided it would be a fun challenge to try to build one myself. I used a Raspberry Pi Pico to control the project.

I also noticed there weren't many resources or guides available to help with a project like this so I started writing up instructions on how to replicate my project and put them on GitHub. This is a current project and I am continually updating it.

As part of a collaborative team of four, I contributed to the development of an iOS application for the University of Vermont Medical Center. The objective given to us by the medical professionals, was to educate individuals at risk of falling and empower them to adopt safer practices. Using SwiftUI, we blended design and functional factors to ensure the app's features were tailored to meet the needs of the end-users.

To enhance the app's functionality, we implemented efficient parsing of user data and leveraged locally-stored JSON files for seamless storage and retrieval. Throughout the project, we used an Agile approach, working closely with our team to manage workloads effectively and deliver high-quality results within the allocated semester timeframe.

My sophomore year, I went through Burlington's apartment search and it was a nightmare. You'd tour a place, have to decide on the spot, and only after moving in would you find out the utilities were far more expensive than advertised, the landlord was unreachable, and the neighbors were unbearable. There was no way to know any of that before signing a lease.

So I built RNTR, a platform that combined the review model of Yelp with the search and discovery of Zillow, applied specifically to Burlington's rental market. Look up any address and see reviews of the apartment and the landlord, search available rentals, and leave reviews for your current place. The whole idea was that renters deserved to know what they were getting into before they signed anything.

I built the entire thing off the back of one intro web design class, teaching myself everything else from scratch. Authentication flows, relational database design, address standardization, dynamic UI rendering. It was fully functional, tested with friends, and won an award at the UVM CS Fair.

What kept it from gaining traction was the cold start problem. No data meant no users, no users meant no data. Monetization was its own challenge too, since you only apartment hunt once a year and the landlords you're holding accountable have little incentive to support the platform. Eventually let the domain expire.

As the Senior Media & Communications Chair for the Schlesinger Global Family Enterprise Case Competition, a prestigious global business competition focused on family businesses, I identified the need to enhance our online presence. With participants from 27 countries over the past decade, it was crucial to develop a website that accurately represented the competition's scale and prominence.

Drawing on my web development experience, I quickly mastered WordPress to create a professional and visually appealing website for the current year's competition. This ensured both immediate impact and long-term maintainability, providing a seamless user experience for participants, judges, coordinators, and visitors. I also implemented effective search engine optimization (SEO) techniques, resulting in an estimated 300% increase in website traffic. By optimizing the website's search engine ranking, we significantly expanded the competition's global reach and recognition.

In addition to the website I came up with new ways to promote the competition and engage participants.

Under the guidance of Professor Josh Bongard, I worked on a project to evolve the locomotion capabilities of a simulated robot in various environments. Through evolutionary algorithms, I evolved a neural network to achieve optimal performance. Throughout the semester, I explored different strategies and techniques to continually improve the robot's adaptability and efficiency.

For my final project in the course on compiler design and construction, I focused on extending the language's capabilities by implementing simple dataclasses. This project was a culmination of the various topics covered throughout the course, which provided a comprehensive understanding of the compilation process.

Throughout the semester, I explored the representation and analysis of code and how to translate a high-level programming language into Intel x86 assembly language. We learned about essential concepts such as register allocation, static type checking, and handling mutable data. The course also emphasized the significance of garbage collection for efficient memory management in compiled languages.

By extending the language with dataclasses, I not only demonstrated my proficiency in implementing new language features but also showcased my grasp of the entire compilation process. This project solidified my knowledge of code analysis, code generation, and the crucial role of language features in creating powerful and expressive programming languages.

After failing to keep my plants alive, I used an Arduino Nano and an ESP8266 module, along with sensors for soil moisture, light, and air quality, to automate the process of watering plants and create a more nurturing environment.

By continuously monitoring important factors like soil moisture, light levels, and air quality, my system provided valuable insights into the specific needs of my plants. With the help of a peristaltic pump, the system automatically watered the plants, ensuring they received the appropriate amount of water at the right times.

This project allowed me to explore the integration of hardware components and programming skills, resulting in a practical solution to improve plant care. In the end, I have less dead plants!

For this project, I collaborated with an agile team to recreate the immensely popular game, Plants vs. Zombies, using Python and the Python Arcade library. As part of the team, I played a pivotal role in organizing the program flow and backend development. I focused on designing the structure of the game, ensuring seamless transitions between different states and screens. Additionally, I took charge of implementing the spawning mechanics for zombies, creating algorithms that combined randomness with the timing patterns seen in the original game. This resulted in a challenging and engaging gameplay experience.

Another significant contribution I made was developing a dynamic system for level progression. I designed a flexible framework that allowed for the easy addition and modification of levels, accommodating three waves of zombies per level, each increasing in difficulty. This approach added replayability and a sense of progression for players. By working on this project, I enhanced my skills in game development, algorithm design, and collaboration.

As my first open-ended project during my Introduction to Programming course, I had the ambitious goal of recreating the WWII enciphering machine, Enigma, using Python. Despite being new to programming, I wanted to challenge myself.

Through self-guided learning, I successfully developed a fully functional version of Enigma. Going beyond the original scope, I even added an additional feature that allowed for the translation of enciphered messages into Morse code. This project not only showcased my ability to tackle complex programming tasks but also demonstrated my dedication to expanding my skill set and exploring innovative solutions.