Welcome to P-ai!

Apply To Be a Spring ‘25 Project Member:

Read through the application information to get an introduction to the requirements and necessary information to apply.

Applications are hosted through the button above and will close on Sunday, 2/2 at 11:59 pm PST.

SP 25’ Projects

p-SymPy

Lead: Tilo Reneau-Cardoso, PO’25

tiloreneau@gmail.com

Team Size: ~5

Speeding up SymPy’s new assumptions

Have you ever wanted to contribute to open source software? Join my project and gain the opportunity to do just that! My goal is to enhance the performance of the assumptions module in SymPy, a widely-used Python-based computer algebra system (think WolframAlpha, but in Python). As an expert in SymPy’s new assumption system, I’ve guided newcomers in contributing to it, drawing from my experience as a contributor during Google Summer of Code 2023.

Tech Stack

  • Python

  • GitHub/Git

Project Member Requirements

  • Self-initiative

  • Time commitment 

    • At least 4+ hours per week.

    • Participation in 1-hour weekly meeting is required.

  • CS62/CS70 or equivalent 

    • If you haven’t taken data structures yet, evidence of coding proficiency such as personal projects can substitute it. 

  • Previous experience with GitHub/Git

    Python proficiency

  • Basic familiarity with logic

  • An interest in math

p-laylist

Co-Lead: Angelina Tsai HMC ‘26 -

antsai@g.hmc.edu

Co-Lead: Tyler Headley HMC ‘26 -

theadley@g.hmc.edu

Co-Lead: Korin Aldam-Tajima HMC ‘26 -

kaldamtajima@g.hmc.edu

Team Size: 5-6

AI-powered music recommendations.

Have you ever been frustrated with Spotify recommending the same songs repeatedly, even when your interests change? Or wished you could expand your music taste and fill in the gaps in your playlists? Our project tackles these issues by developing an AI-powered web app for collaborative music recommendations. Our platform will generate daily, personalized playlists by using a hybrid of collaborative filtering and content-based recommendation techniques.

This is a continuing project from Fall ‘24. Please refer to our proposal to learn about features that were implemented last semester.

Tech Stack

  • Python

  • PyTorch

  • Google Colab

  • Flask

  • React

  • Docker

  • AWS

  • PostgreSQL

Member Requirements:

  • Excitement about our project and willingness to learn new skills

  • ML Team: Python, experience with ML libraries and/or model deployment preferred

  • SWE Team: Python, experience with some of our tech stack preferred

  • Creative Development Team: Music knowledge or graphic design are valuable, even if you don’t have any technical experience

  • Commit 4-5 hours/week

p-UBet

Co-Lead: William Haspel PO ‘27 -

wahp2023@mymail.pomona.edu

Co-Lead: Vadym Mussienko PO ‘27 -

vmmv2023@mymail.pomona.edu

Team Size: 5

A goal setting app that incorporates aspects of fantasy sports betting to enhance accountability.

This project aims to develop a social goal-betting web application, inspired by elements of fantasy sports betting, to help users achieve their personal goals through accountability, community, and stakes. The app will allow users to set personal goals, place monetary bets on their success, and invite friends or peers to bet on their challenges for monetary and social reinforcement. The app’s unique angle blends gamification, social collaboration, and psychology to encourage habit formation and sustained motivation.

Tech Stack

  • Frontend: Next.js (React.js) HTML CSS Tailwind

  • Backend: Node.js, Next.js

  • Database: PostgreSQL, Prisma ORM

  • Deployment: Vercel

Member Requirements:

  • Proficient in at least one programming language (preferably TypeScript or JavaScript).

  • Prior experience with web development or UI/UX design is a plus.

  • Minimum 2 CS courses (loose requirement).

  • Passionate about design or entrepreneurship.

  • Hard-working and open-minded people who are excited to contribute to our product

  • Commit 5-7 hours per week

p-ackUp

Co-Lead: Chau Vu PO ‘26 -

chvo2022@mymail.pomona.edu

Co-Lead: Kartika Santoso PO ‘26 -

ksni2022@mymail.pomona.edu

Team Size: 5-6

LLM-Powered Travel Planner.

In our project, we will be developing a full-stack, chat-based travel planner that utilizes the Google Gemini LLM to help users create unique trip itineraries by not only including classic tourist attractions but also featuring iconic filming locations and memorable places depicted in our favorite movies/shows.

Our system personalizes travel itineraries based on user preferences and selected destinations, creating a plan that includes famous sightseeing spots, movie scene locations, local specialties, and the best places to try them, while at the same time optimizing travel schedules considering opening hours and distance between locations. The main goal of our project is to learn how to prompt and integrate LLMs within a full-stack application!

Tech Stack

  • Languages: Python, Javascript (React JS), HTML/CSS

  • APIs: Gemini Flash 1.5

  • Tools: Git, Figma

Member Requirements:

  • Commit 5-6 hours/week, including one 1hr long team meeting.

  • Prior personal project (web development) / internship experience is a plus.

  • Frontend developers/designers: experience with Figma, HTML/CSS, Javascript/ReactJS.

  • Backend developers: experience with APIs, Python, Javascript.

  • Collaborative & eager to learn!

p-PoseLock

Lead: Sudharsan Gopalakrishnan HMC ‘27 -

sgopalakrishnan@g.hmc.edu

Team Size: 4-5

Leveraging Deep Learning and Computer Vision for Pose Estimation-Driven Biometric Authentication.

This project focuses on developing an authentication system using pose estimation and gait feature analysis. Unlike traditional biometrics, such as fingerprint scanning or facial recognition, this system identifies individuals based on their unique walking styles, which are detected using a camera aimed at the side of the individual. This provides a seamless and non-intrusive authentication method, unlike more invasive methods like fingerprint scanning.

Tech Stack

  • Languages: Python

  • Libraries/Frameworks: PyTorch, OpenCV, scikit-learn, NumPy, Pandas, Matplotlib (as necessary)

  • Pose Estimation Models: YOLO-Pose, OpenPose, AlphaPose, HRNet (Will explore models)

  • Gait Analysis Models: GaitGraph

  • Frontend: React.js, backend: Flask (will discuss skills with team in first week)

  • GitHub for version control, Jupyter Notebooks for prototyping, Google Colab for model training, Docker for deployment

Member Requirements:

  • Fluency with Python.

  • Coursework / experience in machine learning, deep learning, and data analysis.

  • Familiarity with machine learning frameworks (e.g., PyTorch).

  • Application development (React, Flask).

  • Knowledge of computer vision is a plus.

  • Experience with pose estimation is a plus.

  • 5-6 hours commitment per week

Co-Lead: Landen Isacson PO ‘27 -

lcid2023@mymail.pomona.edu

Co-Lead: Devansh Taliyan PO ‘27 -

dtte2023@mymail.pomona.edu

Team Size: 4-6

p-Recommendation

 AI-driven user recommendation system for a real world-mobile app.

Recommendation and matching algorithms are pivotal in enhancing user experiences across various platforms, including social media, dating apps, video games, and professional networking sites. Our project focuses on developing a tailored recommendation algorithm to facilitate meaningful group connections in real-world settings. This algorithm will be implemented in the in-development mobile app Yaaro, which is already live with real users and active data. Yaaro is designed to make meeting people locally and forming in-person connections more accessible. By leveraging user data and behavioral patterns, the app aims to recommend nearby individuals with shared interests.

Tech Stack:

  • Front-end:

    • React Native for mobile app development

    • TailwindCSS for UI design

    • WebSocket for real-time features

  • Back-end:

    • FastAPI (Python) for backend development

    • Sentiment analysis using a custom BERT model

    • Group matching with constrained k-means clustering

    • Real-time processing with Redis

    • User data management using PostgreSQL

Member Requirements:

  • Intro CS courses

  • Basic understanding of data science

  • Interest in ML/AI applications

  • Knowledge of machine-learning libraries is a plus

  • A fun attitude and eager to learn!!

  • 2 ~ 4 hours per week

p-ai

Lead: Asya Lyubavina, Pomona ‘26

alku2022@mymail.pomona.edu

Team Size: 4

Creating a new website + portfolio for the 5C’s biggest tech club

The goal of this project is to create a new website for the P-ai club. The old website is quite outdated and isn't organized well. In this project we would be designing, programming and deploying a new website. The new webpage for P-ai will be easier to navigate, more visually appealing, and function as a portfolio for everyone’s projects. Therefore, the club will be presented as more professional and attractive. Also, in such a way, anyone who participated in a p-ai project can showcase their work in applications by using a link from the website’s portfolio page.

Tech stacks:

  • Jekyll

  • GitHub

  • HTML

  • CSS

Member Requirements

  • Up to 4 hours a week of work, including participation in weekly meeting

  • Passionate about web design or design in general

  • Dedicated to creating quality work that will be seen/used by many other members of the 5C community

p-MarketForecast

Lead: Cole Uyematsu, Pomona ‘26

cjul2022@mymail.pomona.edu

Team Size: 4-6

Predict large swings in stock market trends.

p-MarketForecast aims to predict large swings in stock market trends by identifying significant events and analyzing their impact on financial markets. The project aims to use both news data (scraped from the web) along with financial data (volume, indicators, quarterly reports, etc.). The project addresses the challenges of anticipating market shifts. Using historical market data and news sentiment analysis, we aim to integrate machine learning models to identify patterns and forecast major market movements.

Tech Stack

  • Languages:  Python, R, SQL

  • Models: LSTM. Random Forest Regression, ARIMA, BERT

  • Libraries: TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, NLTK, spaCy, Hugging Face, yFinance

  • Compute Platforms: Cloud computing platform - AWS or Google Cloud Platforms

Member Requirements

  • 4-6 hours per week to the project.

  • An interest in applying machine learning to financial markets.

  • Willingness to collaborate and learn new tools and information as needed.

  • Proficiency in Python, familiarity with R and SQL. (preferred)

  • Knowledge of libraries used in machine learning, including scikit-learn, TensorFlow, PyTorch. (preferred)

  • Understanding of the machine learning and data processing pipeline. (preferred)

  • Previous experience with time-series forecasting and natural language processing (NLP). (preferred)

  • Familiarity with APIs for data retrieval. (preferred)

  • Prior coursework in machine learning, data analysis, or software engineering. (preferred)

p-ickup

Co-Lead: Julianne Louie PO ‘26 -

jljz2022@mymail.pomona.edu

Co-Lead: Francisco Morales Puente PO ‘26 -

fxmn2022@mymail.pomona.edu

Team Size: 7

AI powered rideshare matching.

No one wants to spend $100 on a single Uber ride to LAX, especially after already paying hundreds for a ticket home. While the Claremont Colleges sometimes offer subsidized shuttle services, these are not always available at convenient dates or times for everyone.

Our P-ickup website aims to address this issue by providing students with a platform to fill out a matching form (flight information, dates, airport preferences, etc.). The platform will feature a messaging system, rideshare cost predictions, and a machine learning model that improves its matching accuracy over time based on user ratings and feedback.

Tech Stack

  • Front-end: React.js, tailwindcss

  • Back-end: KNN model- Python, scikit, APIs- Flask, Uber/Lyft, and Aviationstack

  • Data/authentication: Supabase

Member Requirements

  • Minimum Requirements: Intro to CS/Data Science course

  • Nice to have:

    • Experience with web development either in back-end or front-end (Flask, React, tailwindcss, etc.)

    • Experience with the website design process

    • Experience with KNNs and/or other machine learning models

    • Completed an object-oriented programming or data structures course (either informally or through the claremont colleges)

    • A good attitude

    • Good documentation/collaboration skills

NOTE: You do not need experience with both ML and SWE as the teams are separate

p-Invest

Co-Lead: Alex Knight CMC ‘27 -

aknight27@cmc.edu

Co-Lead: Alex Seager HMC ‘27 -

aseager@hmc.edu

Team Size: 5-6

An AI-forward Google Chrome extension for Robinhood.

This project aims to develop a Google Chrome extension for Robinhood to enhance user understanding of investing. The extension will provide advanced insights on user trades, such as detecting overexposure to specific market sectors, and enable filtering and trend visualization. It will also create a unique user investing persona based on scraped data and leverage the OpenAI API to offer personalized, adaptive investment recommendations and guidance. The goal is to help users learn from their investing patterns, improve decision-making, and develop independent investing habits.

Tech Stack

  • Prototyping + UI Design: Figma

  • Scraping : Python, Selenium

  • Frontend : HTML, Javascript, Tailwind CSS, REACT

  • Backend : Python, PostgreSQL

  • Backend Hosting : AWS Lambda (can be integrated at the end)

  • AI : OpenAI API

  • Authentication : OAuth

  • Containerization : Docker

Member Requirements

  • Able to commit 4-6 hours per week

  • Coding applicants:

    • CS5 or equivalent

    • Experience in Python, Selenium, HTML, Javascript, Tailwind CSS, REACT, and/or PostgreSQL (preferred)

    • Computer Science major (preferred)

  • Non-coding applicants (1 spot):

    • Introductory Economics or equivalent

    • Calculus 1 or equivalent

    • Math, Economics, or Data Science major (preferred)

    • Experience with economic/statistical modeling (preferred)

    • Investing experience

Past Projects (Inactive)