Event Networking System and Visualization

The project aims to develop an AI-driven event matchmaking and visualization engine designed to enhance networking experiences at events. The system will suggest potential connections for attendees based on their goals, roles, and shared topics of interest. By integrating with event data and attendee lists or LinkedIn profiles, the system will provide personalized recommendations. A key feature of the project is the development of a visual cluster map, which will help attendees easily identify and approach potential connections, making networking less awkward and more efficient. This project provides an opportunity for learners to apply their knowledge of AI, data integration, and data visualization to create a practical solution for real-world networking challenges.

Website Development for FindGrant

We would like to work with students to develop a new website that is easy to maintain while providing an appealing interface for users. This can be achieved through modern site building tools with e.g. Expo, Javascript and HTML This will involve several different steps for the students, including: Building a website, with our assistance in providing the content and guidance. Providing training on updating and maintaining the website. Bonus steps in the process would also include: Testing prototypes with customers and refining ideas with feedback.

LinkedIn Data-Driven Event Attendee Profiling

We aim to enhance the networking experience at PopIn events by leveraging LinkedIn data to create detailed attendee profiles. The goal is to provide event participants with insights into fellow attendees, facilitating more meaningful connections. This project involves extracting and analyzing LinkedIn data to generate comprehensive profiles that highlight professional backgrounds, skills, and interests. By doing so, we seek to improve the overall engagement and satisfaction of event attendees. The project will require learners to apply their knowledge of data analysis, data privacy considerations, and profile creation techniques. The tasks will include data extraction, data cleaning, and profile generation, all of which are closely related and manageable within the given timeframe.

AI-Driven Event Matcher

LetsPopIn.com aims to enhance user experience by implementing an AI-based event matching system for users to events and with other users at that event. The current challenge is to efficiently connect users with events that align with their interests and preferences and matching them with others present. The goal of this project is to develop a prototype algorithm that can analyze user data and event characteristics to provide personalized event recommendations. This will involve understanding user behavior, preferences, and historical data to create a model that predicts the best event matches. The project will allow learners to apply their knowledge of machine learning, data analysis, and algorithm development. The tasks will include data collection, feature engineering, model training, and evaluation. The project is designed to be completed by a team of learners specializing in data science or computer science within a single academic program.