Cybersecurity Data Collection Research Assistant

The main objective of the project, focusing on cybersecurity data collection and labeling for vulnerability management, is to enhance the understanding and management of cybersecurity vulnerabilities. This project aims to engage students in the crucial task of gathering, analyzing, and labeling data related to cybersecurity threats and vulnerabilities. Problem to Solve: Students will be tasked with addressing the challenge of identifying and categorizing various types of cybersecurity vulnerabilities. This involves the collection of vast amounts of data from different sources, such as security logs, network traffic, and public vulnerability databases. The key challenge lies in accurately analyzing and interpreting this data to identify potential vulnerabilities. Expected Outcome: By the end of the project, students are expected to achieve the following outcomes: 1. Comprehensive Data Collection: Students should be able to gather relevant cybersecurity data from multiple sources systematically. This includes understanding where to find data and how to extract it efficiently. 2. Effective Data Labeling and Categorization: Students should develop skills to accurately label and categorize the collected data based on the type and severity of the vulnerabilities. This involves understanding different types of cybersecurity threats and their characteristics. 3. Vulnerability Analysis Skills: Students should be able to analyze the labeled data to identify patterns or trends that could indicate potential security vulnerabilities or breaches. 4. Reporting and Documentation: Students should be able to document their findings in a clear and concise manner, providing insights and recommendations for vulnerability management and mitigation strategies. 5. Awareness of Ethical and Legal Considerations: Students should understand and adhere to ethical and legal standards in data handling, particularly regarding sensitive or personal information. 6. Collaborative Skills: Given the complexity of cybersecurity, students should also learn to collaborate effectively, sharing insights and combining expertise to tackle multifaceted problems. The success of this project lies in its ability to equip students with practical skills in handling real-world cybersecurity challenges, enhancing their ability to identify, analyze, and manage cybersecurity vulnerabilities in various environments.

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.

AI Prompt Engineering for Students and Teachers

The goal of this project is to develop a set of AI prompts tailored specifically for the education sector, targeting teachers, students, and school managers. EmpowerC aims to enhance the educational experience by leveraging AI to facilitate communication, learning, and administrative tasks. The project involves researching the unique needs of each stakeholder group and crafting prompts that address these needs effectively. Learners will apply their knowledge of AI, education, and user experience design to create prompts that are intuitive and impactful. The project will focus on creating prompts that can be used in various educational scenarios, such as classroom management, personalized learning, and administrative decision-making. By the end of the project, the team will have developed a comprehensive set of AI prompts that can be tested and refined for real-world application.

Carbon Mutual Backend Prototype

The project involves designing and prototyping a lightweight, scalable backend for Carbon Mutual's Minimum Viable Product (MVP). The backend must support essential functionalities such as user authentication, data storage, and integration with external APIs. These APIs may include carbon footprint databases and payment or loyalty middleware. The primary goal is to create a backend that is suitable for early-stage testing and iteration, allowing Carbon Mutual to refine its offerings based on user feedback and performance metrics. The project will provide learners with an opportunity to apply their knowledge of backend development, database management, and API integration in a real-world context. The tasks are closely related and can be completed by a team of learners specializing in computer science or software engineering.