Senior Project 2026
An Intelligent Team Announcement Aggregation System
I am a dedicated developer driven by a constant desire to learn and evolve. I believe that true growth happens at the edge of one's expertise, which is why I actively seek out new challenges that push me to leave my comfort zone.
I am a developer focused on building efficient systems. Collaborating on Axiom allowed me to explore advanced aggregation logic and user-centric design to solve real-world communication gaps.
Team members are part of numerous project-specific group chats where task assignments are mixed with social conversations, questions, and other noise. Manually sifting through hundreds of messages to find important deadlines is time-consuming and prone to error. Existing solutions like general-purpose calendar apps require manual entry. This creates a significant gap in the team's operational workflow, leading to decreased productivity and increased project risk.
Axiom centralizes announcements into an intelligent dashboard, ensuring that every team member stays informed through real-time aggregation and smart filtering. The system consists of a chatbot that integrates with popular messaging platform to parse conversations in real-time, a Natural Language Processing (NLP) engine to identify and classify task-relevant messages, and a mobile application that presents formatted tasks within a personalized to-do list.
Axiom is designed with a "Privacy by Design" approach. Since the system parses group chat messages, transparency in data usage is our primary priority. The Discord bot is programmed to strictly process messages only in specific channels where it has been explicitly authorized by a server administrator. No unauthorized data scraping or cross-server data sharing occurs within the application architecture.
Axiom leverages the power of open-source technologies and pre-trained models (BERT). We maintain a commitment to integrity by ensuring all custom logic, wrappers, and integration code are original works. Proper licensing and attribution are maintained for all external assets, adhering to the standard open-source community guidelines and academic honesty policies.
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