Our project focuses on intent detection through the fusion of passive and active acoustic cues by utilizing advanced deep learning models. We are leveraging multi-head attention residual convolutional neural networks (CNNs) to effectively integrate and analyze the two types of acoustic signals for accurate intent recognition.

We’re Jobiter – a team of Hopkins students building an intelligent web platform to transform the job application process for college students and new graduates. By integrating AI-powered resume and cover letter optimization, application form auto-filling, and job recommendations based on individuals’ skills and qualifications, Jobiter streamlines the repetitive, time-consuming, and often demoralizing experience of navigating ATS (Applicant Tracking System) platforms.
Through powerful automation and customization, Jobiter dramatically boosts users’ chances of advancing past ATS filters and reaching the next stage of recruiting – the interviews.

Design, build, and test a means to detect and prevent an autonomous forklift from tipping. Use the device to inform performance improvements to the forklift to safely improve speed of operation.

This project implements a real‑time engine knocking detection system on a resource‑constrained embedded platform. On‑board microphones capture combustion noise, a compact ML model classifies “knock” events in‑situ, and summarized diagnostics are pushed securely to a cloud backend for visualization, alerts and analytics. By enabling predictive maintenance and continuous performance monitoring, it helps prevent engine damage and optimize operational uptime.