AI-MI with BEAT+
AI-MI partners with the BEAT+ Capstone Program at City College of New York, where undergraduate teams build AI-powered assistive technology for real users.
Student teams design assistive technology with AI, data analytics and app development.
AI-MI is partnering with the BEAT+ Program (Adding Branding and Entrepreneurship for Real-World Applications Using Emerging Technologies) in collaboration with the Zahn Innovation Center, a capstone project started in 2010 at CCNY. BEAT+ is a program for undergraduate seniors in computer science related fields.
Participants work in teams to design assistive technology solutions using AI, data analytics and app development skills to help people in need. Teams learn how to design user interfaces, and communicate with end users, community partners and external collaborators about their technical solutions. AI-MI is broadening the scope of the BEAT+ program, supporting participants each year from both the BEAT+ cohort and students from CS and materials backgrounds.
Student Research at IEEE URTC 2026
Three BEAT+ student teams presented AI-MI–supported papers at the 2026 IEEE MIT Undergraduate Research Technology Conference (URTC) in Cambridge, Massachusetts, October 9–11, 2026.
CCNY BEAT+ students at IEEE URTC 2026, MIT.
Auto-OPDx: Automated Profilometry Data Analysis for High-Throughput Experimentation
An open-source pipeline that analyzes Bruker Dektak profilometry scans of polymer brush arrays fabricated by Hypersurface Photolithography. It cuts the analysis of an 8×8 patterned substrate from more than half an hour of manual work to about 2 seconds, and removes operator bias.
Christopher Jeong, Adam Braunschweig and Zhigang Zhu
EquiLearn: BLV Math Learning with Haptic Feedback, Audio Sonification and AI Assistance
An iOS app that helps blind and low-vision college students explore graphs and math concepts independently through vibration and sound on an iPhone or iPad. It includes EquiChat, an AI assistant for explanations, and a math keyboard built entirely around VoiceOver.
Cindy Zheng, Aditya Jha and Zhigang Zhu
ScribeAI: A Course-Aware AI Lecture Companion with Retrieval-Augmented Generation
A free, open-source, real-time lecture companion that combines live transcription, an AI chatbot grounded in the course’s own materials, automated summaries and quiz generation. It was co-designed with the CCNY AccessAbility Center; in a survey of 12 CCNY students, all four core features averaged above 4.0 out of 5.
Daniel Chen, Jia Xiang Zhang and Zhigang Zhu
Full abstracts and more about each project are in the BEAT+ at URTC 2026 summary. Learn more about the program on the BEAT+ Capstone Program website.