Design and implement AI-driven hardware solutions for wearables, autonomous systems, and next-gen semiconductor optimization.
Discover how AI is reshaping the future of hardware—from smart wearables and autonomous systems to next-generation chip design. In this hands-on workshop, you’ll explore how AI enhances edge computing, sensor integration, and system performance across real-world devices. Learn how engineers are optimizing energy efficiency, real-time perception, and semiconductor design through AI-driven innovation. Perfect for students interested in robotics, embedded systems, or hardware design, this session bridges machine learning with physical systems to power the next era of intelligent devices.
Speaker
Robin Singh
MIT AI & Hardware Researcher and Industry Expert
He conducted research at MIT, focusing on integrated photonic devices for deep brain neuroimaging, biosensing, and AI-driven healthcare applications. His work explores the intersection of machine learning, optical design, and sensor technology. He has authored 14+ journal publications, including in Nature, ACS Sensors, and APL, along with 5 patents and 10 conference proceedings. With 4+ years of industry experience at a BigTech company, he is developing next-generation wearable AI devices for AR/VR technology. His goal in leading this PBL track is to equip students with the most cutting-edge industry skills, emphasizing: AI isn’t replacing jobs—it’s redefining them."
Agenda
Speaker Introduction & Background
AI Hardware and Edge Computing
AI-Driven Optimization for Smart Devices
Reinforcement Learning in Semiconductor and Chip Design
Autonomous Systems and Sensor Fusion
Wrap-Up: AI+X Programs & Emerging Hardware Pathways
Related Project
https://program.blendedlearn.org/pbls/ai-in-hardware