My teaching aims to build strong fundamentals to translate classroom concepts into real engineering outcomes. Courses are structured around problem solving, case studies, and research-informed examples, so students develop skills that are directly useful for industry and advanced R&D roles.

Winter 2026: Emerging Memory Devices for Neuromorphic Computing (ECE 614: EMDNC)

EMDNC is an industry and research relevant elective course focused on technologies driving modern AI hardware and low-power computing. Students will learn the fundamentals and practical aspects of resistive, phase change, ferroelectric and magnetic memories. The course emphasizes key metrics including energy, latency, endurance, variability, and scalability to guide real-world technology and product decisions.The course connects devices to circuits and architectures, and highlights how industry evaluates technology readiness for edge AI, IoT, and next-generation accelerators. 

This is a very useful course for B.Tech/M.Tech/Ph.D. students targeting hardware, semiconductor, and AI-systems roles in industry and R&D research lab.