Dimple Kochar

Dimple Kochar is a graduate student in electrical engineering and computer science, where she is advancing AI-driven circuit design and energy-efficient hardware. Her research addresses two pressing challenges in modern electronics: optimizing analog and mixed-signal circuit design through automation and building ultra-low-power chips that support real-time AI workloads. As a MathWorks Fellow, Dimple will develop new machine learning frameworks for the automated design and characterization of analog-to-digital converters. MATLAB is a central component of Dimple’s work. She uses its tools for signal processing, neural network prototyping, spectral analysis, and hardware performance modeling, including FFT-based SNDR calculations and histogram-based nonlinearity assessments. By combining AI with circuit innovation, Dimple’s work could transform electronic design automation, enabling more accessible, efficient, and scalable chip development. This, in turn, could pave the way for next-generation devices in healthcare, communication, and beyond.