Vivienne Sze

Vivienne Sze is a Professor at MIT's Department of Electrical Engineering and Computer Science (EECS), part of the School of Engineering. Her research focuses on energy-efficient computing systems for machine learning, computer vision, and video compression, with applications in autonomous systems, healthcare, and IoT. She leads projects integrating algorithmic innovations with hardware design to achieve low-power solutions for embedded and mobile devices.

Her work has been recognized through prestigious awards, including the Primetime Engineering Emmy Award for co-developing the HEVC video compression standard and multiple faculty awards from tech giants like Google and Qualcomm. She co-authored the book *Efficient Processing of Deep Neural Networks*, emphasizing practical hardware-software co-design strategies.

Research Interests:

• Energy-Efficient Machine Learning Accelerators
• Video Coding and Compression Standards
• Embedded Systems and Mobile Computing
• Processing-in-Memory (PIM) Architectures
• AI for Health Monitoring and Digital Health
• Sustainability in AI Infrastructure

Publications highlight trends in:

• Optimizing DNNs for edge devices
• Innovations in entropy coding and CABAC
• Memory-efficient Gaussian-based algorithms
• Energy-aware design for photonic computing

Awards include IEEE conference best paper awards and industry recognitions for her contributions to video coding and hardware acceleration. Her lab's collaborative efforts span academia and industry, aiming to bridge theoretical research with real-world deployable systems.