Vivienne Sze is an Associate Professor at MIT in the Electrical Engineering and Computer Science Department. Her research interests include energy-aware signal processing algorithms, and low-power circuit and system design for portable multimedia applications, including computer vision, deep learning, autonomous navigation, and video coding. Prior to joining MIT, she was a Member of Technical Staff in the R&D Center at TI, where she designed low-power algorithms and architectures for video coding. She also represented TI in the JCT-VC committee during the development of High Efficiency Video Coding (HEVC), which received a Primetime Emmy Engineering Award. She is a co-editor of the book entitled “High Efficiency Video Coding (HEVC): Algorithms and Architectures” (Springer, 2014).

Prof. Sze is a recipient or co-recipient of the 2011 Jin-Au Kong Outstanding Doctoral Thesis Prize in Electrical Engineering at MIT, the 2017 CICC Outstanding Invited Paper Award, the 2017 Qualcomm Faculty Award, the 2016 IEEE Micro Top Picks Award, the 2016 Google Faculty Research Award, the 2016 AFOSR Young Investigator Award, the 2016 3M Non-Tenured Faculty Award, the 2014 DARPA Young Faculty Award, the 2008 A-SSCC Outstanding Design Award and the 2007 DAC/ISSCC Student Design Contest Award.

My research has focused on power and energy in large-scale computing: modeling, measurement, management, and design at the system level. I'm currently Chair of CS at Sonoma State University, a campus of the California State University system. Ph.D. students who are interested in teaching at undergraduate institutions are welcome to contact me!

I recently graduated from Penn State University. My research at Penn State was focused on memory and storage architecture. I worked on NAND flash solid state drives on cloud and enterprise environments. I also did an internship at Memory Solution Lab in Samsung Semiconductor where I worked on open channel solutions for SSD storage architectures. I am now a Software Engineer at Google platform team working on system drivers for cloud storages.

I am an Assistant Professor in the Computer Science and Engineering Department at University of California, San Diego. My research spans and stretches the boundary between computer architecture and system software, with an emphasis on memory and storage systems and domain-specific acceleration. I am particularly interested in architecting emerging memory technologies (e.g., nonvolatile memories and 3D-stacked memory) into future data centers and edge computing systems that execute various applications, e.g., machine learning, big-data analytics, smart home, and smart transportation. I am also interested in developing deep learning to help computer systems design and programming.