Please note: This seminar will be given online.
Hong Zhang, Postdoctoral Scholar
Department of Electrical Engineering and Computer Sciences
University of California, Berkeley
The world is undergoing a data revolution. Emerging big data and ML applications are harnessing massive volumes of data to uncover hidden patterns, correlations, and other valuable insights, transforming information and knowledge production. As the data volume keeps growing explosively, these applications require high-performance big data and ML systems to efficiently transfer, store, and process data at a massive scale.
In this talk, I advocate an application-oriented principle to design big data and ML systems: fully exploiting application-specific structures — communication patterns, execution dependencies, ML model structures, etc. — to suit application-specific performance demands. I will present how I have developed the application-oriented principle throughout my thesis and postdoc research, and how I have applied the principle to build systems tailored for different big data and ML applications.
Bio: Hong Zhang is currently a Postdoctoral Scholar at UC Berkeley working with Ion Stoica. He received his Ph.D. degree in Computer Science and Engineering from HKUST in 2017. Hong is broadly interested in computer systems and networking, with special focuses on distributed data analytics and ML systems, data center networking, and serverless computing. His research work appeared in prestigious systems and networking conferences, such as SIGCOMM, NSDI, and EuroSys. He has been awarded the Google Ph.D. Fellowship in systems and networking.