Hao Zhuang, Ph.D.
I earned my Ph.D. degree in Computer Science from University of California, San Deigo, CA, USA (UCSD) in 2016 after 3-year full-time PhD training and 1-year part-time training while I was a software engineer at ANSYS, Inc. I co-authored 15 peer-reviewed papers during this period and had around 210 (total) paper citations by the graduation. My graduate research included numerical algorithms, matrix computation, and stochastic numerical solvers, with the applications in the analysis of large-scale dynamical systems, computer-aided design (CAD), and electronic design automation (EDA) for building computer chips. Some of the techniques I developed with my awesome colleagues during my graduate research (both my master and PhD) had been adopted in the industry. Now, I am generally interested in the fundamental algorithms behind computational mathematics, numerical algorithms, high-performance computing (HPC), and distributed computing systems for simulation, optimization, signal processing, large-scale dynamical systems analysis, reasoning and ranking.
In addition, I believe the education matters to our society and we should make use of the available technologies to help ourselves. Therefore, during my Ph.D. period, beyond building computation engines for design automation tools of VLSI chips, I contributed to the projects in my spare time, which created online collaboration and personalized systems in order to make education massive and accessible to the world. I worked with (computational) linguists, teachers, and professors. We built those systems with the help of the technologies from natural language processing, computational linguistics, machine learning, numerical algorithms, and matrix computation.
I am now a Senior Software Developer at Apache Design (apache-da.com, originally a computer-aided design start-up at Silicon Valley, providing the industrial standard of VLSI chip power sign-off and accquired by ANSYS, Inc.) of ANSYS to commercialize my research at graduate schools. I am also co-leading the simulation-based machine learning research in Apache Design. (The products have been used for power analysis and verification of tape-outs of modern low power CPU, GPU, and ASIC, which are used by different purposes of computing, such as general logic operations, deep learning, big data processing, data center traffic, etc.) Since June 2015, I write software with my Apache R&D colleagues to build distributed computation infrastructure, large-scale matrix computation algorithms, machine learning systems, and big data applications. It is my privilege to work with the teams consist of legends in the area of design automation algorithms, such as the forerunner and researchers of AWE, the creators of MIT FastCap, CMU PRIMA, UT RICE, Synopsys PrimeTime, and Apache Redhawk. Before that I worked at Synopsys, Inc., designed graph partitioning algorithms and multi-threaded programs during that summer internship. The algorithm in that summer scaled up the matrix solver for full-chip power network analysis with over billion graph nodes.
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