Chris Peterson
Mr. Pushpanathan Sundram
Faustino Gomez
American by birth, Gallego at heart, Faustino’s career in AI started with a fascination for how the principles of natural selection could be harnessed to train neural networks for autonomous control tasks. Shortly after receiving his PhD in artificial intelligence from the University of Texas at Austin in 2003, Faustino joined the Swiss AI Lab, IDSIA, initially as a post-doctoral researcher and then senior researcher working with Dr. Juergen Schmidhuber. There he continued his pioneering work in evolutionary reinforcement learning to solve complex autonomous control problems and published more than 50 papers in the fields of neural networks, evolutionary computation, machine learning, and reinforcement learning. In the fall of 2014, he co-founded NNAISENSE with his IDSIA colleagues to develop and deploy large-scale neural network solutions for industrial inspection, modelling, and control.
Matthew Burns
Matthew Burns develops go-to-market strategies for Samtec’s Silicon to Silicon solutions. Over the course of 20+ years, he has been a leader in design, technical sales and marketing in the telecommunications, medical and electronic components industries. Mr. Burns holds a B.S. in Electrical Engineering from Penn State University.
Thang Tran
Dr. Thang Tran, Principal Architect of Andes Technology Corp. and veteran of many high-performance computing (HPC) designs. Dr. Tran has engineered innovative CPUs at Intel, AMD, Freescale, TI, Analog Devices among others. His doctoral thesis at the University of Texas at Austin from the Electrical and Computer Engineering Department was on Superscalar Microprocessor Architecture with Multi-Bit Scoreboard Technique.
John Min
John Min is the Director of Field Applications Engineering for North America at Andes. John has been working for processor companies in the Silicon Valley for past 30 years with companies including at Hewlett Packard, LG, ARC, MIPS and SiFive. He brings wealth of information on Architecture of processors, IP and high performance processing. John specializes in balancing the Power, Area and Performance to yield optimized SoC. John is a graduate of University of Southern California with degrees in Electrical Engineering and Biomedical Engineering.