GPU Techology Conference 2016

About the Speaker: Arthur van Hoff is a serial entrepreneur and was most recently CTO at Flipboard. He started his career in Silicon Valley at Sun Microsystems where he was an early developer of the Java programming language. Since then he has started several successful companies including Marimba (IPO 1999), Strangeberry (acquired by TiVo), ZING (acquired by Dell), and Ellerdale (acquired by Flipboard). Arthur has expertise in machine learning, big data, mobile applications, 3D printing, and computational photography. He is originally from the Netherlands and has a master's degree in Computer Science from Strathclyde University in Glasgow.

About the Speaker: Ian is a Senior Research Scientist. He is the lead author of the textbook Deep Learning (www.deeplearningbook.org). He studies new methods to improve deep learning. His interests include generative models, machine learning in the adversarial setting, and accelerating the training of neural networks. He has contributed to several open source machine learning libraries that leverage CUDA, including Theano, Pylearn2, and TensorFlow.

About the Speaker: Bryan Catanzaro is a research scientist at Baidu's Silicon Valley AI Lab, where he leads the systems team. His research is focused on efficient tools and methodologies for training and deploying large deep neural networks. Before joining Baidu, Bryan was involved in popularizing GPUs for machine learning while working at NVIDIA, including the creation of CUDNN. Bryan received his PhD from the University of California at Berkeley, where he wrote the first Support Vector Machine training library to run on Graphics processors, and created Copperhead, a Python-based DSL for parallel programming.

About the Speaker: Chris Huybregts drives the strategy and vision for Microsoft's Virtual GPU technology stack. By working with teams like HyperV and Azure, he's helping ensure the capabilities of GPUs in different virtualized environments meet the expectations of Microsoft's technology partners. Additionally, Chris owns the story around Microsoft's virtualized, GPU-accelerated visualization technology stack found in Remote Desktop.

About the Speaker: Clement Farabet founded Madbits, which was acquired by Twitter, building core deep learning stack at Twitter Cortex. He has a Ph.D. in deep learning with Yann LeCun at New York University.

About the Speaker: Jeremy Cowles is the GPU team lead at Pixar, where he has contributed to Universal Scene Description, OpenSubdiv, and Pixar's animation system for Presto. He is also the co-architect of Hydra, Pixar's real-time render engine for film assets. Jeremy and his team will present Presto's next generation hybrid GL/path traced viewport architecture and the future of Presto real-time workflows, where these technologies come together.

About the Speaker: John Stone is a senior research programmer in the Theoretical and Computational Biophysics Group at the Beckman Institute for Advanced Science and Technology, and associate director of the NVIDIA CUDA Center of Excellence at the University of Illinois. John is the lead developer of VMD, a high-performance molecular visualization tool used by researchers all over the world. His research interests include molecular visualization, GPU computing, parallel processing, ray tracing, haptics, and virtual environments. John was named an NVIDIA CUDA Fellow in 2010. In 2015, he joined the Khronos Group Advisory Panel for the Vulkan graphics API. John also provides consulting services for projects involving computer graphics, GPU computing, and high performance computing.

About the Speaker: Rajesh Bordawekar is a Research Staff Member at the IBM T. J. Watson Research Center. His current interest is exploring software-hardware co-design of analytics workloads. He works at the intersection of high-performance computing, analytics, and data management domains. He has been investigating how GPUs could be used for accelerating key analytics kernels in text analytics, data management, graph analytics, and deep learning. As part of this work, he collaborates closely with the IBM Power Systems, and various analytics and database product teams. He is currently leading a team that is exploring applications of GPUs for accelerating key Spark workloads.

About the Speaker: Simon is the lead for scientific computing at SKA South Africa. Overall design and architecture for the high performance computing systems on the MeerKAT radio telescope. Core member of the international SKA Science Data Processor consortia that is currently designing the compute backend for the Square Kilometer Array, which will be one of the largest scientific facilities in the world.

About the Speaker: Michela Taufer joined the University of Delaware in 2007, where she was promoted to associate professor with tenure in 2012. She earned her M.S. in computer engineering from the University of Padova and her Ph.D. in computer science from the Swiss Federal Institute of Technology (ETH). She was a post-doctoral researcher supported by the La Jolla Interfaces in Science Training Program (also called LJIS) at UC San Diego and the Scripps Research Institute. Before she joined the University of Delaware, Michela was faculty in computer science at the University of Texas at El Paso.

About the Speaker: Stephen Jones leads the Simulation and Analytics group at SpaceX, where he works on various projects including large-scale simulation of combustion processes in rocket engines. He previously worked at NVIDIA, where he was the architect for the CUDA language and worked closely with NVIDIA's hardware designers to develop new GPU features in support of parallel programming. His background is in computational fluid mechanics and plasma physics, but he has worked in diverse industries, including networking, CAD/CAM, and scientific computing.

About the Speaker: Thomas is Director of the Swiss National Supercomputing Centre (CSCS) and a professor for computational physics at ETH Zurich. He received his PhD in physics in 1994. Since 2010 he has taken interest in refactoring climate codes to take advantage of novel, energy efficient computing architectures.

About the Speaker: H. Carter Edwards has over three decades of experience developing software for simulations of a variety of engineering domains. He is an expert in high performance computing (HPC) and is currently focusing on thread-scalable algorithms and data structures for heterogeneous manycore architectures such as NVIDIA GPU, AMD Fusion, and Intel Xeon Phi. He has a B.S. and M.S. in aerospace engineering from the University of Texas at Austin, and worked for 10 years at the Johnson Space Center in the domain of spacecraft guidance, navigation, and control. He has a Ph.D. in computational and applied mathematics, also from the University of Texas at Austin. He has been researching and developing software for HPC algorithms and data structures for the past 16 years at Sandia National Laboratories.

About the Speaker: Julie is a Senior Solutions Architect for Deep Learning at NVIDIA. She attended ENS Cachan from 2001 to 2004 where she received a degree in Physical Chemistry. She obtained her PhD from Université Paris-Sud in 2006 while performing research in the Yeast Structural Genomics group. Her thesis focused on the use of Voronoi models for modelling protein complexes. After a post-doctoral position at Stanford University with Pr. Michael Levitt, Nobel Prize in Chemistry 2013, she joined Inria, the French National Institute for Computer Science. While Senior Research Scientist at Inria, Adjunct Associate Professor of Computer Science at École Polytechnique and Visiting Research Scientist at SLAC, her work focused on computational methods for structural bioinformatics, specifically scoring functions for macromolecular docking using machine learning, and statistical potentials for molecular simulations. She was the first to successfully introduce machine learning for coarse-grained models in the CAPRI challenge.

About the Speaker: Phillip Miller is senior director of NVIDIA's commercial advanced rendering offerings, ranging from the Iray and mental ray shipping within leading products in Design and Entertainment to the IndeX technology used in large data visualization. Phil has been with NVIDIA for 7 years and has led leading software products for over 20 years, including the Entertainment offerings at Autodesk and the Web Design product line at Adobe. He holds a Masters of Architecture from the University of Illinois and is a registered architect.

About the Speaker: Ben Barsdell completed his Ph.D. in astronomy at Swinburne University, where he studied the application of GPUs to a number of widely used simulation and data processing algorithms. In 2013, he moved to the U.S. to take up a post-doctoral position at the Harvard-Smithsonian Center for Astrophysics, where he worked on GPU algorithms and digital back-ends for large-N radio telescope arrays. He joined NVIDIA in 2015, where he works on GPU-accelerated astronomy and deep learning applications.

About the Speaker: Mark Harris is Chief Technologist for GPU Computing at NVIDIA, where he works as a developer advocate and helps drive NVIDIA's GPU computing software strategy. His research interests include parallel computing, general-purpose computation on GPUs, physically based simulation, and real-time rendering. Mark founded www.GPGPU.org while he was earning his Ph.D in computer science from the University of North Carolina at Chapel Hill. Mark lives completely off grid out the back of Byron Bay, Australia with his family, some domestic animals, and a variety of wildlife.

About the Speaker: Jon Barker is a solution architect with NVIDIA, helping customers and partners develop applications of GPU-accelerated machine learning and data analytics to solve defense and national security problems. He is particularly focused on applications of the rapidly developing field of deep learning. Prior to joining NVIDIA, Jon spent almost a decade as a government research scientist within the U.K. Ministry of Defence and the U.S. Department of Defense R&D communities. While in government service, he led R&D projects in sensor data fusion, big data analytics, and machine learning for multi-modal sensor data to support military situational awareness and aid decision making. He has a Ph.D. and B.S. in pure mathematics from the University of Southampton, U.K.

About the Speaker: Christoph Kubisch is a senior developer technology engineer for NVIDIA, where he focuses on advanced OpenGL and Vulkan real-time rendering techniques suitable for CAD/DCC and scientific applications. He collaborates with external partners and NVIDIA's internal teams to optimize rendering algorithms. Prior to joining NVIDIA, he was a researcher on hardware-accelerated visualization techniques for medical datasets at the Otto-von-Guericke University of Magdeburg. He has also worked as a technical artist creating game art, technology, and tools.

About the Speaker: Markus finished his studies in computer science with focus on computer graphics in 2008. He was one of the first using ray-tracing on CUDA for this diploma thesis which brought him straight to NVIDIA. There he primarily worked on GPU raytracing for SceniX, NVIDIA's scene-graph technology, which had been showcased at SIGGRAPH 2008. Afterwards he applied his experience to implement parts of OptiX, improve SceniX and develop several ray-tracing demos. In close cooperation with external partners he improved rendering performance and scenegraph usability as developer technology engineer. Now he is using the gained knowledge to experiment with future rendering technologies that bring high interactivity to complex scenes. This work includes both CPU and GPU strategies to solve typical scene-graph operations related to rendering.

About the Speaker: Everett Phillips is a senior engineer in the Tesla Performance group at NVIDIA, where he works on high performance computing applications and benchmarks for GPU-accelerated supercomputers. He holds an M.S. in mechanical and aeronautical engineering from University of California, Davis.

About the Speaker: Shalini Gupta has been a senior research scientist in the Mobile Visual Computing group of NVIDIA Research since April 2013. From 2011 to 2013, she worked as a senior mobile computer vision engineer at NVIDIA, where she designed and productized computer vision and computational photography solutions for mobile platforms and GPUs. She worked as an imaging and architecture scientist at Texas Instruments, from 2008 to 2010, where she designed algorithms for the image signal processing pipeline of mobile phones, at AT&T Laboratories on their IPTV project, and at Advanced Digital Imaging Research, LLC, where she designed algorithms for 3D human face recognition. Shalini received her M.S. and Ph.D. in electrical and computer engineering from the University of Texas at Austin in 2004 and 2008, respectively. She received a B.S. in electronics and electrical communication engineering from Punjab Engineering College, India, in 2002. She is a recipient of the Summer Research Fellowship 2001, awarded by the Jawaharlal Nehru Center for Advanced Scientific Research, Bangalore, India. Her primary research interests are image/signal processing, computer vision, and machine learning, and their application to scene understanding and interpretation.

About the Speaker: Tom is a Senior Applied Engineer at NVIDIA where he works with developers to optimize the designed and implementation of GPU-based professional video broadcast, digital post production and large scale multi-GPU multi-display visualization systems. Tom has a Bachelor of Science degree from the Rochester Institute of Technology and a Master of Science degree from the graphics lab at Brown University. Prior to joining NVIDIA, Tom was an application engineer at Silicon Graphics.

About the Speaker: Peter Messmer is a senior software engineer in NVIDIA's Developer Technology organization, working with clients to accelerate their scientific discovery process with GPUs. One area of his current research is to investigate how to utilize the GPUs in high performance computing systems for data analysis and visualization. Prior to joining NVIDIA, Peter spent more than 15 years developing HPC- and GPU-accelerated applications for industry and government clients, ranging from simulating next-generation particle accelerators or electromagnetic problems to modeling the behavior of dust on the surface of the Moon. Peter holds an M.S. and Ph.D. in physics from ETH Zurich, Switzerland, with specialization in kinetic plasma physics and nonlinear optics.

About the Speaker: Stefan Schoenefeld is a senior developer technology engineer at NVIDIA. After working on the Scenix Scene Graph SDK and the Workstation Performance Drivers, he now works on implementing NVIDIA GRID, improving video encoding and new ideas for remote graphics.

About the Speaker: Rajesh Bordawekar is a Research Staff Member at the IBM T. J. Watson Research Center. His current interest is exploring software-hardware co-design of analytics workloads. He works at the intersection of high-performance computing, analytics, and data management domains. He has been investigating how GPUs could be used for accelerating key analytics kernels in text analytics, data management, graph analytics, and deep learning. As part of this work, he collaborates closely with the IBM Power Systems, and various analytics and database product teams. He is currently leading a team that is exploring applications of GPUs for accelerating key Spark workloads.