John L. Hennessy, Chairman, Alphabet Inc. 
Why Attend
The AI Systems Summit, Research brings together researchers, engineers and architects from universities, HPC centers, and national labs worldwide to address the design and deployment of AI systems for scientific purposes. The summit is free to attend for attendees from public research institutions.
The summit tackles the challenges of designing and integrating HPC systems that can support ever larger and more complex AI and scientific workloads. This year’s event will dissect results from MLPerf benchmarking submissions from institutions such as NERSC, ANL, FZJ and NCSA, and discuss approaches to systems heterogeneity and design for AI. The event will also cover augmenting simulation workloads with AI, debate the merits of GPU-based architectures vs. dataflow architectures, and discuss the limitations of current AI technology for science. Finally, the summit will feature focused case studies from research institutions who are building AI capabilities into their HPC centers.
WHO WILL BE THERE
Meet Our 2022 Speakers
Natalia Vassilieva
Natalia Vassilieva is Director of Product, Machine Learning at Cerebras Systems, a computer systems company dedicated to accelerating deep learning. Her focus is machine learning and artificial intelligence, analytics, and application-driven software-hardware optimization and co-design. Prior to joining Cerebras, Natalia was a Sr. Research Manager at Hewlett Packard Labs, where she led the Software and AI group and served as the head of HP Labs Russia from 2011 until 2015. Prior to HPE, she was an Associate Professor at St. Petersburg State University in Russia and worked as a software engineer for several IT companies. Natalia holds a Ph.D. in computer science from St. Petersburg State University.
Andy Hock
Dr. Andy Hock is VP of Product Management at Cerebras Systems with responsibility for product strategy. His organization drives engagement with engineering and our customers to inform the hardware, software, and machine learning technical requirements and accelerate world-leading AI with Cerebras’ products. Prior to Cerebras, Andy has held senior leadership positions with Arete Associates, Skybox Imaging (acquired by Google), and Google. He holds a PhD in Geophysics and Space Physics from UCLA.
Gordon Hirsch Wilson
Catherine Schuman
Catherine (Katie) Schuman is an Assistant Professor at the University of Tennessee. Prior to her appointment at UoT, she was a Liane Russell Early Career Fellow at Oak Ridge National Laboratory (ORNL). She received her Ph.D. in Computer Science from the University of Tennessee in 2015, where she completed her dissertation on the use of evolutionary algorithms to train spiking neural networks for neuromorphic systems. Katie has a joint faculty appointment with the Department of Electrical Engineering and Computer Science at the University of Tennessee, where she, along with four other professors at UT, leads a neuromorphic research team made up of more than twenty faculty members, graduate student researchers, and undergraduate student researchers. Katie has over 30 publications as well as four patents in the field of neuromorphic computing. Katie has spoken on neuromorphic computing at over 25 conferences and workshops.
Dirk Van Essendelft
Dr. Van Essendelft is the principle investigator for the integration of AI/ML with scientific simulations within in the Computational Device Engineering Team at the National Energy Technology Laboratory. The focus of Dr. Van Essendelft’s work is building a comprehensive hardware and software ecosystem that maximizes speed, accuracy, and energy efficiency of AI/ML accelerated scientific simulations. Currently, his work centers around building Computational Fluid Dynamics capability within the TensorFlow framework, generating AI/ML based predictors, and ensuring the ecosystem is compatible with the fastest possible accelerators and processors in industry. In this way, Dr. Van Essendelft is developing NETL’s first cognitive-in-the-loop simulation capability in which AI/ML models can be used any point to bring acceleration and/or closures in new ways. Dr. Van Essendelft sits on the Technical Advisory Group for NETL’s new Science-Based Artificial Intelligence/Machine Learning Institute (SAMI) and holds degrees in Energy and Geo-Environmental Engineering, Chemical and Biochemical Engineering, and Chemical Engineering from the Pennsylvania State University, University of California, Irvine, and Calvin College respectively.
Recent publications:
Rocki, K., Van Essendelft, D., Sharapov, I., Schreiber, R., Morrison, M., Kibardin, V., Portnoy, A., Dietiker, J. F., Syamlal, M., and James, M. (2020) Fast stencil-code computation on a wafer-scale processor, In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp pp 1-14, IEEE Press, Atlanta, Georgia.
Christelle Piechurski
Fabio Porto
Fabio Porto is a Senior Researcher at the National Laboratory of Scientific Computing, in Brazil. He is the founder of the DEXL Laboratory, developing R&D activities in the context of scientific data analysis and management. He holds a PhD in Informatics from PUC-Rio, with sandwich at INRIA, in 2001, and a postdoc at Ecole Polytechnique Fédérale de Lausanne (EPFL). He has more than 80 research papers published in International Conferences and Scientific Journals, including VLDB, SIGMOD and ICDE. He was the General Chair of VLDB 2018 and SBBD 2015. Since 2018 he has been a member of the SBBD steering committee, and a member of SBC and ACM.
Geoffrey Fox
Fox received a Ph.D. in Theoretical Physics from Cambridge University, where he was Senior Wrangler. He is now a Professor in the Biocomplexity Institute & Initiative and Computer Science Department at the University of Virginia. He previously held positions at Caltech, Syracuse University, Florida State University, and Indiana University. after being a postdoc at the Institute for Advanced Study at Princeton, Lawrence Berkeley Laboratory, and Peterhouse College Cambridge. He has supervised the Ph.D. of 75 students. He has an hindex of 86 with over 41,000 citations. He received the High-Performance Parallel and Distributed Computing (HPDC) Achievement Award and the ACM - IEEE CS Ken Kennedy Award for Foundational contributions to parallel computing in 2019. He is a Fellow of APS (Physics) and ACM (Computing) and works on the interdisciplinary interface between computing and applications. He is currently active in the Industry consortium MLCommons/MLPerf.
Dhireesha Kudithipudi
In Fall 2019, I started as a professor with joint appointment in the Department of Electrical and Computer Engineering and Department of Computer Science at the University of Texas- San Antonio.
Before that, I enjoyed 13 years in the Department of Computer Engineering at Rochester Institute of Technology, where I was the founding director of the Center for Human-Aware AI.
My research interests are in neuromorphic computing, brain inspired AI algorithms, novel computing substrates (e.g.: memristors), energy efficient machine intelligence, and AI-Platforms. I offer consulting services to startup firms and other agencies in Neuromorphic AI field.
Murali Emani
Dr. Murali Emani is and Assistant Computer Scientist in the Data Science group with the Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory. Prior, he was a Postdoctoral Research Staff Member at Lawrence Livermore National Laboratory, US. Dr. Emani obtained his PhD and worked as a Research Associate at the Institute for Computing Systems Architecture at the School of Informatics, University of Edinburgh, UK under the guidance of Prof. Michael O'Boyle. Dr. Emani’s research interests are in Parallel programming models, High Performance Computing, Scalable Machine Learning, Runtime Systems, Emerging HPC architectures, Online Adaptation. Some of his current projects include:
- Developing performance models to identifying and addressing bottlenecks while scaling machine learning and deep learning frameworks on emerging supercomputers for scientific applications.
- Co-design of emerging hardware architectures to scale up machine learning algorithms.
- Efforts on benchmarking ML/DL frameworks and methods on HPC systems.
At ALCF, Dr. Emani also co-leads the AI Testbed where his team explores the performance, efficiency of AI accelerators for scientific machine learning applications. Dr. Emani is also serving as a chair for MLPerf HPC group at MLCommons to benchmark large scale ML on HPC systems.
James Ang
Jim is the Chief Scientist for Computing in the Physical and Computational Sciences Directorate (PCSD) at Pacific Northwest National Laboratory (PNNL). Jim’s primary role is to serve as PNNL’s Sector Lead for the DOE/SC Advanced Scientific Computing Research (ASCR) Office. At PNNL, the ASCR portfolio includes over a dozen R&D projects in computer science, applied mathematics, networking, and computational modeling and simulation. Jim also serves as the lead of the Data-Model Convergence Initiative, a lab-wide 5 year investment to develop new computer science capabilities that support integration of scientific high performance computing and data analytics computing paradigms. Through a co-design process, challenge problems that integrate scientific modeling and simulation, domain-aware machine learning, and graph analytics are used to drive the development of a supporting system software stack that maps these heterogeneous applications to conceptual designs for System-on-Chip (SoC) heterogeneous processors. A key element of this converged computing strategy is to support PNNL objectives in accelerating scientific discovery, and real time control of the power grid. Jim's prior connections to other government agencies transferred to PNNL with him and has led to PNNL and Jim's engagement in several national security programs.
Prior to joining PNNL, Jim served as the a member of the initial DOE Exascale Computing Project (ECP) leadership team from 2015-2017. Jim's role was the Director of ECP's hardware technology focus area. His primary role and responsibility was the development and definition of the DOE ECP's hardware R&D strategy. The key elements of the strategy included: 1) Establish a portfolio of PathForward vendor-led hardware R&D projects for component, node and system architecture design, and 2) Create a Design Space Evaluation team to provide ECP with independent architectural analysis of the PathForward vendors' designs and the ability to facilitate co-design communication among the PathForward vendors and the ECP's application and system software development teams.
Henrique Mendonça
Prasanna Balaprakash
Prasanna Balaprakash is a computer scientist at the Mathematics and Computer Science Division with a joint appointment in the Leadership Computing Facility at Argonne National Laboratory. His research interests span the areas of artificial intelligence, machine learning, optimization, and high-performance computing. Currently, his research focuses on the development of scalable, data-efficient machine learning methods for scientific applications. He is a recipient of U.S. Department of Energy 2018 Early Career Award. He is the machine-learning team lead and data-understanding team co-lead in RAPIDS, the SciDAC Computer Science institute. Prior to Argonne, he worked as a Chief Technology Officer at Mentis Sprl, a machine learning startup in Brussels, Belgium. He received his PhD from CoDE-IRIDIA (AI Lab), Université Libre de Bruxelles, Brussels, Belgium, where he was a recipient of Marie Curie and F.R.S-FNRS Aspirant fellowships.
Stefan Kesselheim
I want to push the boundaries of what we can do with Artificial Intelligence (AI) methods. I pursue three directions: more prior knowledge, more compute and more fascinating questions.
Prior knowledge can look very different. It can be unlabelled or or weakly labelled data (e.g. noisy or unrelated labels), physical equations or symmetries, input data statistics or something completely different and requires the AI method to be tailored to it. The large amounts of related data or a large model complexity require using the game-changing capabilities of the Jülich Supercomputing Center. Integrating prior knowledge can vastly improve the data efficiency and allows researchers to apply AI methods to even more interesting, intriguing and impactful applications from all fields of science and engineering.
Steve Farrell
Steve is a Machine Learning Engineer in the Data and Analytics Services group at NERSC. He supports machine learning and deep learning workflows on the NERSC supercomputers and collaborates with scientists for applied ML research.
Steve's background is in high energy experimental particle physics. As an undergrad in Minnesota, he worked on the MINOS experiment, SNEWS, and CLEAR. As a Ph.D. student at UC Irvine, he joined the ATLAS experiment at CERN, where he worked on searches for Supersymmetry. Finally, as a Postdoc at Berkeley Lab in the Physics Division, Steve worked on software and computing for the ATLAS experiment and machine learning R&D for HEP.
Steve maintains the Deep Learning software stack at NERSC, including Intel-optimized Tensorflow and PyTorch, scalable libraries for training such as Horovod and the Cray PE ML Plugin, and Jupyter notebook solutions for distributed ML on the Cori supercomputer. He is also compiling and maintaining a set of Deep Learning science benchmark applications for NERSC, to characterize the supercomputer systems and to guide optimization efforts to ensure that scientific applications run smoothly and efficiently. Finally, Steve provides training to the community through documentation, blog posts, workshops, and tutorials.
Volodymyr Kindratenko
I am a Senior Research Scientist at the National Center for Supercomputing Applications (NCSA), an Adjunct Associate Professor in the department of Electrical and Computer Engineering (ECE) and Research Associate Professor in the department of Computer Science (CS) at the University of Illinois at Urbana-Champaign (UIUC). I received the D.Sc. degree from the University of Antwerp (UIA), Belgium, in 1997 and the Specialist degree from the Vynnychenko State Pedagogical University (KDPU), Kirovograd, Ukraine, in 1993. Prior to becoming a Research Scientist, I was a Postdoctoral Research Associate at NCSA.
Venkatram Vishwanath
Venkatram Vishwanath is a computer scientist at Argonne National Laboratory. He is the Data Science Team Lead at the Argonne leadership computing facility (ALCF). His current focus is on algorithms, system software, and workflows to facilitate data-centric applications on supercomputing systems. His interests include scientific applications, supercomputing architectures, parallel algorithms and runtimes, scalable analytics and collaborative workspaces. He has received best papers awards at venues including HPDC and LDAV, and a Gordon Bell finalist. Vishwanath received his Ph.D. in computer science from the University of Illinois at Chicago in 2009.
Research Interests
- Scientific data analysis and visualization
- Parallel I/O and I/O middleware
- Large-scale computing systems and other exotic architectures (Blue Gene, Cray, multi-core systems, GPUs and other accelerators)
- High-speed interconnects (InfiniBand, high-speed Ethernet, optical), data movement and transfer protocols, and (v) collaboration workspaces
Wahid Bhimji
Wahid Bhimji is acting Group Lead and a Big Data Architect in the Data and Analytics Services Group at NERSC. His interests include machine learning and data management. Recently he led several projects applying AI for science including deep learning at scale, generative models and probabilistic programming. He coordinates aspects of machine learning deployment for the Lab's CS-Area and NERSC: including the upcoming Perlmutter HPC system and plans for future NERSC machines. Previously he was user lead for the commissioning of Cori Phase 1, particularly data services, and for the Burst Buffer. Wahid has worked for many years in Scientific Computing and Data Analysis in Academia and the U.K. Government and has a Ph.D. in High-Energy Particle Physics.
Rahul Gupta
Dr. Rahul Gupta has been working at the Army Research Lab for more than a decade. In his current position he is conducting research and development using Deep Learning Artificial Neural Network and Convolutional Neural Network. He joined ARL as a Distinguished Research Scholar and led several successful programs. He became a Fellow of the American Society of Mechanical Engineers in 2014. He is passionate about mentoring and team building with the goal of providing the Army the best possible technology to dominate today’s complex Multi-Domain Environment (MDE).
Prabhat Ram
Prabhat supports AI+HPC workloads with the Azure HPC team at Microsoft. In the recent past, Prabhat led the Data and Analytics Services team at NERSC and the Big Data Center collaboration between NERSC, Intel, Cray, UC Berkeley, UC Davis, NYU, UBC, Oxford and Liverpool.
Stephane Requena
Mauricio Araya
Dr. Araya is a Senior Computer Scientist and Senior Manager of the HPC & ML team at TotalEnergies E&P Research and Technology USA. He is also lecturer of the Professional Science Master’s Program of the Wiess School of Natural Sciences of Rice University. Previously, he led Machine Learning for Geophysics efforts at Shell International E&P Inc. Before that, he worked for Repsol USA and Barcelona Supercomputing Center (BSC).
Keren Bergman
Keren Bergman received a Ph.D. at M.I.T. and currently is the Charles Batchelor Professor of Electrical Engineering at Columbia University where she leads research programs at the intersection of computing and photonics. Bergman is the recipient of the IEEE Photonics Engineering Award and is a Fellow of Optica and IEEE.
WHO ATTENDS AI SYSTEMS SUMMIT RESEARCH
2022 Partners
Headline Partner
Cerebras
Website: https://www.cerebras.net/industry-pharma/
Cerebras Systems makes the world’s most powerful AI accelerator, removing roadblocks to biomedical research, drug discovery and data-driven healthcare. Our CS-2 system is doing groundbreaking work at leading institutions including GlaxoSmithKline, AstraZeneca, and Argonne National Laboratory. We offer cluster-scale deep learning acceleration in a single, easy-to-program device, so your researchers can focus on medical innovation, not on working around the limitation of traditional computing systems.
Gold Partners
Media Partners
IoT Global Network
Website: www.iotglobalnetwork.com
IoT Global Network
Discover the world of IoT.
IoT Global Network is the ultimate intelligence platform for the global machine-to-machine communication value chain.
IoT Global Network serves as an invaluable source of information for IoT decision makers in all areas of industry and public services, including consumer-related, energy, financial, industrial, healthcare, security and transportation, – and anyone else who would like to learn more about how the Internet of Things will shape tomorrow’s society.
Discover. Learn. Connect. Engage.
Enterprise AI
Website: www.enterpriseai.news/
EnterpriseAI further sharpens and expands upon the already extensive coverage originating from its predecessor, EnterpriseTech, on machine/deep learning, advanced modeling/simulation, high performance data analytics and the technologies that enable them, including; high performance data centers, cloud computing, high performance storage, AI silicon and AI frameworks – spanning all relevant verticals. With its focus trained on decision-makers, technologists and thought leaders across the technology spectrum, EnterpriseAI delivers reporting, insight and analysis on AI technologies, tools and strategies that enable business deployment and integration in the enterprise.
HPCwire
Website: https://www.hpcwire.com/
HPCwire is the #1 news and information resource covering the fastest computers in the world and the people who run them. With a legacy dating back to 1987, HPC has enjoyed more than three decades of world-class editorial and topnotch journalism, making it the portal of choice selected by science, technology and business professionals interested in HPC, AI and data-intensive computing. For topics ranging from late-breaking news and emerging technologies in HPC, to new trends, expert analysis, and exclusive features, HPCwire delivers it all and remains the HPC community’s most reliable and trusted resource.
Partnership Opportunities
The AI Systems Summit offers a rare opportunity to engage and meet with a very specific set of engineers and leaders solely from global National Labs and Research Institutes. This event gathers a group of people focused on designing, building and deploying HPC and AI systems to tackle the most arduous of AI use cases.
These organisations are looking to scout the latest technology to deploy to advance these systems, which offers a unique partnership opportunity for:
- Early-stage AI technology startups looking for applications & opportunities to prove their tech
- Mature startups with off-the-shelf technology ready to benefit scientific research
- AI systems & application builders – suppliers of HPC systems, memory solutions, software and more.
As a two day, virtual summit we can offer a host of options to engage this audience including (but not limited to):
Contact us today for more information and to build a package that will get you the results you need – [email protected]
2022 Agenda
Become a Partner
Kisaco Research provides the platform where industry executives can network, learn and meet potential industry partners.
Far from the typical ‘meet-and-greet’ experience, you – as a sponsor or exhibitor – will be positioned as a partner of the event with a focus on the benefits of your product and brand.
Your partnership with Kisaco Research will offer you a strategic approach that is an extension and enhancement of your own marketing and branding efforts. We value your ROI and will work with you directly on your specific goals and targets.
Find out more by calling us at +44 (0)20 3696 2920 or email [email protected]
PORTFOLIO TESTIMONIALS
About Kisaco Research
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