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.
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.
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.
Murali Emani
Murali Emani is a Computer Scientist in the Data Science group with the Argonne Leadership Computing Facility (ALCF) at Argonne National Laboratory. At ALCF, he co-leads the AI Testbed where they explore the performance, efficiency of novel AI accelerators for scientific machine learning applications. He also co-chairs the MLPerf HPC group at MLCommons, to benchmark large scale ML on HPC systems. His research interests are in Scalable Machine Learning, AI accelerators, AI for Science, and Emerging HPC architectures. His current work includes
- 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 workloads.
- Efforts on benchmarking ML/DL frameworks and methods on HPC systems.
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.
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.
