Jean-Pierre Hubaux
Jean-Pierre Hubaux is a full professor at EPFL and head of the Laboratory for Data Security. Through his research, he contributes to laying the foundations and developing the tools for protecting privacy in today’s hyper-connected world. He has pioneered the areas of privacy and security in mobile/wireless networks and in personalized health.
He is the academic director of the Center for Digital Trust (C4DT). He leads the Data Protection in Personalized Health (DPPH) project funded by the ETH Council and is a co-chair of the Data Security Work Stream of the Global Alliance for Genomics and Health (GA4GH). From 2008 to 2019 he was one of the seven commissioners of the Swiss FCC. He is a Fellow of both IEEE (2008) and ACM (2010). Recent awards: two of his papers obtained distinctions at the IEEE Symposium on Security and Privacy in 2015 and 2018. He is among the most cited researchers in privacy protection and in information security.
Spoken languages: French, English, German, Italian
Lawrence Lundy-Bryan
Lawrence is a deep tech researcher and investor. He is a Partner for Research at Lunar Ventures, a deep tech venture fund where he focuses on horizon scanning. He developed stateofthefuture.xyz, a deep tech tracker, monitoring over 100+ technologies, and writes weekly stateofthefuture.substack.com. He published an investment thesis on privacy-enhancing technologies in 2021. He has previously advised the UK Government, EU Commission, and World Economic Forum on emerging technologies.
Adri Purkayastha
Adri Purkayastha is currently Group Head of AI and Digital Risk Analytics at BNP Paribas S.A. In this role, his span of responsibility includes all Brands and Subsidiaries, across Domestic Markets, International Financial Services and Corporate & Institutional Banking. He focuses on developing, championing, and building an enterprise-wide understanding of AI/ML opportunities and risks, and overseeing end-to-end AI & Analytics governance and operating models. Additionally, he provides strategic and technical counsel on Data strategy and development of AI and Data Science solutions across the entire Group. Earlier in his career at Deloitte, he was the founder and product owner of AI-enabled SaaS solutions and AI & Data Science advisory lead focussed on Financial Services, worked in Forensic Data Analytics at EY and on Marketing Data Science in Pitney Bowes. He comes from a background that includes entrepreneurship, product management, and data science where we founded companies building AI-enabled products for EdTech, and P2P marketplaces.
Henrique Mendonça
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.
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.