Plenary Speakers
Mateo Valero (Director, Barcelona Supercomputing Center)
Abstract: The Barcelona Supercomputing Center has a long tradition of developing advanced HPC architectures to support science in several important fields from life science to earth science and engineering. Recent advances in open ISA such as RISC-V are now being adopted by BSC to develop new dedicated chips for HPC and AI. This development is accompanied by similar developments in the entire HPC software ecosystem. The talk will review the latest progress and provide an outlook to the future work included in a wider European and international context of which TPC is an obvious example.
Speaker: Mateo Valero is is professor of Computer Architecture at Technical University of Catalonia (UPC) and is the Founding Director of the Barcelona Supercomputing Center, where his research focuses on high performance computing architectures. He has published approximately 700 papers, has served in the organization of more than 300 International Conferences and has given more than 800 invited talks. Prof. Valero has been honored with numerous awards, among them: The Eckert-Mauchly Award 2007 by IEEE (Institute of Electrical and Electronics Engineers) and ACM (Association for Computing Machinery), the Seymour Cray Award 2015 by IEEE and the Charles Babbage 2017 by IEEE. Among other awards, Prof. Valero has received The Harry Goode Award 2009 by IEEE, The Distinguished Service Award by ACM and the Spanish National awards “Julio Rey Pastor” and “Leonardo Torres Quevedo”. Prof. Valero is a “Hall of the Fame” member of the ICT European Program (selected as one of the 25 most influents European researchers in IT during the period 1983-2008, Lyon, November 2008). In 2020 he was awarded the “HPCWire Reader’s Choice Awards” “for his exceptional leadership in HPC” and for “being an HPC pioneer since 1990 and the driving force behind the renaissance of European HPC independence”. He has been also honored with “Condecoración de la Orden Mexicana del Águila Azteca” 2018, the highest recognition granted by the Mexican Government. Prof. Valero holds Honorary Doctorate by 11 Universities, is member of 10 academies and a fellow of IEEE and ACM, and Fellow of AAIA, Asia-Pacific Artificial Intelligence Association. In 1998 Mateo Valero was distinguished as “Favourite Son” of his home town, Alfamén (Zaragoza) and in 2006, his native town of Alfamén named its Public School after him”.
Rick Stevens (Associate Laboratory Director, Argonne National Laboratory)
Abstract: The successful development of transformative applications of AI for science, medicine and energy research will have a profound impact on the world. The rate of development of AI capabilities continues to accelerate, and the scientific community is becoming increasingly agile in using AI, leading us to anticipate significant changes in how science and engineering goals will be pursued in the future. Frontier AI (the leading edge of AI systems) enables small teams to conduct increasingly complex investigations, accelerating some tasks such as generating hypotheses, writing code, or automating entire scientific campaigns. However, certain challenges remain resistant to AI acceleration such as human-to-human communication, large-scale systems integration, and assessing creative contributions. Taken together these developments signify a shift toward more capital-intensive science, as productivity gains from AI will drive resource allocations to groups that can effectively leverage AI into scientific outputs, while others will lag. In addition, with AI becoming the major driver of innovation in high-performance computing, we also expect major shifts in the computing marketplace over the next decade, we see a growing performance gap between systems designed for traditional scientific computing vs those optimized for large-scale AI such as Large Language Models. In part, as a response to these trends, but also in recognition of the role of government supported research to shape the future research landscape the U. S. Department of Energy has created the FASST (Frontier AI for Science, Security and Technology) initiative. FASST is a decadal research and infrastructure development initiative aimed at accelerating the creation and deployment of frontier AI systems for science, energy research, national security. I will review the goals of FASST and how we imagine it transforming the research at the national laboratories. Along with FASST, I’ll discuss the goals of the recently established Trillion Parameter Consortium (TPC), whose aim is to foster a community wide effort to accelerate the creation of large-scale generative AI for science. Additionally, I’ll introduce the AuroraGPT project an international collaboration to build a series of multilingual multimodal foundation models for science, that are pretrained on deep domain knowledge to enable them to play key roles in future scientific enterprises.
Speaker: Rick Stevens is a Professor of Computer Science at the University of Chicago and the Associate Laboratory Director of the Computing, Environment and Life Sciences (CELS) Directorate and Argonne Distinguished Fellow at Argonne National Laboratory. His research spans the computational and computer sciences from high-performance computing architecture to the development of advanced tools and methods. Recently, he has focused on developing AI methods for a variety of scientific and biomedical problems, and also has significant responsibility in delivering on the U.S. national initiative for Exascale computing and developing the DOE’s Frontiers in Artificial Intelligence for Science, Security, and Technology (FASST) initiative.
Satoshi Matsuoka (Director, RIKEN Center for Computational Science)
Abstract: AI for Science is highly anticipated to revolutionize science and its activities, whereby the cycle of innovation via science to further accelerate the cycle of innovations, resulting in significant societal benefits. In this regard, AI driven by HPC, and affecting the sciences on HPC, has brought both HPC and AI to be now the forefront of computer science and IT in general, instead of being rather niches in the past. This has the effect of significantly accelerating system development for HPC, as global assets are pouring in, in terms of talent, money, and efforts. Thus, exploring how science can advance utilzing the latest AI driven by HPC, in combination with traditional physics-based simulation, as well as data-driven real-time methods such as data assimilation. In Japan, there are now significant activities to advance AI for Science — Fugaku-LLM is an effort to realize foundation model training at massive scale, where we have 14,000 nodes of Fugaku being utilized for over two months to train a Japanese-centric language model; the importance is not just the model itself, but also the acquisition of the ability to conduct training at that scale on one of the world’s top-tier machines. A new endeavor at Riken is the TRIP-AGIS project, which involves multiple research centers within Riken including our R-CCS (Center for Computational Science), to create domain-specifc models for AI for Science, and to facilitate the combination of AI with Simulation/automated experiments as a standardized methodology throughout Riken and permeate to all the sciences in Japan to revolutionize the cycle of innovation. TRIP-AGIS and other AI for Science activities will have significant and tight collaboration with counterpart activities at ANL as well as other TPC institutions. Finally the results and demands from the AI for Science activities will be central to the design of our next generation machine FugakuNEXT to be deployed in 2029, as well as for future machines. There the major questions are what are the features that will mutually accelerate AI as well as traditional simulations, and what are the designs that are thought to be specific to AI could actually be leveraged for simulations, or vice versa. For the former, we believe the key would be data movement, for example, minimizing energy for data access to memory, while attaining high capacity. We are investigating several promising technologies for both the former and the latter, all of which will serve as foundational R&D necessary for the post-exascale era.
Speaker: Professor Satoshi Matsuoka from April 2018 has been the director of Riken Center for Computational Science (R-CCS), the Tier-1 national HPC center for Japan, developing and hosting Japan’s flagship ‘Fugaku’ supercomputer which has become the fastest supercomputer in the world in 2020 and 2021, supporting cutting edge HPC research, including investigating Post-Moore era computing, especially the future FugakuNEXT supercomputer. He led the TSUBAME series of supercomputers that received many international acclaims, at the Tokyo Institute of Technology, where he holds a professor position pursuing research in HPC, scalable Big Data, and AI. His longtime contribution was commended with the Medal of Honor with Purple ribbon by his Majesty Emperor Naruhito of Japan in 2022. He is a Fellow in ACM, ISC, IPSJ and the JSSST and has won numerous awards including ACM Gordon Bell Prizes, the IEEE-CS Sidney Fernbach Award, and the IEEE-CS Computer Society Seymour Cray Computer Engineering Award.
Jérôme Bobin (CEA)
Abstract: Bobin will discuss the International Post-Exascale (InPEx) initiative, including an update on the June 2024 workshop held immediately prior to the TPC workshop.
Speaker: Jérôme Bobin is the Head of Detectors, Electronics, and Computing for Physics Department (DEDIP) at CEA. He is a co-director of the French Numérique pour l’Exascale (NumPEx) program, a partner in the International Post-Exascale (InPEx) project.
Fabrizio Gagliardi (Barcelona Supercomputing Center)
Speaker: Fabrizio Gagliardi is the Senior Strategy Advisor to the Barcelona Supercomputing Center Director’s Office. He was a senior scientist at CERN, European Centre for Particle Physics, from 1975-2005. From 2000-2003 he was PI of the EU DataGrid and initiator and PI of EGEE 1 and 2 from 2003-2005. During this time he co-founded Global Grid Forum (later Open Grid Forum). Gagliardi was the founder and director of the first Grid School in 2003. From 2005-2013 he was the director for external research in LATAM and EMEA at Microsoft, joining the Barcelona Supercomputing Centre in 2013 as Senior Advisor. He established the annual ACM HPC and AI summer school in Barcelona in 2019 and has initiated schools including the EU-ASEAN HPC school in Bangkok (2021and 2022). He was a visiting professor at the Gran Sasso Science Institute from 2013-2015 and from 2009-2015 founded and chaired the ACM European council. He was ACM President Award recipient in 2013 and 2018.
Daniela Petkova (EU)
Speaker: Daniela Petkova is a Policy Officer on Digital and AI team of the European Commission, DG Research and Innovation, Unit Industry 5.0.
Jaume Blasco (ES)
Speaker: Jaume Blasco is from the Spanish Ministerio para la Transformación Digital y de la Función Pública (Ministry for Digital Transformation and Public Administration).
Ian Foster (Argonne National Laboratory /University of Chicago)
Speaker: Dr. Ian Foster is Senior Scientist and Distinguished Fellow, and also director of the Data Science and Learning Division, at Argonne National Laboratory, and the Arthur Holly Compton Distinguished Service Professor of Computer Science at the University of Chicago. Ian received a BSc degree from the University of Canterbury, New Zealand, and a PhD from Imperial College, United Kingdom, both in computer science. His research deals with distributed, parallel, and data-intensive computing technologies, and innovative applications of those technologies to scientific problems in such domains as materials science, climate change, and biomedicine. Foster is a fellow of the AAAS, ACM, BCS, and IEEE, and an Office of Science Distinguished Scientists Fellow.
Sampo Pyysalo (University of Turku)
Presentation: Large models for Small Languages
Speaker: Sampo Pyysalo is a researcher in the TurkuNLP group (https://turkunlp.org/) and Research Fellow at the Department of Computing, University of Turku. My work focuses on machine learning for natural language processing, with particular application domains including scientific text mining, Finnish language technology, and large language models. After defending his PhD thesis in computer science at the University of Turku, he held researcher positions at the University of Tokyo, University of Manchester and University of Cambridge before returning to the University of Turku in 2019. His research focuses on natural language processing using machine learning approaches, with recent emphasis on deep learning methods and large language models. Pyysalo has been working on scientific text mining as an application area for nearly 20 years, with specific focus on the English biomedical literature, and has in recent years also addressed a variety of tasks in the processing of Finnish text as well as multi- and cross-lingual applications. His work covers the full range of natural language processing development from initial task design to the development of practical applications and organizing community challenges, including also running manual annotation efforts and developing annotation tools and machine learning methods for various natural language processing tasks.
Jenia Jitsev (Juelich Supercomputing Center / LAION)
Title: Open Foundation Models: Reproducible Science of Transferable Learning
Abstract: Recently, breakthroughs in strongly transferable learning was achieved by training models that use simple, generic losses and large amounts of generic, diverse web-scale data. Crucial for the progress was increasing pre-training scales, that is model, compute and dataset scales employed in the training. Derived scaling laws suggest that generalization and transferability improve when increasing scales hand in hand. Studying learning at such large scales is challenging, as it requires corresponding datasets at sufficiently large scales to be available, sufficient compute resources to execute the training, while handling properly distributed training across thousands of compute nodes without suffering instabilities. We show how work done by LAION community made the whole pipeline for training strongly transferable multi-modal models of various kind (openCLIP, openFlamingo) – termed foundation models – fully open and reproducible. We show how important experiments necessary for studying such models, for instance those leading to scaling laws derivation, critically depend on the open and reproducible nature of such pipelines – requiring also open-sourcing dataset composition and model benchmark evaluation procedures. We conclude with an outlook on studying next generation open multi-modal foundation models and datasets necessary for their creation.
Speaker: Jenia Jitsev is computer scientist, neuroscientist and machine learning researcher, who is co-founder and scientific lead of LAION e.V, the German non-profit research organization committed to open science around large-scale foundation models (openCLIP, openFlamingo) and datasets (LAION-400M/5B, DataComp). He also leads Scalable Learning & Multi-Purpose AI (SLAMPAI) lab at Juelich Supercomputer Center of Helmholtz Association, Germany. His research happens in the overlap of machine learning and neuroscience, seeking to investigate learning as a generic process of incrementally building up a useful model of the surrounding world from available sensory observations and executed actions. He did his PhD with Frankfurt Institute for Advanced Studies (FIAS) on unsupervised learning in hierarchically organized recurrent networks of the visual cortex, and continued as postdoc with Max Planck Institute for Neurological Research in Cologne and Institute of Neuroscience and Medicine in Research Center Juelich, working on models of unsupervised and reinforcement learning in cortico-basal ganglia loops. In LAION and in his lab at Juelich Supercomputing Center, Dr. Jitsev current focus is on driving and democratizing research on scalable systems for generalist, transferable multi-modal learning, leading to foundation AI models capable of strong transfer with predictable behavior derived from corresponding scaling laws, and therefore easily adaptable to broad range of desired tasks and hardware resource settings. For his work, Dr. Jitsev received Best Paper Award at IJCNN 2012, Outstanding Paper Award at NeurIPS 2022 and Falling Walls Award for Scientific Breakthrough 2023.
Marko Grobelnik (Jozef Stefan Institute)
Title: Open GenAI Challenges with Large-Scale Models
Abstract: Despite its success, Generative AI (GenAI) has significant challenges and opportunities in the future. To list just a few: (1) Introducing a suitable notion of “world models” for better model representation and reasoning. (2) Enabling various forms of multi-hop reasoning capabilities, to uncover new knowledge beyond the current human comprehension. (3) Understanding in human terms why GenAI works at all is one of the fundamental questions today. Early results are promising, given substantial computational resources. (4) Large agent infrastructures are an opportunity to reveal emergent behaviors in collaborative and distributed systems. (5) Integrating data modalities beyond the usual ones used in different areas of science (like various forms of sensor data, chemical data, events, spectral data etc.). In conclusion, we can say the future AI challenges are exciting, uneasy and heavily dependent on computing power. Based on NVIDIA announcements, computing speeds to become 1 million times more powerful by the mid-2030s, we can say the AI’s potential is just beginning to be unlocked.
Speaker: Marko Grobelnik is a researcher in the field of Artificial Intelligence (AI). Focused areas of expertise are Machine Learning, Data/Text/Web Mining, Network Analysis, Semantic Technologies, Deep Text Understanding, and Data Visualization. Marko co-leads Artificial Intelligence Lab at Jozef Stefan Institute, cofounded UNESCO International Research Center on AI (IRCAI), and is the CEO of Quintelligence.com specialized in solving complex AI tasks for the commercial world. He collaborates with major European academic institutions and major industries such as Bloomberg, British Telecom, European Commission, Microsoft Research, New York Times, OECD. Marko is co-author of several books, co-founder of several start-ups and is/was involved into over 100 EU funded research projects in various fields of Artificial Intelligence. Significant organisational activities include Marko being general chair of LREC2016 and TheWebConf2021 conferences. Marko represents Slovenia in OECD AI Committee (AIGO/ONEAI), in Council of Europe Committee on AI (CAHAI/CAI), NATO (DARB), and Global Partnership on AI (GPAI). In 2016 Marko became Digital Champion of Slovenia at European Commission.
Franck Cappello (Argonne National Laboratory)
Title: AuroraGPT/Evaluation of AI Assistant for Science: Critical and non-Trivial
Abstract: Innovative methods, new instruments, disruptive techniques, and groundbreaking technologies have led to significant leaps in scientific progress. The increasingly powerful Large Language Models (LLMs) released each month already speed up research activities such as concept explanation, literature search, and summarization. The transformative potential of AI in research activities, in particular foundation models, raises important questions about their performance in science activities, their potential application in different contexts, and their ethics. In this talk, I will specifically focus on the task of a conversational research assistant. To illustrate the gap between existing LLMs and an ideal AI research assistant, I will share observations from using existing LLMs as early research assistants in three parallel and distributed computing experiments with experts. I will discuss AuroraGPT’s efforts in developing benchmarks to evaluate the LLMs’ scientific skills, safety, and trustworthiness and present our first attempt at generating scientific MCQs at scale.
Speaker: Cappello received his Ph.D. from the University of Paris XI in 1994 and joined CNRS, the French National Center for Scientific Research. In 2003, he joined INRIA, where he holds the position of permanent senior researcher. He initiated the Grid’5000 project in 2003 and served as Director of Grid’5000 in its design, implementation, and production phase from 2003 to 2008. Grid’5000 is still used today and has helped hundreds of researchers with their experiments in parallel and distributed computing and to publish more than 2000 research publications. In 2009, Cappello became a visiting research professor at the University of Illinois. He created with Marc Snir the Joint Laboratory on Petascale Computing that was developed in 2014 as the Joint Laboratory on Extreme-Scale Computing (JLESC) gathering seven of the most prominent research and production centers in supercomputing: NCSA, Inria, ANL, BSC, JSC, Riken CCS and UTK. Over his ten-year tenure as the director of the JLPC and JLESC, Cappello has helped hundreds of researchers and students share their research and collaborate to explore the frontiers of supercomputing. From 2008, as a member of the executive committee of the International Exascale Software Project, he led the roadmap and strategy efforts for projects related to resilience at the extreme scale. Through his 30 years of research career, Cappello has directed the development of several high-impact software tools, including XtremWeb, one of the first Desktop Grid softwares, the MPICH-V fault tolerance MPI library, the VeloC multilevel checkpointing environment, the SZ lossy compressor for scientific data. He is an IEEE Fellow, the recipient of the 2024 IEEE CS Charles Babbage Award, the 2024 Europar Achievement Award, the 2022 HPDC Achievement Award, two R&D100 awards (2019 and 2021), the 2018 IEEE TCPP Outstanding Service Award, and the 2021 IEEE Transactions of Computer Award for Editorial Service and Excellence.
Mikel Rodriguez (DeepMind UK)
Title: Emerging challenges in Securing Frontier AI Systems
Speaker: Dr. Mikel Rodriguez has spent over two decades working in the public and private sector securing the application of Artificial Intelligence in high-stakes consequential environments. At Google DeepMind, Mikel defines and leads the cross-functional AI Red and Blue “ReBl” team to ensure that foundational models are battle-tested with the rigor and scrutiny of real-world adversaries, and help drive research and tooling that will make this red-blue mindset scalable in preparation for AGI. In his role as the Managing Director at MITRE Labs, Mikel built and led the AI Red Team for the Department of Defense that focused on deployed AI systems that can be susceptible to attacks involving evasion, data poisoning, model replication; and the exploitation of software flaws to deceive, manipulate, compromise, and render them ineffective. Mikel’s team worked on developing methods to mitigate bias and defend against emerging ML attacks, securing the AI supply chain, and generally ensuring the trustworthiness of AI systems so they perform as intended in mission-critical environments. While at MITRE, his team in collaboration with many industry partners, published ATLAS (Adversarial Threat Landscape for AI Systems) – a knowledge base of adversary tactics, techniques, and case studies for machine learning (ML) systems based on real-world observations, demonstrations from ML red teams and security groups, and the state of the possible from academic research. Mikel firmly believes that AI’s potential will only be realized through collaborations that help produce reliable, resilient, fair, interpretable, privacy preserving, and secure technologies. Mikel received his Ph.D. in 2010 while working at University of Central Florida’s computer vision lab with professor Mubarak Shah. He then moved to Paris where he worked as a post-doctoral research fellow at INRIA.
Rio Yokota (Tokyo Institute of Technology)
Title: Overview of LLM Pre-training Efforts in Japan
Abstract: Large language models (LLM) have the potential to redefine how we interact with computers, and will increase our productivity in almost every aspect. However, the behavior of LLMs heavily depends on the data which it is trained on, and different cultures have slightly different preferences with respect to how they want these LLMs to behave. Therefore, there is a strong incentive for each country to pre-train its own LLM, and Japan is no exception. There are currently many efforts in Japan to pre-train LLMs in both industry and academia. This talk will give an overview of the current state of LLM pre-training in Japan.
Speaker: Rio Yokota is a Professor at the Global Scientific Information and Computing Center, Tokyo Institute of Technology. His research interests lie at the intersection of high performance computing, linear algebra, and machine learning. He is the developer numerous libraries for fast multipole methods (ExaFMM), hierarchical low-rank algorithms (Hatrix), and information matrices in deep learning (ASDFGHJKL) that scale to the full system on the largest supercomputers today. He has been optimizing algorithms on GPUs since 2006, and was part of a team that received the Gordon Bell prize in 2009 using the first GPU supercomputer. Rio is a member of ACM, IEEE, and SIAM.
Uljan Sharka (iGENIUS)
Title: Composite AI: Cutting Through the Hype
Speaker: Uljan Sharka is the Founder and CEO of iGenius, the company behind Crystal, the generative AI platform for business intelligence and data analytics, also known as ‘GPT for Numbers’. Following a brilliant career at Apple, today Uljan is leading an international team of +100 talents, with the mission to democratize data access by leveraging AI with a human approach. He is globally recognized as a cutting-edge expert in the generative AI space.
Charlie Catlett ( Argonne National Laboratory /University of Chicago)
Presentation (Closing Summary)
Speaker: Charlie Catlett is a Senior Computer Scientist at the U.S. Department of Energy’s Argonne National Laboratory, and a Visiting Scientist at the University of Chicago’s Mansueto Institute for Urban Innovation. His research focuses on building cyberinfrastructure to embed edge-AI in urban, environmental, and emergency sensing and response settings. He was founding chair of Grid Forum / Global Grid Forum from 1999-2005 and director of NSF’s TeraGrid initiative from 2004-2007. Charlie was part of the team that established the National Center for Supercomputing Applications (NCSA) in 1985, leading efforts there including the deployment and operation of the NSFNET backbone network, an early component of the Internet, and serving as Chief Technology Officer prior to joining Argonne and UChicago in 2000. He was one of GovTech magazine’s “25 Doers, Dreamers & Drivers” of 2016 and in 2019 received the Argonne Board of Governors Distinguished Performer award. Charlie is a Computer Engineering graduate of the University of Illinois at Urbana-Champaign.
Breakout Lightning Talk Speakers
(bold = Session co-organizer)
| Speaker | Organization | Breakout Session |
| Ansari, Mehrad | Acceleration Consortium | DTW |
| Aula-Blasco, Javier | BSC | SST |
| Becker, Tobias | Groq | HARD |
| Beckman, Pete | Northwestern | SOFT |
| Bhattacharya, Suparna | HPE | SST |
| Bosselut, Antoine | EPFL | LLM-HC |
| Cappello, Franck | ANL | SST |
| Crivelli, Silvia | LBNL | LLM-HC |
| Da Dalt, Severino | BSC | DTW |
| Dallago, Chrsitian | NVIDIA | BCD |
| Domenico Arlandini, Claudio | CINECA | GTAC |
| Durillo Barrionuevo, Juan | LRZ | SOFT |
| Emani, Murali | ANL | MAPE |
| Ferruz, Noelia | IBMB/CSIC | BCD |
| Foster, Ian | ANL/UChicago | DTW |
| Gagliardi, Fabrizio | BSC | GTAC |
| Ganti, Raghu | IBM | MAPE, HARD |
| Garcia Gasulla, Dario | BSC | LLM-HC |
| González-Agirre, Aitor | BSC | MAPE |
| Grobelnik, Marko | Jozef Stefan Institute | DTW |
| Hernandez-Orallo, Jose | UPV | SST |
| Hie, Brian | Stanford | BCD |
| Huang, Dawei | Sambanova | HARD |
| Jitsev, Jenia | JSC | SOFT |
| Kulebi, Baybars | BSC | DTW |
| Kumar, Neeraj | PNNL | DTW |
| Li, Bo | UChicago | SST |
| Lu, Zhiyong | NIH | LLM-HC |
| Ma, Po-Lun | PNNL | DTW |
| Madduri, Ravi | ANL | DTW |
| Taiji, Makoto | RIKEN | GTAC |
| Marrón Vida, Diego | BSC | SOFT |
| Morselli, Laura | CINECA | GTAC |
| Oermann, Eric | NYU | LLM-HC |
| Palomar, Jorge | BSC | DTW |
| Ramanathan, Arvind | ANL | BCD |
| Rish, Irina | UdeM/MILA | MAPE |
| Sarasua, Ignacio | NVIDIA | HARD |
| Sugita, Yuji | RIKEN | BCD |
| Taylor, Valerie | ANL/UChicago | GTAC, SOFT |
| Thakker, Urmish | Sambanova | HARD |
| Ugarte La Torre, Diego | RIKEN | BCD |
| Van Essen, Brian | LLNL | MAPE |
| Vazquez, Miguel | BSC | BCD |
| Vassilieva, Natalia | Cerebras | MAPE, HARD |
| Von Werra, Leandro | Hugging Face | DTW |
| Wahib, Mohamed | RIKEN | SST, DTW |
| Wells, Azton | ANL | MAPE |
| Yokota, Rio | RIKEN | DTW |
| Zamora-Resendiz, Rafael | LBNL | LLM-HC |
| Zhang, Minjia | UIllinois | MAPE |