Researchers from U.S. Department of Energy (DOE) national laboratories, universities, and industry convened for the Monterey Data Conference 2026, a meeting initiated in 2019 that this year highlighted advances in scientific data analysis and computing. The 2026 conference, with over 200 participants, focused on Acceleration and its connection to the DOE’s Genesis Mission.
“MDC has grown in size and impact every year,” said NERSC Director Sudip Dosanjh, a co-founder of the conference, noting discussions included machine learning and disruptive technologies like quantum computing. Ana Kupresanin of Lawrence Berkeley National Laboratory added that acceleration also comes from reducing friction around data access and reuse.
Integrated Computing Platforms Support DOE’s Genesis Mission
The conference agenda prominently featured discussions on building and exploiting integrated computing platforms, directly addressing a core tenet of the DOE’s Genesis Mission and the forthcoming American Science Cloud. Sessions explored how these platforms will underpin scientific discovery by enhancing data accessibility and usability for researchers across multiple facilities. Beyond infrastructure, the conference examined agentic scientific intelligence and reasoning-based discovery methods, alongside the potential of scientific foundation models and applications merging classical and quantum computing.
Kathy Yelick, director of Lawrence Berkeley National Laboratory, framed these advancements within the broader context of “Science in the Age of AI” with her opening presentation, signaling a shift towards data-driven research paradigms. Participants also examined quantum supercomputing, indicating a growing interest in using quantum systems to address complex scientific challenges.
This year there were over 200 participants from universities, laboratories and industry-scientists, computer scientists and computer architects discussing advances in data analysis and machine learning, as well as disruptive technologies like quantum computing.




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