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The Center for Decoding the Universe brings together researchers across scientific disciplines to answer the biggest questions about our Universe by leveraging complex data with the most advanced statistical and computational methods.
Our November session is Scaling Training Across GPUs and Nodes with speaker Zoe Ryan. This session focuses on efficient strategies for using multiple GPUs and nodes. We will discuss how to deploy strategies of data parallelism and model parallelism to scale to multiple GPUs through PyTorch, enabling faster training times and better model performance.
Location: CoDa W401 & Zoom
Refreshments and snacks provided
Please follow the link to register
Our November session is Scaling Training Across GPUs and Nodes with speaker Zoe Ryan. This session focuses on efficient strategies for using multiple GPUs and nodes. We will discuss how to deploy strategies of data parallelism and model parallelism to scale to multiple GPUs through PyTorch, enabling faster training times and better model performance.
Location: CoDa W401 & Zoom
Refreshments and snacks provided
Please follow the link to register
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Please note that this event is open to Stanford affiliates only or by invitation.
Please note that this event is open to Stanford affiliates only or by invitation.
Advanced statistical and computational insights are transforming all aspects of physics. The goal of PhyStat 2027: statistical inference from physics data in the age of AI is to bring together physics/statistics/data science/computer science researchers, practitioners, and educators who are interacting with physics data using modern tools. Physics here is broadly defined and includes related fields within the physical sciences. The conference will include both methodology and applications involving complex statistical challenges.
Save the date! Registration details coming soon.
Many areas of physics have been in a big-data, precision era for decades. As such, they were early adopters of AI tools. The deep learning revolution of the last decade has transformative potential in physics, especially given the field’s culture of developing and using advanced statistical and computational techniques. Physics also has unique challenges that necessitate dedicated research and pedagogy. For example, the largest scientific datasets are not always images or text. Additionally, ab initio simulations often play a key role in physics research, leading to the rise of simulation-based inference. These and other aspects of physics have led to a growing research area at the intersection of physics and AI—with data physicists and phystatistians emerging as researchers at the forefront of novel methodology.
This Conference is modeled after the Machine Learning and Physical Sciences Workshop (MLPS) at NeurIPS. We welcome short research papers that will be peer reviewed and then ranked for impact by area chairs. Accepted papers will be published, and authors will be invited to present a poster. A small number of accepted papers will be selected for spotlight talks. The rest of the program will include invited talks and panels. Paper submission is open to all.
Olaf Behnke / DESY + CERN PhyStat
Lydia Brenner / Nihef + CERN PhyStat
Iona Ciuca / Stanford C4DU
Yang Chen / U. Michigan Stats + ASA AIG
Susan Clark / Stanford C4DU
Louis Lyons / Oxford + CERN PhyStat
Simon Mak / Duke Stats + ASA SPES
Michael Mahoney / LBNL SDD + UCB Stats
Benjamin Nachman / Stanford + SLAC C4DU
Michelle Ntampaka / Space Science Telescope + AAS WGAA
Philip Stark / UCB Stats
Guenther Walther / Stanford Stats
Risa Wechsler / Stanford + SLAC C4DU