[Call for Abstracts] BayLearn 2026:Abstract Submission Deadline: July 30th, 2026
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BayLearn 2026 Call for Abstracts
BayLearn 2026 will be an in-person event, held on Thursday, October 15th, 2026.
BayLearn 2026 will be hosted at Santa Clara University and co-organized in partnership with the University of California, Santa Cruz.
Note: BayLearn 2026 will not be a hybrid event, and it will not be live-streamed.
For a list of candidate topics, abstract selection criteria, and submission details, please see:
https://baylearn.org/submissions
The abstract submission deadline is Thursday, July 30th, 2026, 11:59pm PDT.
Please submit abstracts as a 2-page pdf in NeurIPS format. An extra page for acknowledgements and references is allowed.
About BayLearn
The BayLearn Symposium is an annual gathering of machine learning researchers and scientists from the San Francisco Bay Area. While BayLearn promotes community building and technical discussions between local researchers from academic and industrial institutions, it also welcomes visitors. This one-day event combines invited talks, contributed talks, and posters, to foster exchange of ideas.
Meet with fellow Bay Area machine learning researchers and scientists during the symposium that will be held on October 15th, at Santa Clara University.
Feel free to circulate this invitation to your colleagues and relevant contacts.
Key Dates
Abstract submission deadline: Thursday, July 30th, 2026 at 11:59pm PDT
Abstract acceptance notifications: Monday, September 14th, 2026—IMPORTANT: If your abstract is selected, at least one author must attend the event in person.
Registration Lottery opens: Tuesday, Sep 15th, 2026
BayLearn 2026 Symposium: Thursday, October 15th, 2026 - BayLearn 2026 Symposium. We are planning for BayLearn 2026 to be an in-person event, to be held on Thursday, October 15, 2026, at Santa Clara University.
Submissions
We encourage submission of abstracts. Acceptable material includes work which has already been submitted or published, preliminary results, and controversial findings. We do not intend to publish paper proceedings; only abstracts will be shared through an online repository. Our primary goal is to foster discussion! For examples of previously accepted talks, please watch the paper presentations from previous BayLearn Symposiums: https://baylearn.org/previous
For more information about submissions, please look here:
https://baylearn.org/submissions
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Best Regards,
The BayLearn Organizers