About the Seminar
With the advent of foundation models and the emergence of capabilities at scale, algorithms designed to improve the training and inference efficiency of machine learning (ML) systems have become critical research topics. Despite their importance, expertise in training the most efficient systems is often in short supply, limiting the ability of teams to scale experiments, work at maximum productivity, develop foundation models for underexplored domains, and make the best use of our computing resources.
This seminar series seeks to educate, provide a platform for, and facilitate collaborative research opportunities for all algorithmic and technological questions arising in the quest for more efficient machine learning at scale. The desired outcomes of the seminar series are:
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A proliferation of knowledge on state-of-the-art approaches directly employable by diverse research teams hoping to scale training or make use of large-scale models.
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The provision of a research discussion forum on emerging topics to help catalyze further research and form connections among researchers invested in researching these questions.
Upcoming Speakers
Ari Morcos (DatologyAI) & Andreas Kirsch (University of Oxford)
Jane X Wang (Google DeepMind) & Julian Coda-Forno (Helmholtz Institute Munich)
Joaquin Vanschoren (TU Eindhoven)
Razvan Pascanu (Google DeepMind)
Song Han (MIT)
Torsten Hoefler (ETH Zürich)
Vijay Janapa Reddi (Harvard University)
Access
In the spirit of an open, diverse, inclusive, and collaborative scientific culture, we will livestream the seminar series on YouTube, allowing anyone (regardless of affiliation) to follow the seminar online. In addition, we will allow remote participation through live questions on Zoom and the use of online Q&A tools. All participants are required to abide by the ICML Code of Conduct.
Format
Each session is organized in a unique co-hosted format, consisting of a 30-minute tutorial presented by a rising star researcher followed by a 50-minute talk on recent work and open questions by an established scientist in the field. Beyond providing an accessible entry point to the topic of discussion, the rising Star Series is intended as an inclusive element, with presenters drawn from the speaker's lab or broader research community. We conclude with a 10-minute Q&A with the main speaker.
Organization
Seminar Hosts: Jonathan Richard Schwarz, Owen Queen, Marinka Zitnik
Supported by: Department of Biomedical Informatics at Harvard Medical School, Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, and Harvard Data Science Initiative