Speakers

Loic Lannelongue

Talk Title: Un/sustainable computing: what can we do about it? [Details]

Bio: Loic Lannelongue is an Assistant Research Professor in the Department of Public Health and Primary Care at the University of Cambridge, where he leads the Cambridge Sustainable Computing Lab. His research focuses on understanding and reducing the environmental impact of computing, including tools and frameworks for sustainable computational science. He is also known for leading the Green Algorithms initiative and for work at the intersection of sustainable computing, machine learning, and biomedical data science.

Pierre Jacquet

Talk Title: Monitoring GPUs in Cloud Platforms: Challenges and Opportunities for Orchestration [Details]

Bio: Dr. Pierre Jacquet is a postdoctoral researcher at Universite du Quebec (ETS Montreal, Canada) and research scientist at OVHcloud, a hyperscale cloud provider. He holds a Ph.D. in Computer Science from the University of Lille, France. His research focuses on large-scale datacenter environments in applied contexts, spanning the full lifecycle of cloud instances from design and orchestration to monitoring. A particular emphasis of his work is reducing the environmental impact of cloud infrastructures.

Edoardo M. Ponti

Talk Title: Adaptive foundation models for efficient and long-horizon AI [Details]

Bio: Edoardo M. Ponti is an assistant professor in Natural Language Processing at the University of Edinburgh. Over the past year, he was a visiting professor at NVIDIA. His research focuses primarily on efficient and modular architectures for foundation models, especially with respect to adaptive memory and end-to-end tokenization. Previously, he was a visiting postdoctoral scholar at Stanford University and a postdoctoral fellow at Mila Montreal. In 2021, he obtained a PhD from the University of Cambridge. His research has been featured in The Economist and Scientific American, among others. He received a Google Research Faculty Award and several awards (Highlight Awards at ACL 2025 and Best Paper Awards at EMNLP 2021 and RepL4NLP 2019). He is a recipient of an ERC Starting Grant and an ARIA Scaling Compute grant. He is a Scholar of the European Lab for Learning and Intelligent Systems (ELLIS) and part of the TACL journal editorial team.

Maximilian Boether

Talk Title: Efficient data mixing and loading for foundation model training [Details]

Bio: Maximilian Boether is a Ph.D. student at ETH Zurich and a Member of Technical Staff at DatologyAI. At ETH, he is part of the Systems Group and the Efficient Architectures and Systems Lab (EASL), supervised by Ana Klimovic and Gustavo Alonso. His research interests lie at the intersection of systems and data-centric AI. He has published at venues such as SIGMOD, VLDB, MLSys, and ICLR, and contributed to Apertus, Switzerland’s national LLM.