How can machines learn meaningful representations from data without requiring expensive human annotations? This talk explores self-supervised learning—a paradigm shift that enables AI systems to learn from vast amounts of unlabeled data, much like humans learn through observation. We'll focus on BYOL (Bootstrap Your Own Latent), a method that ...

Michal is the Founding Researcher at Isara Labs, tenured researcher at Inria, and the lecturer at the MVA master of ENS Paris-Saclay. Michal is primarily interested in designing algorithms that would require as little human supervision as possible. That is why he is working on methods and settings that are able to deal with minimal feedback, such ...

While LLMs have substantially improved the quality of generated texts, they still tend to make errors in their outputs, which can be subtle and harder to find than for older approaches. This needs to be reflected in the evaluation, where standard metrics or simple scores may not capture errors easily. While human evaluation should produce better ...

Ondřej Dušek is an Assistant Professor at Charles University in Prague, working on natural language generation and human-computer dialogue. His research focuses on generative language models including large language models, mostly applied to the data-to-text and dialogue response generation tasks. He is specifically interested in evaluating the ...

We introduce Meta Agents Research Environments (ARE), a research platform for scalable creation of environments, integration of synthetic or real applications, and execution of agentic orchestrations. ARE provides simple abstractions to build complex and diverse environments, each with their own rules, tools, content, and verifiers, helping to ...

Pierre Ménard is a Research Scientist at Meta, where he works on large language model agents. His research focuses on reinforcement learning, with particular interests in multi-agent systems, unsupervised learning, and reinforcement learning from human feedback. Before joining Meta, he was a postdoctoral researcher, most recently at ENS Lyon. He ...

Sagar Vaze is a researcher at Mistral AI, where he leads the multimodal research team. The team works on multimodal research across the LLM stack (from pre-training to RL) and has contributed to releases from Pixtral 12B to Magistral and Mistral 3. Before joining Mistral, Sagar completed his PhD in the Visual Geometry Group in Oxford University.

Deep learning continues to achieve impressive breakthroughs across disciplines but relies on increasingly large neural network models that are trained on massive data sets. Their development inflicts costs that are only affordable by a few labs and prevent global participation in the creation of related technologies. In this talk, we will ask the ...

Rebekka leads the Relational Machine Learning Group at the Helmholtz Center CISPA since 2021. Their research on sparse deep learning is supported by an ERC starting grant since December 2023 and Apple Research since August 2025. From 2019-2021, she was a PostDoc at the Biostatistics Department of the Harvard T.H. Chan School of Public Health ...

Most back-office work is built around processing documents at scale. Insurance companies review thousands of claim packets per day. Logistics firms reconcile shipping documents across millions of orders per year. Underwriting teams extract the same fields and check the same rules - thousands of times. These aren't one-off analytical tasks. They're ...

After graduating from École Polytechnique, Louis worked on multimodal generative models at the University of Cambridge. He is now the CEO of Retab, a platform used by AI and operations teams, from fast-growing startups to Fortune 500 companies, to automate their hardest document workflows.

Predicting tumor drug response is a fundamental challenge in precision oncology, complicated by cellular heterogeneity, where minor resistant sub-populations drive clinical relapse. While single-cell transcriptomics offers a high-resolution view of these mixtures, a significant domain gap remains: most drug-response labels originate from bulk ...

Dr.Marina Esteban-Medina is a postdoctoral researcher at the ETH Zürich AI Center and ETH Zürich’s Department of Biosystems Science and Engineering (Switzerland). She works at the intersection of AI and translational biomedicine, developing machine-learning and statistical models to understand tumor heterogeneity and treatment response, with a ...

Music Emotion Recognition (MER) aims to model and understand how music conveys emotion, and lies within the broader field of Music Information Retrieval (MIR). Despite recent advances in machine learning, MER remains challenging due to issues such as limited data and generalization across datasets. In this talk, she will provide a high-level ...

Joann is a PhD researcher in Computer Science at the Institute of Computational Perception, Johannes Kepler University. She holds a Bachelor's degree (BM) in Music Performance (Violin) from The University of Texas at Austin and a Master’s degree (MSc) in Music Technology from the Georgia Institute of Technology. She previously worked as a ...

The recent revolution in generative AI has been driven by the rapid growth in training and inference compute for Foundation Models (FMs). This scaling, however, brings unintended consequences, including high energy demand and associated carbon emissions. My research sets out to reverse this trend by targeting a fundamental inefficiency in dominant ...

I am an assistant professor in Natural Language Processing at the University of Edinburgh and an affiliated lecturer at the University of Cambridge. Over the past year, I was a visiting professor at NVIDIA. My research focuses primarily on efficient and modular architectures for foundation models, especially with respect to adaptive memory and ...