News

EPFL scientists have developed a geometric deep learning method that can create a coherent picture of neuronal population activity during cognitive and motor tasks across experimental subjects and conditions.

“Designing Life with AI” is a cross-disciplinary MAKE project in which EPFL students get a chance to explore the research process by designing proteins. A total of eight research labs are involved.

To advance modern medicine, EPFL researchers are developing AI-based diagnostic tools. Their goal is to predict the best treatment a patient should receive.

EPFL researchers have developed a computational method to explicitly consider the impact of water while designing membrane receptors with enhanced stability and signaling, paving the way for novel drug discovery and protein engineering.

By applying techniques from explainable artificial intelligence, engineers can improve users’ confidence in forecasts generated by artificial intelligence models. This approach was recently tested on wind power generation by a team that includes experts from EPFL.

EPFL scientists have used deep learning to design new proteins that bind to complexes involving other small molecules like hormones or drugs, opening up a world of possibilities in the computational design of molecular interactions for biomedicine.

The Swiss National Science Foundation (SNSF) has awarded three “SNSF Advanced Grants” to EPFL researchers, including Florent Krzakala in the School of Engineering.

The use of generative artificial intelligence in protein design stands to revolutionize new drug development. EPFL ambitions putting together a consortium to further explore this avenue.

A researcher at EPFL working at the crossroads of neuroscience and computational science has developed an algorithm that can predict how surprise and novelty affect behavior.

EPFL researchers have developed 4M, a next-generation, open-sourced framework for training versatile and scalable multimodal foundation models that go beyond language.