The EPFL AI Center brings together outstanding early-career researchers who collaborate with EPFL faculty across diverse scientific domains to advance the capabilities and applications of AI.
With a focus on excellence and collaborative research, our fellows and researchers develop their own research agendas while contributing to the Center’s community and strategic initiatives.
Below, meet the fellows and researchers currently shaping the future of AI at the EPFL AI Center, including researchers supported through the AI Fellowship Programme 
.
Safe and Robust AI
Andi studies how groups of large language models can be organised to solve complex tasks more effectively. His research focuses on multi-agent orchestration: designing systems in which specialized agents should interact, collaborate and take actions to achieve the best possible outcome.
EPFL Hosts: Maryam Kamgarpour & Volkan Cevher
AI, optimization & energy efficiency
Arto develops optimization methods to make the training of foundation models faster and more resource-efficient. His research addresses major bottlenecks in distributed training, developing methods that reduce communication costs and allow faster devices to continue working without waiting for slower ones.
EPFL Hosts: Martin Jaggi & Volkan Cevher
Medical AI, multimodal & active learning
Linus develops efficient and trustworthy algorithms for multimodal healthcare data. His research focuses on transfer learning and active learning: reusing knowledge from related tasks, selectively acquiring new informative data, and applying these methods to novel precision healthcare problems.
EPFL Hosts: Charlotte Bunne & Bart Deplancke
Medical AI, evaluation & global health
Generative AI, formal verification & interactive proofs
Orr develops “Self-Proving Models”: generative models that can formally prove their answers are correct. His research builds on interactive proof systems, where an AI generates an answer and interacts with a verification algorithm until the verifier is mathematically certain the answer is correct.
EPFL Hosts: Nicolas Flammarion, Thomas Bourgeat, Lenaïc Chizat, Viktor Kuncak.
NLP, AI for Health and Humanitarian reponse
Multimodal learning, 3D Vision & Spatial Intelligence
NLP, ML and cognitive neuroscience
Badr works at the intersection of natural language processing, machine learning, and cognitive neuroscience. His research explores how large language models can be adapted to better capture the neural and behavioral responses of unseen individuals or populations, with a particular focus on cognitive disorders.
EPFL Hosts: Antoine Bosselut & Martin Schrimpf
NLP, ML & AI for scientific discovery
Agentic AI and AI for scientific discovery
Siba develops AI agents to support scientific discovery from single-cell biological data. His research focuses on training and evaluating LLM-based agents that can explore complex datasets, propose hypotheses and run computational analyses, with particular attention to reliability and reproducibility.
EPFL Host: Maria Brbic
Optimization & Data efficiency
Medical AI, multimodal & active learning
Linus develops efficient and trustworthy algorithms for multimodal healthcare data. His research focuses on transfer learning and active learning: reusing knowledge from related tasks, selectively acquiring new informative data, and applying these methods to novel precision healthcare problems.
EPFL Hosts: Charlotte Bunne & Bart Deplancke
NLP, AI for Health and Humanitarian reponse
