Martin SchrimpfExpertise
NeuroAI, computational neuroscience, computer vision, natural language processing, human alignment, startups [CV]
Mission
Modeling the brain: Martin's research focuses on a computational understanding of the neural mechanisms underlying natural intelligence in vision and language. To achieve this goal, he bridges Deep Learning, Neuroscience, and Cognitive Science, building artificial neural network models that match the brain’s neural representations in their internal processing and are aligned to
Juan Cruz Landoni MartinExpertise
Mitochondria, metabolism, mitochondrial DNA, superresolution microscopy, ageing
Mission
Dissecting the dynamic orchestration between mitochondria and metabolism underlying health, disease and ageing using advanced imaging and integrative multi-omics.
Current Work
I am a chemist turned mitochondria enthusiast. Using molecular biology, super-resolution microscopy and smart imaging techniques, I aim to decipher the complex behaviour of the tiny intruders that live inside our cells, mitochondria, and
Han SunExpertise
Deep Learning, Computer Vision, Domain Adaptation, Generative Modeling
Open Projects:
Test-Time Domain Adaptation for Anomaly Detection: SiROP link for detailed information
Han obtained her master's degree in Geomatic from the Federal Institute of Technology in Zürich (ETHZ). Before joining IMOS Lab - EPFL, she focused on computer vision with a particular interest in domain adaptation & style adaptation. During her Ph.D. study, she will explore more in the field of domain adaptation with its appli
Keivan Faghih NiresiExpertise
Computational sensing/imaging, Inverse problems, Signal processing, Graph neural networks, Domain adaptation.
Keivan is a PhD student at école Polytechnique Fédérale de Lausanne (EPFL). He joined the Intelligent Maintenance and Operations Systems (IMOS) Lab under the supervision of Prof. Olga Fink in February 2023.Prior to joining EPFL, he obtained his master's degree from the Institute of Communications Engineering, College of Electrical Engineering and Computer Science, National Tsing Hua Univer
Milena Maria SchuhmacherResearchSchuhmacher Lab & Chemical and Membrane Biology It's all about the lipids: In the Schuhmacher Lab we are interested in answering outstanding questions in membrane biology. Our chemical biology approach opens the door to investigate the so far mostly invisible and thus secret work of lipids. In particular, we would like to understand the biological role of lipid diversity and their impact on signaling processes.
Teaching & PhD PhD Students Mélissa Ruiz, Gary Domeniconi, Linda Wedemann Courses
Camille Véronique Bernadette GoemansCamille's research focuses on understanding how antibiotics and non-antibiotic drugs affect the diverse bacteria from the human gut microbiota and how they contribute to dysbiosis and the development of antibiotic resistance. To achieve this goal, she combines systems approaches with molecular microbiology, bacterial genetics and biochemistry.She obtained her Bachelor's and Master's degrees in Biomedical Sciences from the UCLouvain, Belgium, after which she completed her PhD (FRIA national fellowship) at th
Alexander DittrichCurrent Work
My research interests are somewhere between neural networks with Hebbian plasticity, meta-reinforcement learning, and the control of rather unconventional soft robotic platforms like tensegrity robots.
Simon Elias BibriExpertise
• Sustainable cities• Smarter eco-cities• Sustainable smart cities• Artificial Intelligence of Things (AIoT) • Generative Artificial Intelligence of Things (GAIoT)• Foundation Models (FMs) • Quantum Generative Artificial Intelligence (QGAI)• Urban Digital Twins (UDTs)• Urban Brain (UB) • Smart Circular Metabolism• Urban planning and design• Environmental sustainability and climate resilience• Sustainable smart energy• Sustainable smart transportation• Sustainable smart waste and water management•
Lazar MilikicExpertise
LLMs (Large Language Models), NLP (Natural Language Processing), Automated Reasoning
Mission
My work focuses on developing robust methods for autoformalization and automated theorem proving using large language models. I aim to enhance the reasoning reliability of modern AI systems by reducing hallucinations, improving predictability, and integrating formal verification techniques and tools into LLM-based pipelines.
Before joining EDIC, I completed a Bachelor’s in Mathematics and Computer Science
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