{"id":431,"date":"2023-06-22T16:29:50","date_gmt":"2023-06-22T14:29:50","guid":{"rendered":"https:\/\/wp.unil.ch\/tml\/?page_id=431"},"modified":"2024-11-11T21:13:25","modified_gmt":"2024-11-11T20:13:25","slug":"jonas-richiardi","status":"publish","type":"page","link":"https:\/\/wp.unil.ch\/tml\/team\/jonas-richiardi\/","title":{"rendered":"Jonas Richiardi"},"content":{"rendered":"\n

Research statement<\/h2>\n\n\n\n

In the past decade, the interactions between parts of a biological system<\/em> have emerged as a central concept in biomedical sciences. I am convinced that exploiting this in practice by coupling graph-based representations with machine learning and statistics is key to yielding great predictive power for biomedical data. Compared with other diagnostic modalities, such as protein levels in the blood, imaging offers the unique benefit of spatial localization. The main goal of my research is thus to develop advanced machine learning methods to jointly model imaging data, omics data, and clinical data to yield improved clinical decision support<\/strong>. The applications include diagnosis, differential diagnosis, prognosis and forecasting, subtyping, and treatment planning.<\/p>\n\n\n\n

Bio sketch<\/h2>\n\n\n\n

I am currently a principal investigator (responsable de recherche) and senior lecturer (MER1) at the Department of Radiology of the Lausanne University hospital (CHUV)<\/a> and University of Lausanne<\/a> (Faculty of Biology and Medicine), where I head the Translational Machine Learning Laboratory. I also head the Imaging for Precision Medicine Section of the Data Science Module of the CIBM<\/a> Center for Biomedical Imaging.<\/p>\n\n\n\n

I was previously Clinical Research Lead at Siemens Healthcare Switzerland’s Advanced Clinical Imaging Technology<\/a>, a joint position with the Department of Radiology<\/a>, Lausanne University Hospital<\/a>. Before that, I was a Marie Curie Fellow<\/a> with my project project “Modelling and Inference on brain Networks for Diagnosis” (#299500), jointly affiliated to the FINDlab<\/a> at Stanford University and LabNIC<\/a> at the University of Geneva. Between 2009 and 2012, I was\u00a0 a post-doctoral research fellow in the Medical Image Processing Lab<\/a>, a joint position between the Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne<\/a> (EPFL), Institute of Bioengineering<\/a>, and the University of Geneva<\/a>‘s Faculty of Medicine<\/a>, Radiology and Medical Informatics Department<\/a>. Prior to joining the group, I co-founded the information engineering consultancy PatternLab<\/a>.<\/p><\/p>\n\n\n\n

I obtained my Ph.D. in 2007 at EPFL in the laboratory of the Dalle Molle Institute for Perceptual Artificial Intelligence (LIDIAP)<\/a>, part of the now-fragmented Signal Processing Institute. <\/p>\n\n\n\n

I have published over 90 peer-reviewed publications, with a focus on modelling and inference for complex multimodal biological data, in particular magnetic resonance imaging data and its combination with omics data. My perennial interest in on graph-based machine learning approaches, where all data is first represented as a graph, and machine learning approaches are applied to form prediction with graphs. My current areas of focus are stroke, cardiac structure and (dys)function, and brain cancer, with ongoing efforts to develop the necessary tools and infrastructure to access and process messy, hospital-scale data.<\/p>\n\n\n\n

Awards and honours<\/h2>\n\n\n\n