Our latests
Don’t miss our latest news and events.
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HIDA@HIDSS4Health / ELLIS Life / NCT Data Science Seminar: Julia Schnabel
AI-Enabled Imaging Julia Schnabel, Institute of Machine Learning in Biomedical Imaging, Helmholtz Center MunichFebruary 23, 11:00 AM (CET) Join the meeting here Abstract Artificial intelligence, in particular from the class of machine / deep learning, has shown great promise for applications in medical imaging. However, the success of AI-based techniques is limited by the availability
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ELLIS Life / NCT Data Science Seminar: Florian Büttner
Trustworthy Machine Learning in Oncology Florian Büttner, Frankfurt University & German Cancer Consortium (DKTK)December 15, 11:00 AM (CET) Join the meeting here Abstract In this talk I will give an overview on some challenges and opportunities of developing machine learning approaches tailored for translational applications. I will present recent work on how to achieve (more)
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ELLIS Life / NCT Data Science Seminar: Jean-Philippe Vert
Machine Learning for Single Cell Omics Jean-Philippe Vert, MINES ParisTech & Google Brain, ParisNovember 17, 11:00 AM (CET) Watch the video here Abstract In this talk I will describe several machine learning-based methods to analyze single cell omics data, which provide a rich characterization of individual cells within a heterogeneous population. I will focus on: 1)
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ELLIS Life Annual Symposium 2021
We are pleased to invite you to the ELLIS Life Annual Symposium 2021 “Using AI to accelerate Life Science Research”. The ELLIS Unit Heidelberg was established in September 2020 and this symposium also marks the first anniversary of the unit. We hope you will join us for a vibrant discussion on using AI and Machine
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ELLIS Life / NCT Data Science Seminar: Smita Krishnaswamy
Geometric and Topological Approaches to Representation Learning in Biomedical Data Smita Krishnaswamy, Yale UniversityOctober 6, 15:00 PM (CEST) Watch the video here Abstract High-throughput, high-dimensional data has become ubiquitous in the biomedical sciences as a result of breakthroughs in measurement technologies and data collection. While these large datasets containing millions of observations of cells, peoples,
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Björn Ommer nominated as ELLIS Member
A grand welcome to our new ELLIS Member – Björn Ommer! Prof. Björn Ommer heads the Computer Vision group at the Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University. His group works on developing non-invasive diagnostic tools for the neurosciences that can be efficiently integrated into clinical or scientific practice since they require little to
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Congratulations to our new ELLIS Fellows
Wolfgang Huber, Group Leader and Senior Scientist, Anna Kreshuk, Group Leader, and Oliver Stegle, Associate Group Leader, have been appointed ELLIS Fellows by the European Laboratory for Learning and Intelligent Systems (ELLIS), the leading European network of AI researchers. They act as ambassadors and are faculty members of the ELLIS Unit Heidelberg, which brings together
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Ullrich Köthe nominated as ELLIS Member
A grand welcome to our new ELLIS Member – Ullrich Köthe! Prof. Ullrich Köthe heads the Explainable Machine Learning group at the Computer Vision and Learning Lab at the Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University. He employs explainable learning to derive insight from data using invertible neural networks for applications in medicine, image
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ELLIS Life / NCT Data Science Seminar: Andrea Vedaldi
Discovering Actionable Interpretations from Raw Visual Data: From 2D Clustering to 3D Reconstruction Machines can repeat what they are told; is this intelligence? Andrea Vedaldi, University of OxfordJuly 28, 11:00 AM (CEST) Abstract In this talk, I will discuss the problem of discovering interpretable and actionable representations of visual data without supervision. I will start
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ELLIS Life / NCT Data Science Seminar: Dmitry Kobak
Neighbour Embeddings for Scientific Visualization How do the existing algorithms differ and what are the trade-offs? Dmitry Kobak, University of TübingenJune 16, 11:00 AM (CEST) Abstract I am going to present our recent work on manifold learning and low-dimensional visualizations of single-cell transcriptomic data. Single-cell transcriptomics yields ever growing datasets containing RNA expression levels for