Michael Moor

News

• 2023-07-27: Excited to announce Med-Flamingo, a multimodal medical few-shot learner! Stoked to see our thread to reach >200k impressions! Furthermore, the paper got covered by Huggingface Daily Papers and the medical AI newsletter Dr. Penguin!
• 2023-07-17: Thrilled to give a talk at IBM Research Zurich today! I will talk about our recent Nature paper and some exciting ongoing medical foundation model work.
• 2023-07-11: Honored to join Stanford HAI today to give an executive education course on multimodality in foundation models!
• 2023-06-22: Thrilled to talk today about Generalist medical AI at Stanford’s MedAI Group exchange. I will present our recent Nature paper and some cool ongoing work.
• 2023-05-25: Excited to give a guest lecture at Stanford in Prof. Emma Brunskill’s CS 31 N course ‘Counterfactuals: The Science of What Ifs?’. Hamed and I present and discuss our recent paper Zero-shot causal learning.
• 2023-04-12: A dream come true. Thrilled to announce our paper Foundation models for generalist medical AI was published today in Nature! Check it out here.
• 2023-04-10: Excited to give a talk at Prof. Sean Mackey’s lab at Stanford Medicine titled Medical foundation models: a primer on pre-trained, multimodal medical AI.
• 2023-02-05: New preprint: Zero-shot causal learning. First paper of my postdoc! Check it out here
• 2022-11-05: Humbled to be invited to present my PhD thesis at the ETH Silver Medal Presentations Seminar at ETH Zurich!
• 2022-10-05: I just had the honor to give a talk at the Swiss MD/PhD retreat, presenting a Hitchhiker’s guide to medical AI!
• 2022-08-29: Excited to be invited to talk about medical AI at the Stanford Center for Biomedical Informatics Research!
• 2022-02-07: Thrilled to announce that I will join Stanford University as a postdoctoral scholar!
• 2022-01-24: Happy to announce that our paper Topological Graph Neural Networks was accepted to ICLR 2022!
• 2021-12-14: I successfully defended my PhD Thesis! Check out the wonderful doctoral hat I received here!
• 2021-08-01: Excited to release the preprint of our multi-centre study for predicting sepsis!
• 2021-05-29: Check out our systematic review on machine learning-based sepsis prediction that was just published in Frontiers in Medicine! And here a summarizing thread (followed by an engaged discussion).
• 2021-04-13: Our survey on topological machine learning has been accepted to Frontiers in Artificial Intelligence. Check it out here!
• 2021-01-08: Our work Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis was accepted for publication in Applied Sciences. Congratulations and thanks to all collaborators!
• 2020-12-02: Our paper Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score has won a best paper award at ICHI 2020, the 8th IEEE International Conference on Healthcare Informatics! The price is honored with 500 USD, congrats to the entire team!
• 2020-11-12: Excited to talk about Machine learning for personalized medicine together with Bastian Rieck at the Young Scientists Lunch Webinar, Swiss Society for Biomaterials and Regenerative Medicine.
• 2020-10-31: Our work Challenging Euclidean Topological Autoencoders was accepted for presentation at the Neurips 2020 Workshop on TDA and Beyond.
• 2020-09-03: Thrilled to be invited to talk about topological representation learning at the DataSig Seminar of the Mathematical Institute, University of Oxford.
• 2020-09-02: Check out our new systematic review (medRxiv preprint) on the early prediction of sepsis using machine learning.
• 2020-07-03: Happy to hear that our work Enhancing statistical power in temporal biomarker discovery through representative shapelet mining was accepted for presentation at ECCB 2020.
• 2020-07-02: Our work Path Imputation Strategies for Signature Models was accepted for presentation at the Workshop on the Art of Learning with Missing Values (ARTEMISS) at ICML 2020.
• 2020-06-01: Super excited to have 2 papers accepted at ICML 2020: Topological Autoencoders and Set Functions for Time Series.