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i6092467/README.md

👋 Hi! My name is Ričards Marcinkevičs. 🎓 I pursued a doctorate at the Institute for Machine Learning, Department of Computer Science, ETH Zurich, where I was a Medical Data Science group member supervised by Prof. Dr. Julia E. Vogt and co-advised by Prof. Dr. Fanny Yang. I passed my doctoral exam in July 2024.

🤖 My research interests include interpretable and explainable machine learning, in particular, inductive biases for neural networks that may render the model more interpretable in specific use-cases. I am also interested in how we can leverage interpretations and explanations to obtain actionable insights about the data or the model itself, for instance, to perform scientific discovery or make the predictions fairer and more robust? From the application perspective, I have worked on time series and survival analysis and participated in several interdisciplinary projects leveraging ML methods to analyse biomedical data.

🤝 Starting from September 2024, I am on the job market. I would love to hear about new opportunities!

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  1. sonialagunac/Beyond-CBM sonialagunac/Beyond-CBM Public

    Repository for the paper "Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?"

    Python 1 1

  2. semi-supervised-multiview-cbm semi-supervised-multiview-cbm Public

    Concept bottleneck models for multiview data with incomplete concept sets

    Python 9 4

  3. diff-bias-proxies diff-bias-proxies Public

    Pruning and fine-tuning for debiasing an already-trained neural network with applications to deep chest X-ray classifiers

    Python 4 1

  4. vadesc vadesc Public

    A probabilistic model to cluster survival data in a variational deep clustering setting

    Jupyter Notebook 28 14

  5. GVAR GVAR Public

    An interpretable framework for inferring nonlinear multivariate Granger causality based on self-explaining neural networks.

    Python 64 18

  6. pediatric-appendicitis-ml pediatric-appendicitis-ml Public

    Using ML to predict the diagnosis, management, and severity of pediatric appendicitis

    R 9 1