I am a research scientist at Apple in Zurich. I have a PhD in Computer Science at ETH Zürich obtained under the supervision of Julia Vogt and Gunnar Rätsch. I was a member of the Medical Data Science group and the ETH AI Centre.

I did my undergraduate studies in Information Engineering at the University of Padua, Italy, where I worked with Fabio Vandin on the Optimization of Fast Westfall-Young algorithm for mining significant patterns. I further obtained a M.Sc. in Data Science at ETH Zürich, where I acquired a strong background in Machine Learning. During my master's studies, I gained practical experience by working with Digited Galaxus, Switzerland, to implement subset selection algorithms using the Imitation Learning procedure. My Master's thesis project under the supervision of Gunnar Rätsch was focused on the intersection between clustering and representation learning. After that, I did a research internship at the European Space Agency where I had the opportunity to apply state-of-the-art Machine Learning methods in astrophysics. In February 2020 I joined the Medical Data Science lab lead by Julia Vogt at ETH as a PhD student. During my PhD studies, I completed an internship at Microsoft Research Cambridge, working with Melanie F. Pradier on disentangled representantions of T-cell receptors repertoire and at Apple Zurich, where I worked with Jörn-Henrik Jacobsen on simulation-based inference. I was the co-leader of CSNOW, Computer Science Network of Women at ETH. During my PhD I was supported by a PhD fellowship from the Swiss Data Science Center.

Publications

1. On the Challenges and Opportunities in Generative AI.
   Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin.
   [paper]
2. Tree Variational Autoencoders.
   Laura Manduchi, Moritz Vandenhirtz, Alain Ryser, Julia E Vogt
   NeurIPS, 2023. Spotlight
   [paper][code]
3. Deep Generative Clustering with Multimodal Diffusion Variational Autoencoders.
   Emanuele Palumbo, Laura Manduchi, Sonia Laguna, Daphne Chopard, Julia E Vogt
   ICLR, 2024.
   [paper]
4. Learning Group Importance using the Differentiable Hypergeometric Distribution.
   Thomas M Sutter, Laura Manduchi, Alain Ryser, Julia E Vogt
   ICLR, 2023. Spotlight
   [paper]
5. Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms.
   L. Manduchi, H. Ragnarsdottir, H. Michel, F. Laumer, S. Wellmann, E. Ozkan, J. E. Vogt.
   GCPR 2022
   [paper]
6. Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss.
   Moritz Vandenhirtz, Laura Manduchi, Ricards Marcinkevics, Julia E Vogt
   Domain Generalization Workshop, ICLR 2023. Spotlight
   [paper]
7. Weakly supervised inference of personalized heart meshes based on echocardiography videos.
   F. Laumer, M. Amrani, L. Manduchi, A. Beuret, A. Dubatovka, L. Rubi, C. Matter, J. M. Buhmann.
   Medical Image Analysis 2022.
   [paper]
8. Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models.
   Alain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E Vogt
   Machine Learning for Healthcare (MLHC), 2022.
   [paper]
9. A Deep Variational Approach to Clustering Survival Data.
   Laura Manduchi, Ricards Marcinkevics, Michela C. Massi, Thomas Weikert, Alexander Sauter, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Bram Stieltjes, Julia E. Vogt
   ICLR, 2022.
   Contributed Talk AI for Public Health Workshop, ICLR 2021.
   [paper][code]
10. Deep Conditional Gaussian Mixture Model for Constrained Clustering.
    Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E. Vogt.
    NeurIPS, 2021
    [paper][code]
11. Deep Heart Beat: Latent trajectory learning of cardiac cycles using cardiac ultrasounds.
    Fabian Laumer, Gabriel Fringeli, Alina Dubatovka, Laura Manduchi, Joachim M. Buhmann
    ML4H Workshop, NeurIPS 2020. Best Newcomer Award & Spotlight
    [paper][code]
12. T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states
    Laura Manduchi, Matthias Hüser, Martin Faltys, Julia E. Vogt, Gunnar Rätsch, Vincent Fortuin.
    ACM CHIL 2021 and ML4H Workshop, NeurIPS 2019.
    [paper][code]

Invited Talks

• December 2023: "Tree Variational Autoencoders", SDSC PhD fellows workshop at EPFL.
• November 2023: invited to present my work at the ALPACA seminar on modern algorithms in machine learning and data analysis at the European Centre for Theoretical Studies.
• February 2023: invited to present my work at the Dagstuhl seminar on Challenges and Perspectives in Deep Generative Modeling, organized by Kevin Murphy, Stephan Mandt, Yingzhen Li, and Vincent Fortuin.
• May 2022: "Incorporating domain knowledge in deep generative models for weakly supervised clustering" medAI talk series at Stanford University. See video here.
• December 2021: "Deep Variational Approaches for Weakly Supervised Clustering" Thomas Gärtner's lab at the TU Wien.
• May 2021: "A Deep Variational Approach to Clustering Survival Data" IBM research (Zurich, Switzerland)
• December 2019: "Can Neural Networks help to understand X-ray spectra?" European Space Agency (Madrid, Spain)

Teaching

• Spring 2022: Head Teaching Assistant for Machine Learning for Healthcare at ETH Zürich
• Fall 2021: Teaching Assistant for Advanced Machine Learning at ETH Zürich
• Spring 2021,2020: Teaching Assistant for Machine Learning for Healthcare at ETH Zürich
• Fall 2020: Teaching Assistant for Probabilistic Artificial Intelligence at ETH Zürich
• Spring 2019: Student Teaching Assistant for Computational Intelligence Lab at ETH Zürich

Service

• Co-leader CSNOW: leading a team of students motivated to abolish gender-based barriers and prejudices in computer science.
• Workshop Organizer: Organizer of Deep Generative Models for Health workshop at NeurIPS 2023 and Time Series Representation Learning for Health workshop at ICLR 2023.
• Conference Reviewer: ICML 2023, NeurIPS 2023, NeurIPS 2022, Advances in Approximate Bayesian Inference and Bridging the Gap: From ML Research to Clinical Practice workshop, NeurIPS 2021.
• Journal Reviewer: Nature Communications 2024.