Biography

Charlotte Bunne is an assistant professor at EPFL in the School of Computer Science (IC), the Swiss Institute for Experimental Cancer Research (ISREC) of the School of Life Sciences (SV) and the EPFL AI Center. She is affiliated with the Precision Oncology Unit of HUG. Before, she was a PostDoc at Genentech and Stanford with Aviv Regev and Jure Leskovec and completed a PhD in Computer Science at ETH Zurich working with Andreas Krause and Marco Cuturi. During her graduate studies, she was a visiting researcher at the Broad Institute of MIT and Harvard hosted by Anne Carpenter and Shantanu Singh and worked with Stefanie Jegelka at MIT. Charlotte has been a Fellow of the German National Academic Foundation and is recipient of the ETH Medal. 

Charlotte Bunne's research develops multi-scale and multi-modal AI methods that can leverage large-scale biomedical datasets obtained from various experimental technologies. The solutions aim at the development of diagnostic tools that build on learned virtual patient representations as well as seamless integrations of AI algorithms and data routinely produced in hospitals. This translational and interdisciplinary cancer research is facilitated through close collaborations with clinicians and basic researchers.

Research Overview

Tailoring therapies to an individual’s unique molecular profile is essential for effective treatment, and this requires a deep understanding of the cellular and macromolecular mechanisms that influence treatment response. To achieve this, our lab develops novel artificial intelligence methods designed to mimic the structure and inner workings of biological systems, allowing them to be trained on large biomedical datasets from various experimental technologies. By leveraging these AI techniques inspired by biological systems, our work not only provides important contributions to personalized medicine but also impacts active machine learning research. As we unlock a deeper understanding of cellular mechanisms, we aim to enable more effective and individualized therapies for patients.

Personalized Treatments and Therapy Design

To advance personalized medicine, we need to design deep learning architectures that incorporate biomedical priors, scale to large biological datasets, and are deployable in the clinical setting. Such architectures will be essential for making reliable predictions of therapy outcomes and forecasting the molecular behavior of different drugs.

Our previous work has developed AI methods, such as vision transformers (Wenckstern et al., Preprint 2025), diffusion models and neural optimal transport (Bunne et al., NeurIPS 2022, Nature Methods 2023, Nature Reviews Methods Primers 2024), to learn from highly multiplex digital pathology data, predict accurate diagnostic properties of patients and infer dynamic responses of single cells derived from cancer patients using both imaging and sequencing data. By integrating insights of the underlying biological mechanisms with concepts from dynamic systems theory, and contributing novel theoretical and algorithmic frameworks, these tools can forecast the dynamics of complex biological processes. Our efforts in that area directly translate into clinical applications: In a current observational clinical study of cancer patients within the Tumor Profiler consortium, our tools demonstrate the potential for meaningful treatment outcome predictions for a diverse set of patients.

Digital Diagnostics and the Vision of the Digital Twin

Building on insights from AI breakthroughs in vision and language and leveraging vast amounts of biological data, our current research aims to advance the vision of a virtual cell, a foundational AI-based model capable of predicting the behavior of healthy and diseased cells with broad applications for biomedical research and therapeutic development (Bunne et al., Cell 2024). AI virtual cells allow the creation of a patient’s medical digital twin, leading to faster diagnoses, more successful treatments, and the detection of rare diseases. Such machine learning models then allow simulating patient-specific treatment responses and providing an in silico environment for testing drug candidates and conduct diagnosis of diseases. Creating such virtual patient representations as well as digital diagnostics requires innovations on adaptive algorithms that navigate the constraints of clinical settings and the complexity of human biology.

Our research was awarded four best paper awards at different machine learning conferences (NeurIPS’18, ICML’20, and ICML’21, ICML’24 workshops), a Remarkable Output Award by SIB, and two ETH Medals. The recognized expertise and research on cutting-edge methodologies earned Charlotte an invitation to present a tutorial at the International Conference of Machine Learning and were invited to write a Nature Reviews Methods Primer article (Bunne et al., Nature Reviews Methods Primer 2024). She is invited to talks, lectures and panels around the world and her research has been featured in news outlets (The Atlantic, MIT Press, ETH Press, etc.) as well as interviews with Nature.

Publications

Johann Wenckstern, Eeshaan Jain, Kiril Vasilev, Matteo Pariset, Andreas Wicki, Gabriele Gut, Charlotte Bunne

AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery

Preprint, 2025.

Charlotte Bunne, Yusuf Roohani, Yanay Rosen, Ankit Gupta, Xikun Zhang, Marcel Roed, Theo Alexandrov, Mohammed AlQuraishi, Patricia Brennan, Daniel B. Burkhardt, Andrea Califano, Jonah Cool, Abby F. Dernburg, Kirsty Ewing, Emily B. Fox, Matthias Haury, Amy E. Herr, Eric Horvitz, Patrick D. Hsu, Viren Jain, Gregory R. Johnson, Thomas Kalil, David R. Kelley, Shana O. Kelley, Anna Kreshuk, Tim Mitchison, Stephani Otte, Jay Shendure, Nicholas J. Sofroniew, Fabian Theis, Christina V. Theodoris, Srigokul Upadhyayula, Marc Valer, Bo Wang, Eric Xing, Serena Yeung-Levy, Marinka Zitnik, Theofanis Karaletsos, Aviv Regev, Emma Lundberg, Jure Leskovec, Stephen R. Quake

How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities

Cell, vol. 187, iss. 25, pp. P7045-7063, 2024.

Charlotte Bunne, Geoffrey Schiebinger, Andreas Krause, Aviv Regev, Marco Cuturi

Optimal transport for single-cell and spatial omics 

Nature Reviews Methods Primers, 2024.

Charlotte Bunne, Stefan Stark, Gabriele Gut, …, Mitchell Levesque, Kjong Van Lehmann, Lucas Pelkmans, Andreas Krause, Gunnar Rätsch

Learning Single-Cell Perturbation Responses using Neural Optimal Transport

Nature Methods, 2023.

Charlotte Bunne, Andreas Krause, Marco Cuturi

Supervised Training of Conditional Monge Maps

Advances in Neural Information Processing Systems (NeurIPS), 2022.