Charlotte Bunne

Assistant Professor in Computer Science and Life Sciences at EPFL.


This is my old webpage. I have since moved to EPFL and you can reach me via

I am a Postdoctoral Fellow at Genentech and Stanford University working with Aviv Regev and Jure Leskovec. I recently received my doctorate at ETH Zurich under the supervision of Andreas Krause and Marco Cuturi. I am part of the Institute for Machine Learning and the ETH AI Center. Before, I visited the Broad Institute of MIT and Harvard as a research fellow and worked with Stefanie Jegelka as a Master student at MIT. In the past, I interned at Google Research, Apple, and IBM Research. Throughout my undergraduate and graduate studies I’ve been a Fellow of the German National Academic Foundation. For my studies I received the Excellence Scholarship of the ETH Foundation. I am a recipient of the Google Generation Scholarship and holder of the ETH Medal.

My research aims to advance personalized medicine by utilizing machine learning and large-scale biomedical data. In my doctoral thesis, I developed novel machine learning algorithms based on neural optimal transport for modeling dynamical systems. Tailored to problems in single-cell biology, these tools allow us to predict fine-grained and continuous-time responses of single-cells to perturbations, i.e., the effect of a cancer drug on a patient’s tumor cells (see a feature in ETH Press). Proving successful within both the medical and the machine learning community, they provide a foundation for understanding cellular heterogeneity and personalized therapies in biomedical research.

In case you wonder how optimal transport allow us to study dynamical systems, how it connects to control theory, flow matching, and diffusion models, and how it is advancing molecular biology research, have a look at our tutorial at the International Conference on Machine Learning (ICML).

Follow: Google Scholar bunnech @_bunnech


Dec, 2023 I am hiring PhDs and PostDocs. Please apply!
Sep, 2023 Marco Cuturi and I gave a tutorial on Optimal Transport in Learning, Control, and Dynamical Systems at ICML 2023. Recording, slides, and script are now online!
Aug, 2023 I am organizing two workshops at NeurIPS 2023: The Optimal Transport and Machine Learning Workshop and the Workshop on Diffusion Models, taking place Dec 15 to 16!
Apr, 2023 I am co-organizer of the Molecular Machine Learning Conference (MoML). After the sucess of the first edition at MIT, the second one will take place May 29, 2023 in-person at Mila.
Mar, 2023 I am a Panelist at the Structured Probabilistic Inference & Generative Modeling Workshop taking place at ICML 2023.

Selected Publications

  1. Nature Methods
    Research Briefing
    Learning Single-Cell Perturbation Responses using Neural Optimal Transport
    Charlotte Bunne, Stefan Stark, Gabriele Gut, ..., Mitchell Levesque, Kjong Van Lehmann, Lucas Pelkmans, Andreas Krause, and Gunnar Rätsch
    Nature Methods, 2023
    NeurIPS Workshop on Optimal Transport and Machine Learning (OTML), 2021.
  2. UAI
    Aligned Diffusion Schrödinger Bridges
    Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh, Maria Rodriguez Martinez, Andreas Krause, and Charlotte Bunne
    Conference on Uncertainty in Artificial Intelligence (UAI), 2023
    ICML Workshop on New Frontiers for Learning, Control, and Dynamical Systems, 2023.
    The Schrödinger Bridge between Gaussian Measures has a Closed Form
    Charlotte Bunne, Ya-Ping Hsieh, Marco Cuturi, and Andreas Krause
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
    ICML Workshop on Continuous Time Methods for Machine Learning, 2022.
    Oral Presentation at AISTATS (Top 1.9% of Submitted Papers).
  4. NeurIPS
    Supervised Training of Conditional Monge Maps
    Charlotte Bunne, Andreas Krause, and Marco Cuturi
    Advances in Neural Information Processing Systems (NeurIPS), 2022
    ICML Workshop on Interpretable Machine Learning in Healthcare (IMLH), 2022.
    Best Paper Award
    Proximal Optimal Transport Modeling of Population Dynamics
    Charlotte Bunne, Laetitia Meng-Papaxanthos, Andreas Krause, and Marco Cuturi
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
    Best Paper Award and Contributed Talk at ICML Time Series Workshop, 2021.
  6. ICLR
    Independent SE (3)-Equivariant Models for End-to-End Rigid Protein Docking
    Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, and Andreas Krause
    International Conference on Learning Representations (ICLR), 2022
    Contributed Talk at ELLIS Machine Learning for Molecule Discovery Workshop, 2021.
    Spotlight Talk at ICLR and Ranked Top 15 among 3326 Submissions (Top 0.4%).
  7. Featured Cover
    Machine intelligence for chemical reaction space
    Philippe Schwaller, Alain C Vaucher, Ruben Laplaza, Charlotte Bunne, Andreas Krause, Clemence Corminboeuf, and Teodoro Laino
    Wiley Interdisciplinary Reviews: Computational Molecular Science, 2022
    Selected as Featured Cover of Volume 12, Issue 5.
  8. NeurIPS
    Multi-Scale Representation Learning on Proteins
    Charlotte Bunne, Vignesh Ram Somnath, and Andreas Krause
    Advances in Neural Information Processing Systems (NeurIPS), 2021
    Spotlight Talk at ICML Computational Biology Workshop, 2021.
  9. NeurIPS
    Best Paper Award
    Learning Graph Models for Retrosynthesis Prediction
    Vignesh Ram Somnath, Charlotte Bunne, Connor Coley, Andreas Krause, and Regina Barzilay
    Advances in Neural Information Processing Systems (NeurIPS), 2021
    Best Paper Award and Contributed Talk at ICML Workshop on Graph Representation Learning and Beyond, 2019.
  10. ICML
    Best Paper Award
    Learning Generative Models across Incomparable Spaces
    Charlotte Bunne, David Alvarez-Melis, Andreas Krause, and Stefanie Jegelka
    International Conference on Machine Learning (ICML), 2019
    Best Paper Award and Contributed Talk at NeurIPS Workshop on Relational Representation Learning, 2018.