Charlotte Bunne

PhD Student in Machine Learning at ETH Zurich and Broad Institute of MIT and Harvard.


I am a PhD student in Computer Science at the Eidgenössische Technische Hochschule (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. During my PhD, I interned at Google Research and Apple Research, and visited the Broad Institute of MIT and Harvard as a research fellow. Before, I worked with Stefanie Jegelka as a Master student at the Massachusetts Institute of Technology (MIT). During my Master’s studies in Computational Biology, I also interned at IBM Research. My research focuses on dynamic optimal transport and optimal transport across incomparable domains with applications in single-cell genomics and protein design.

Throughout my undergraduate and graduate studies I’ve been a Fellow of the German National Academic Foundation. For my Master’s 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.

Contact: bunnec [at] ethz [dot] ch
Follow: Google Scholar bunnech @_bunnech


Nov, 2022 I am invited to speak at the Models, Inference & Algorithms (MIA) Initiative at the Broad Institute of MIT and Harvard.
Oct, 2022 I am co-organizer of the Molecular Machine Learning Conference (MoML). The first edition will take place October 2022 in-person at MIT.
Aug, 2022 I joined the Broad Institute of MIT and Harvard to work with Anne Carpenter and Shantanu Singh.
Mar, 2022 Recently, I was invited to speak at AMLD 2022 and as part of Molecular Modeling and Drug Discovery’s (M2D2) talk series, available here and here.
Dec, 2021 I am co-organizing the Optimal Transport and Machine Learning Workshop at NeurIPS 2021.

Selected Publications

  1. Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings
    Frederike Lübeck*, Charlotte Bunne*, Gabriele Gut, Jacobo Sarabia Castillo, Lucas Pelkmans, and David Alvarez Melis
    Under Review, 2022
    Spotlight Talk at NeurIPS Meaningful Representations of Life Workshop, 2022.
  2. 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.
  3. Nature Methods
    Learning Single-Cell Perturbation Responses using Neural Optimal Transport
    Charlotte Bunne*, Stefan Stark*, Gabriele Gut*, Jacobo Sarabia Castillo, Mitchell Levesque, Kjong Van Lehmann, Lucas Pelkmans, Andreas Krause, and Gunnar Rätsch
    Under Review at Nature Methods, 2022
    NeurIPS Workshop on Optimal Transport and Machine Learning (OTML), 2021.
    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.
  5. 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%).
  6. 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.
  7. 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.
  8. 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.
  9. 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.