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 @bunnech@sigmoid.social
Announcements
| Nov, 2022 | I am invited to speak at the Models, Inference & Algorithms (MIA) Initiative at the Broad Institute of MIT and Harvard. |
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| 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
- AISTATSBest Paper AwardProximal Optimal Transport Modeling of Population DynamicsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022Best Paper Award and Contributed Talk at ICML Time Series Workshop, 2021.
- ICLRIndependent SE (3)-Equivariant Models for End-to-End Rigid Protein DockingInternational Conference on Learning Representations (ICLR), 2022Contributed Talk at ELLIS Machine Learning for Molecule Discovery Workshop, 2021.Spotlight Talk at ICLR and Ranked Top 15 among 3326 Submissions (Top 0.4%).
- NeurIPSMulti-Scale Representation Learning on ProteinsAdvances in Neural Information Processing Systems (NeurIPS), 2021Spotlight Talk at ICML Computational Biology Workshop, 2021.