Charlotte Bunne
PhD Candidate in Computer Science, ETH Zurich
Institute for Machine Learning and ETH AI Center
Learning and Adaptive Systems Group
bunnec at inf.ethz.ch

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.

News

• I joined the Broad Institute of MIT and Harvard to work with Anne Carpenter and Shantanu Singh this summer and fall.
• 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.
• I am co-organizing the Optimal Transport and Machine Learning Workshop at NeurIPS 2021.

Publications

• Supervised Training of Conditional Monge Maps
  Charlotte Bunne, Andreas Krause, Marco Cuturi
  Preprint, 2022.
  ICML Workshop on Interpretable Machine Learning in Healthcare (IMLH), 2022.
  [paper][code (coming soon)]
  
  
• Learning Single-Cell Perturbation Responses using Neural Optimal Transport
  Charlotte Bunne*, Stefan G. Stark*, Gabriele Gut*, Jacobo Sarabia Del Castillo, ‪Mitch Levesque, Kjong-Van Lehmann, Lucas Pelkmans, Andreas Krause, Gunnar Rätsch
  In Review, 2022.
  NeurIPS Optimal Transport and Machine Learning (OTML) Workshop, 2021.
  [paper][code]
  
  
• Machine Intelligence for Chemical Reaction Space
  Philippe Schwaller, Alain Vaucher, Ruben Laplaza, Charlotte Bunne, Andreas Krause, Clemence Corminboeuf, Teodoro Laino
  WIREs Computational Molecular Science, 2022.
  [paper]
  
  
• Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges
  Charlotte Bunne*, Ya-Ping Hsieh*, Marco Cuturi, Andreas Krause
  Preprint, 2022.
  ICML Workshop on Continuous Time Methods for Machine Learning, 2022.
  [paper][code (coming soon)]
  
  
• Optimal Transport Tools (OTT): A JAX Toolbox for All Things Wasserstein
  Marco Cuturi, Laetitia Meng-Papaxanthos, Yingtao Tian, Charlotte Bunne, Geoff Davis, Olivier Teboul
  Preprint, 2022.
  [paper][code]
  
  
• Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
  Octavian-Eugen Ganea*, Xinyuan Huang*, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi S. Jaakkola, Andreas Krause
  Spotlight at International Conference on Learning Representations (ICLR), 2022.
  Contributed Talk at ELLIS Workshop on Machine Learning for Molecule Discovery, 2021.
  [paper][code][press]
  
  
• Proximal Optimal Transport Modeling of Population Dynamics
  Charlotte Bunne, Laetitia Meng-Papaxanthos, Andreas Krause, Marco Cuturi
  International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
  Best Paper Award and Contributed Talk at ICML Time-Series Workshop, 2021.
  [paper][code][short talk][talk]
  
  
• Multi-Scale Representation Learning on Proteins
  Charlotte Bunne*, Vignesh Ram Somnath*, Andreas Krause
  Neural Information Processing Systems (NeurIPS), 2021.
  Spotlight Talk at ICML Workshop on Computational Biology, 2021.
  [paper][workshop paper][code][short talk][talk]
  
  
• Learning Graph Models for Retrosynthesis Prediction
  Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause, Regina Barzilay
  Neural Information Processing Systems (NeurIPS), 2021.
  Best Paper Award and Contributed Talk at ICML Workshop on Graph Representation Learning and Beyond (GRL+), 2020.
  [paper][code][talk]
  
  
• COSIFER: A Python Package for the Consensus Inference of Molecular Interaction Networks
  Matteo Manica*, Charlotte Bunne*, Roland Mathis*, Joris Cadow, Mehmet Eren Ahsen, Gustavo Stolovitzky, María Rodríguez Martínez
  Bioinformatics, 2020.
  [paper][code]
  
  
• Learning Generative Models across Incomparable Spaces
  Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka
  International Conference on Machine Learning (ICML), 2019.
  Best Paper Award and Contributed Talk at NeurIPS Workshop on Relational Representation Learning (R2L), 2018.
  [paper][workshop paper][code]
  
  
• Exponential Growth of Glioblastoma In Vivo Driven by Rapidly Dividing and Outwardly Migrating Cancer Stem Cells
  Lisa Buchauer, Muhammad Amir Khan, Yue Zhuo, Chunxuan Shao, Peng Zou, Weijun Feng, Mengran Qian, Gözde Bekki, Charlotte Bunne, Anna Neuerburg, Azer Aylin Acikgöz, Mona Tomaschko, Zhe Zhu, Heike Alter, Katharina Hartmann, Olga Friesen, Klaus Hexel, Thomas Höfer, Hai-Kun Liu
  Preprint, 2019
  [paper]
  
  
• Backbone circularization of Bacillus subtilis family 11 xylanase increases its thermostability and its resistance against aggregation
  Max C. Waldhauer, Silvan N. Schmitz, Constantin Ahlmann-Eltze, Jan G. Gleixner, Carolin C. Schmelas, Anna G. Huhn, Charlotte Bunne, Magdalena Büscher, Max Horn, Nils Klughammer, Jakob Kreft, Elisabeth Schäfer, Philipp A. Bayer, Stephen G. Krämer, Julia Neugebauer, Pierre Wehler, Matthias P. Mayer, Roland Eils, Barbara Di Ventura
  Molecular BioSystems, 2015.
  [paper][doi]
  

Extracurricular Activities

Women in Data Science Conference (WiDS)

Women in Data Science (WiDS) is a one day technical conference aiming to inspire and educate data scientists worldwide, regardless of gender, and support the women in the field. I was an ambassador and part of the organisation team of the WiDS Conference in Zürich 2018.

iGEM Competition

I joined the iGEM Team Heidelberg 2014 of Heidelberg University. We developed a heat stable DNA-methyltransferase for a PCR 2.0 and designed a standardized intein-based mechanism for protein modifications (BBF RFC 105).

Service

Reviewer

Nature Communications, Neural Information Processing Systems (NeurIPS), International Conference on Machine Learning (ICML), International Conference on Artificial Intelligence and Statistics (AISTATS), Women in Machine Learning (WiML) Workshop.

Selected Top Reviewer at International Conference on Artificial Intelligence and Statistics (AISTATS).

Workshop Organizer

Optimal Transport and Machine Learning (OTML) Workshop 2021 at Neural Information Processing Systems (NeurIPS) Conference.

Teaching

Head Teaching Assistant

• Fall 2022, Spring 2021: Introduction to Machine Learning (largest class at ETH)

Teaching Assistant

• Fall 2022, Fall 2020, Fall 2019: Probabilistic Artificial Intelligence
• Spring 2020: Introduction to Machine Learning
• Spring 2019: Fairness, Explainability, and Accountability for Machine Learning