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

I am a PhD student in Computer Science at the Eidgenössische Technische Hochschule (ETH) Zürich under the supervision of Andreas Krause and member of the Learning and Adaptive Systems Group. 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 Zürich in the Computational Systems Biology Group. 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

• Summer 2021: Our paper Proximal Optimal Transport Modeling of Population Dynamics received the Best Paper Award.
• Autumn 2020: I am interning at Google Brain Paris working with Marco Cuturi.
• Summer 2020: Our paper Learning Graph Models for Template-Free Retrosynthesis received the Best Paper Award.
• Spring 2020: I was named a Google Generation Scholar.
• Winter 2019: I am one of ETH's representatives on the Global Young Scientist Summit (GYSS) in Singapore.
• Fall 2019: Honored to receive the ETH Medal, recognizing outstanding thesis projects. I also received the Willi Studer Prize of ETH Zürich, awarded to the best student in each ETH degree programme.
• Summer 2019: Paper Learning Generative Models Across Incomparable Spaces accepted at ICML 2019.

Publications

• JKOnet: Proximal Optimal Transport Modeling of Population Dynamics
  Charlotte Bunne, Laetitia Meng-Papaxanthos, Andreas Krause, Marco Cuturi
  Preprint, 2021.
  Best Paper Award and Contributed Talk at ICML Time-Series Workshop, 2021.
  [workshop paper][code (coming soon)]
  
  
• Multi-Scale Representation Learning on Proteins
  Charlotte Bunne*, Vignesh Ram Somnath*, Andreas Krause
  Preprint, 2021.
  Spotlight Talk at ICML Workshop on Computational Biology, 2021.
  [workshop paper][code (coming soon)]
  
  
• Learning Graph Models for Retrosynthesis Prediction
  Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause, Regina Barzilay
  Preprint, 2020.
  Best Paper Award and Contributed Talk at ICML Workshop on Graph Representation Learning and Beyond (GRL+), 2020.
  [paper][code (coming soon)]
  
  
• 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).

Teaching

Head Teaching Assistant

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

Teaching Assistant

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