Teaching

Tutorial on Optimal Transport for Learning, Control, and Dynamical Systems

This tutorial introduces the foundations of optimal transport (OT), provides a unifying perspective that underlines the centrality of OT to the wealth of developments in dynamical system theory and control theory, drawing connections between these approaches both in algorithms and theory, and provide some directions on how the field can further evolve to create new machine learning methods grounded on this exciting toolbox.

Speakers: Charlotte Bunne and Marco Cuturi
Originally presented at ICML 2023.

Introduction to Machine Learning

The course introduces the foundations of learning and making predictions from data, reviews basic concepts such as trading goodness of fit and model complexity, discusses important machine learning algorithms used in practice, and provides hands-on experience in a course project.

Head Teaching Assistant: Summer and Fall Semester 2021
With ~1000 students ETH’s largest class.

Teaching Assistant: Every Summer Semester since 2020

Probabilistic Artificial Intelligence

How can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit “intelligent” behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? The course studies core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks and robotics.

Teaching Assistant: Every Fall Semester since 2019

Fairness, Explainability, and Accountability for Machine Learning

The course is focused on the ethical implications of applying big data and machine learning tools to socially-sensitive domains, and presents technical solutions for addressing these issues.

Teaching Assistant: Summer Semester 2019

Thesis Supervision

In addition to these courses, I supervise students at both the Bachelor’s and Master’s level. I have successfully supervised several students since 2019 and prepared them for both, their next academic position or for industry. Their work has been successfully published in prestigious machine learning conferences, was selected for paper awards and spotlight presentations, and featured in the press.

Science Education at German Cancer Research Center

Science education of mathematically and technically particularly gifted high school students.

Synthetic Biology

Courses on in silico and in vitro bioengineering, project design, and scientific communication.

Group Leader and Mentor: 2012 - 2016 (~20 Students)

Biophysics

Courses on concepts in theoretical biology and physics, project design, and scientific communication.

Mentor: 2014 - 2015 (~10 Students)