Student Projects in LAS Group

We offer Semester Projects, Bachelor’s and Master’s theses in our group. Depending on your preference, there are opportunities for working on theory, methods, and applications. M.Sc. projects at LAS often result in publications at leading conferences.
A list of topics for which we are actively recruiting students is given below. If you don’t see a project below that fits well, but you are interested in the kind of research our lab does, feel free to still reach out. To learn more about the research done in the group, visit our recent publications. You can also learn more about research by individual group members.
If you are interested in working with us, you can send an application by clicking the button below. Make sure that your email includes: a résumé, a recent transcript of records, your intended start date, and we highly recommend that you mention the projects you are interested in, members of the group with whom you would like to work, or recent publications by the group relevant to your interests.

If you are a bachelors or masters student but not a student at ETH, please see the opportunity listed in Applications for Summer Research Fellowships.

Current Topics

The detailed project proposals can be downloaded only from the ETH domain.

Generalization for Meta-Learning and Personalized Federated Learning

We aim to find the correct formulation for many important problems in meta-learning and design algorithms to solve the correct formulation.



Keywords: Generalization, Meta-Learning, Personalized Federated Learning

Bayesian Optimization with Privacy

Develop private algorithms for Bayesian Optimization and show that privacy does not come for free, it takes a toll on performance.


Keywords: Bayesian Optimization, Differential Privacy, Meta-Learning

Protein Design and Game Theory

Optimize protein design by interpreting the design process as a game.


Keywords: active learning, multi-agent learning, game theory, experiment design

Bayesian Optimization with Graph Neural Networks

Employing Graph Neural Networks, develop a Bayesian Optimization algorithm, for problems on graph domains, which scales well with size of the domain. [At capacity for Spring semester 2023, may have openings for Fall semester.]


Keywords: Bayesian Optimization, Graph Neural Networks, Methodology, Applied

Deep Gaussian Processes on Manifolds

Develop the analog of deep Gaussian processes for modeling manifold to manifold maps (e.g. sphere to sphere).


Keywords: Gaussian processes, deep Gaussian processes, manifolds, uncertainty, variational inference

Truly non-smooth Matérn Gaussian Processes on Manifolds

Challenging semester project: propose a non-smooth approximation for intractable manifold Matérn kernels.


Keywords: Gaussian processes, geometry, kernels, manifolds, theory

Machine Learning for Population Dynamics

Design and model spatio-temporal population dynamics using recent techniques in optimal transport and machine learning with focus on applications in single-cell biology.


Keywords: optimal transport, spatio-temporal dynamics, partial and stochastic difference equations

Learning to Schedule Energy Generation

Use machine learning to solve mixed integer programs for energy scheduling faster, in collaboration with Hitachi Energy.


Keywords: applied, discrete optimization, integer programming, graph neural networks

Maching Learning for Protein Design

Learn to find the right mutation that leads to the right enzyme.


Keywords: applied, sequence-data

Applications of Machine Learning for Choosing Crop Varieties

Learning crop variety selection and management policies from data.


Keywords: applied, uncertainty quantification, active learning, reinforcement learning, remote sensing

Assimilation of crop growth models with remote sensing

Monitoring staple crops with satellite data.


Keywords: applied, remote sensing, sustainable agriculture

Meta-Learning Function Priors for Bayesian Optimization

Accelerate Bayesian optimization by meta-learning priors.


Keywords: meta-learning, active learning

Machine Learning for Converter Control

Algorithms for control of power electronics converters, in collaboration with Hitachi Energy.



Keywords: applied, reinforcement learning, control

General Areas

We offer projects in several general areas.
  • Probabilistic Approaches (Gaussian processes, Bayesian Deep Learning)
  • Discrete Optimization in ML
  • Online learning
  • Large-Scale Machine Learning
  • Causality
  • Active Learning
  • Bayesian Optimization
  • Reinforcement Learning
  • Meta Learning
  • Learning Theory

Examples of Previous Master Theses

BaCaDI: Bayesian Causal Discovery with Unknown Interventions
Alex Hägele with Jonas Rothfuss and Lars Lorch. AISTATS 2023. [paper]
MARS: Meta-Learning as Score Matching in the Function Space
Kruno Lehman with Jonas Rothfuss. ICLR 2023. [paper]

Neural Contextual Bandits without Regret
Parnian Kassraie with Andreas Krause. AISTATS 2022. [paper]
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning
Scott Sussex with Andreas Krause and Caroline Uhler. NeurIPS 2021. [paper] [blog]

DiBS: Differentiable Bayesian Structure Learning
Lars Lorch with Jonas Rothfuss. NeurIPS 2021. [paper] [blog]
PopSkipJump: Decision-Based Attack for Probabilistic Classifiers
Noman Ahmed Sheikh with Carl-Johann Simon-Gabriel. ICML 2021. [paper]

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