Student Projects in LAS GroupWe 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 TopicsThe detailed project proposals can be downloaded only from the ETH domain.
Active Causal DiscoveryDesign and analyse objectives for active causal structure discovery using recent techniques in the uncertainty estimation of challenging probabilistic models.
Keywords: causality, sequential decision making, uncertainty quantification
Learning to Schedule Energy GenerationUse 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
Local Causal Models in Model-Free Reinforcement LearningExplore how recent techniques using local casual models in RL can be applied to policy gradient methods.
Keywords: reinforcement learning, causality
Experimental Design: Learning to LearnFind a near-optimal policy that can learn on simple problems where the optimal policy is unintuitive.
Keywords: active learning, meta-learning
Maching Learning for Protein DesignLearn to find the right mutation that leads to the right enzyme.
Keywords: applied, sequence-data
Applications of Machine Learning for Choosing Crop VarietiesLearning crop variety selection and management policies from data.
Keywords: applied, uncertainty quantification, active learning, reinforcement learning, remote sensing
Stabilizing Recurrent Neural NetworksPreventing exploding gradients in RNNs on long time-series data.
Keywords: differential equations, vanishing/exploding gradients, time series
Meta-Learning Function Priors for Bayesian OptimizationAccelerate Bayesian optimization by meta-learning priors.
Keywords: meta-learning, active learning
Machine Learning for Converter ControlAlgorithms for control of power electronics converters, in collaboration with Hitachi Energy.
Keywords: applied, reinforcement learning, control
Safe Bayesian Optimization for Quadruped Locomotion ControlSample-efficient reinforcement learning for safely tuning control parameters of a real robot.
Keywords: bayesian optimization, reinforcement learning, robotic control
Urban Vehicle Trajectory Learning with TransformerShort-term trajectory prediction of multi-vehicle trajectories in complex urban driving contexts.
Keywords: sequential learning, Transformer, high-resolution vehicle trajectory
General AreasWe offer projects in several general areas.
- Probabilistic Approaches (Gaussian processes, Bayesian Deep Learning)
- Discrete Optimization in ML
- Online learning
- Large-Scale Machine Learning
- Active Learning
- Bayesian Optimization
- Reinforcement Learning
- Meta Learning
- Learning Theory
Examples of Previous Master Theses
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure LearningScott Sussex with Andreas Krause and Caroline Uhler. NeurIPS 2021. [paper] [blog]
Neural Contextual Bandits without RegretParnian Kassraie with Andreas Krause. AISTATS 2022. [paper]
DiBS: Differentiable Bayesian Structure LearningLars Lorch with Jonas Rothfuss. NeurIPS 2021. [paper] [blog]
PopSkipJump: Decision-Based Attack for Probabilistic ClassifiersNoman Ahmed Sheikh with Carl-Johann Simon-Gabriel. ICML 2021. [paper]
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