Doctoral Fellows
Doctoral students who are listed here are recipients of the highly competitive ETH AI Center Doctoral Fellowship. They have two mentors from the ETH AI Center faculty and are embedded with their respective research groups. The goal is to advance interdisciplinary AI research across discipline and department boundaries.
Afra Amini
natural language processing, social science, machine learning, deep learning
Alice Bizeul
probabilistic machine learning, representation learning, medical applications
Anej Svete
natural language processing, formal language theory, language modelling, representation learning, computational genomics
Anh Duong Vo
neuroscience, human-computer interaction, machine learning, modeling, eeg, eye tracking
Barna Pasztor
machine learning, artificial intelligence, multi-agent reinforcement learning, complex systems, sociology, economics
Bogdan Raonic
deep learning, scientific computing, AI for science, neural operators, physics-informed machine learning
Chenhao Li
robot learning, reinforcement learning, legged intelligence, learning from demonstrations, model-based adaptation
Daniil Dmitriev
mathematics of data science, random matrices, high dimensional probability
Elvis Nava
meta learning, representation learning, multi-modality, neuroscience, robotics
Emanuele Palumbo
generative models, multimodal learning, representations learning, AI for health
Hehui Zheng
vision-based reconstruction, soft robotics, physics-aware object tracking, machine learning
Jakub Macina
natural language processing, educational data science, discourse analysis, text generation, question generation
Javier Martinez
machine learning, healthcare, explainability, uncertainty quantification, causal inference
Javier Rando
natural language processing, large language models, safety, alignment, truthfulness
Johannes Weidenfeller
computer vision, machine learning, 3D scene reconstruction and understanding, generative models, virtual humans, AI for health and in the medical domain, applications in AR/VR
Junling Wang
natural language processing, large language models, human-computer interaction, machine learning, deep learning, educational data science
Karin Yu
physics-informed machine learning, graph neural networks, structural engineering and design
Kenza Amara
explainable AI, graph neural networks, graph theory, information retrieval, drug discovery, environmental sciences
Konstantin Donhauser
machine learning (theory and reliability), non-parametric and high-dimensional statistics, optimisation
Kristina Nikolić
safety, privacy, large language models, vision language models, alignment, robustness, trustworthiness
Levi Lingsch
machine learning for scientific computing, neural operators, symbolic AI, tokenization for PDEs, weather and climate
Lucia Pezzetti
machine learning, multi-agent reinforcement learning, optimization, bayesian statistics
Malte Londschien
statistics, machine learning, distributional robustness, causality, bioinformatics
Manish Prajapat
reinforcement learning, controls, submodularity, bayesian Optimization, multi-agent learning
Mike Michelis
physics-informed machine learning, computational design, numerical simulation, robotics
Pawel Czyz
unsupervised learning, probabilistic machine learning, Bayesian statistics, computational geometry and topology, cancer research
Raphaël Baur
ai in architecture, human-ai collaboration, intelligence augmentation, reinforcement learning, probabilistic programming
René Zurbrügg
robotics, computer vision, embodied ai, object manipulation, scene understanding
Riccardo De Santi
algorithmic decision-making, reinforcement learning, diffusion models, automatic scientific discovery
Samantha Biegel
environmental data science, machine learning, explainability, computer vision, scientific discovery, climate & ecosystem science
Tifanny Portela
robotics, reinforcement learning, embodied AI, whole-body manipulation, computer vision
Vera Balmer
physics-informed machine learning, neural operators, bridge design and concrete structures
Vinzenz Thoma
multi-agent reinforcement learning, market & mechanism design, algorithmic game theory, machine learning
Yarden As
reinforcement learning, constrained markov decision processes, meta-learning and bayesian inference
Yunke Ao
reinforcement learning, deep learning, robotics, optimal control, surgical planning