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

Afra Amini

natural language processing, social science, machine learning, deep learning

Alice Bizeul

Alice Bizeul

probabilistic machine learning, representation learning, medical applications

Alizee Pace

Alizée Pace

machine learning, reinforcement learning, causal inference, medical data science

Anh Duong Vo

Anh Duong Vo

neuroscience, human-computer interaction, machine learning, modeling, eeg, eye tracking

Anej Svete

Anej Svete

natural language processing, formal language theory, language modelling, representation learning, computational genomics

Barna Pasztor

Barna Pasztor

machine learning, artificial intelligence, multi-agent reinforcement learning, complex systems, sociology, economics

Bogdan Raonic

Bogdan Raonic

deep learning, scientific computing, AI for science, neural operators, physics-informed machine learning

Boqi Chen

Boqi Chen

machine learning, computer vision, computational pathology

Chehao Li

Botao Ye

computer vision, 3D generation, scene reconstruction, robotics

Chehao Li

Chenhao Li

robot learning, reinforcement learning, legged intelligence, learning from demonstrations, model-based adaptation

Daniil Dmitriev

Daniil Dmitriev

mathematics of data science, random matrices, high dimensional probability

Elvis Nava

Elvis Nava

meta learning, representation learning, multi-modality, neuroscience, robotics

Elisabetta Fedele

Elisabetta Fedele

3D scene understanding, 3D reconstruction, diffusion models

Emanuele Palumbo

Emanuele Palumbo

generative models, multimodal learning, representations learning, AI for health

Ghjulia Sialelli

Ghjulia Sialelli

probabilistic deep learning, AI for sustainability, computer vision, explainability

Giulia Lanzilotta

Giulia Lanzilotta

biologically inspired learning, task-agnostic learning, measures of intelligence, continual learning, memorisation

Hehui Zheng

Hehui Zheng

vision-based reconstruction, soft robotics, physics-aware object tracking, machine learning

Ilyas Fatkullin

Ilyas Fatkhullin

large-scale optimization, reinforcement learning, federated learning

Jakub Macina

Jakub Macina

natural language processing, educational data science, discourse analysis, text generation, question generation

Javier Martinez

Javier Martinez

machine learning, healthcare, explainability, uncertainty quantification, causal inference

Javier Rando

Javier Rando

natural language processing, large language models, safety, alignment, truthfulness

Jelena Trisovic

Jelena Trisovic

optimization, control, computer vision, reinforcement learning

Jiaoda Li

Jiaoda Li

natural language processing, machine learning

Johannes Weidenfeller

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

Junling Wang

natural language processing, large language models, human-computer interaction, machine learning, deep learning, educational data science

Karin Yu

Karin Yu

physics-informed machine learning, graph neural networks, structural engineering and design

Kenza Amara

Kenza Amara

explainable AI, graph neural networks, graph theory, information retrieval, drug discovery, environmental sciences

Konstantin Donhauser

Konstantin Donhauser

machine learning (theory and reliability), non-parametric and high-dimensional statistics, optimisation

Kristina Nikolic

Kristina Nikolić

safety, privacy, large language models, vision language models, alignment, robustness, trustworthiness

Lucia Pezzetti

Lucia Pezzetti

machine learning, multi-agent reinforcement learning, optimization, bayesian statistics

Malte Londschien

Malte Londschien

statistics, machine learning, distributional robustness, causality, bioinformatics

Manish Prajapat

Manish Prajapat

reinforcement learning, controls, submodularity, bayesian Optimization, multi-agent learning

Mike Michelis

Mike Michelis

physics-informed machine learning, computational design, numerical simulation, robotics

Paola Malsot

Paola Malsot

statistics, machine learning, bioinformatics

Pawel Czyz

Pawel Czyz

unsupervised learning, probabilistic machine learning, Bayesian statistics, computational geometry and topology, cancer research

Tifanny Portela

Tifanny Portela

robotics, reinforcement learning, embodied AI, whole-body manipulation, computer vision

Raphaël Baur

Raphaël Baur

ai in architecture, human-ai collaboration, intelligence augmentation, reinforcement learning, probabilistic programming

Rene Zurbrügg

René Zurbrügg

robotics, computer vision, embodied ai, object manipulation, scene understanding

Riccardo de Santis

Riccardo De Santi

algorithmic decision-making, reinforcement learning, diffusion models, automatic scientific discovery

Samantha Biegel

Samantha Biegel

environmental data science, machine learning, explainability, computer vision, scientific discovery, climate & ecosystem science

Vera Balmer

Vera Balmer

physics-informed machine learning, neural operators, bridge design and concrete structures

Vinzenz Thoma

Vinzenz Thoma

multi-agent reinforcement learning, market & mechanism design, algorithmic game theory, machine learning

Yarden As

Yarden As

reinforcement learning, constrained markov decision processes, meta-learning and bayesian inference

Yunke Ao

Yunke Ao

reinforcement learning, deep learning, robotics, optimal control, surgical planning