ELLIS PhD and PostDoc Program

Thumb ticker md nadine rueegg  phd student

3D Human Pose Estimation

Nadine Rüegg (Ph.D. Student)

I'm working at the intersection between Computer Vision and Machine Learning. Specifically, I focus on 3D human shape and pose estimation and have strong interests in unsupervised learning. I aim to find a good balance between labeling effort and performance.

Primary Host: Konrad Schindler (ETH Zürich)
Exchange Host: Michael J. Black (Max Planck Institute for Intelligent Systems and Amazon)
PhD Duration: 01 June 2017 - 01 June 2022
Exchange Duration: 01 June 2018 - 01 June 2019
Thumb ticker md songyou peng  phd student

3D Vision Meets Deep Learning

Songyou Peng (Ph.D. Student)

My research interests lie at the intersection of deep learning and computer vision, especially 3D vision. What could be the optimal 3D representation? How to effectively and efficiently combine deep learning with 3D vision tasks? How to tackle multiple 3D tasks together with less or no supervision? During the period...

Primary Host: Marc Pollefeys (ETH Zürich & Microsoft)
Exchange Host: Andreas Geiger (University of Tübingen & Max Planck Institute for Intelligent Systems)
PhD Duration: 01 September 2019 - Ongoing
Exchange Duration: 01 June 2020 - 01 June 2021
Thumb ticker md steffen schneider

Adaptation and Robustness in Brains and Machines

Steffen Schneider (Ph.D. Student)

Understanding the mechanisms underlying robust learning and efficient adaptation is an open problem both in neuroscience and machine learning. While robustness and domain adaptation in ML is commonly studied with computer vision tasks, adaptation research in neuroscience has been traditionally carried out in sensori...

Primary Host: Matthias Bethge (University of Tübingen)
Exchange Host: Mackenzie Mathis (Rowland Institute at Harvard University & EPFL)
PhD Duration: 01 November 2019 - 31 October 2022
Exchange Duration: 18 February 2020 - 31 March 2020
Thumb ticker md vincent stimper  phd student

Analyzing Materials through Machine Learning

Vincent Stimper (Ph.D. Student)

Measurement techniques to characterize the properties of materials, such as photoemission spectroscopy, were continuously improved throughout the last decades. This led to a drastic increase of the measured data in terms of size, resolution, and complexity. Analyzing those dataset poses challenges, e.g. processing t...

Primary Host: Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
Exchange Host: José Miguel Hernández-Lobato (University of Cambridge)
PhD Duration: 01 January 2020 - Ongoing
Exchange Duration: 01 January 2020 - Ongoing
Thumb ticker md aaron klein  postdoc

Bayesian Continual Learning

Aaron Klein (PostDoc)

In many real world scenarios, an agent has to face a dynamic environment, where data is not drawn i.i.d from a stationary distribution, but rather changes over time. Continual learning represents a general framework for such scenarios that, in contrast to the standard case where a fixed training and test set is provi...

Primary Host: Cédric Archambeau (Amazon Research)
Exchange Host: Richard E. Turner (University of Cambridge)
PostDoc Duration: 01 July 2019 - 30 June 2021
Exchange Duration: 01 June 2020 - 30 June 2021
Thumb ticker md amir hossein karimi  phd student

Causality of Enhanced Model Interpretability

Amir-Hossein Karimi (Ph.D. Student)

As machine learning is increasingly used to inform decision-making in consequential real-world settings (e.g., pre-trial bail, loan approval, or prescribing life-altering medication), it becomes important to explain how the system arrived at its decision, and also suggest actions to achieve a favorable decision. My ...

Primary Host: Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
Exchange Host: Thomas Hofmann (ETH Zürich)
PhD Duration: 01 October 2018 - Ongoing
Exchange Duration: - Ongoing
Thumb ticker md meike zehlike  phd student

Fairness and Discrimination Mitigation in Rankings

Meike Zehlike (Ph.D. Student)

My research interests center around artificial intelligence and its social impact, algorithmic discrimination, fairness and algorithmic exploitation. In my PhD thesis, I develop methods to detect and mitigate discriminatory patterns that make their way into ranking models. Such biased models usually lead to disparit...

Primary Host: Krishna Gummadi (Max Planck Institute for Software Systems)
Exchange Host: Carlos Castillo (Universitat Pompeu Fabra)
PhD Duration: 01 April 2016 - Ongoing
Exchange Duration: 01 February 2018 - 31 May 2018
Thumb ticker md portrait

Flexible Densities for Deep Generative Models

Didrik Nielsen (Ph.D. Student)

Probability distributions play a central role in machine learning. For probabilistic modeling, they are used as likelihoods and prior distributions, whereas in variational inference, they are employed as approximate posterior distributions. The probability distributions typically used in practice tend to be simple...

Primary Host: Ole Winther (University of Copenhagen & Technical University of Denmark)
Exchange Host: Max Welling (University of Amsterdam)
PhD Duration: 01 January 2019 - 31 December 2019
Exchange Duration: 13 January 2020 - 29 May 2020
Thumb ticker md giambattista parascandolo  phd student

Generalization out of distribution

Giambattista Parascandolo (Ph.D. Student)

While current techniques in machine learning have been showing tremendous success at generalization in the i.i.d. setting when large quantities of data and compute are available, performance consistently drops as soon as we try to extend our models to data out of distribution. Tasks such as transfer learning, meta-l...

Primary Host: Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
Exchange Host: Thomas Hofmann (ETH Zürich)
PhD Duration: 01 March 2017 - 01 March 2021
Exchange Duration: 01 February 2020 - 01 February 2021
Thumb ticker md brandstetter johannes

Geometric deep learning and partial differential equations

Johannes Brandstetter (PostDoc)

Partial differential equations (PDEs) are used in physics, engineering and many other scientific disciplines. While their numerical solutions have been a longstanding challenge, deep learning methods offer an appealing meshfree approach. On the other hand, PDEs are a language in which we can express inductive biases...

Primary Host: Sepp Hochreiter (JKU Linz)
Exchange Host: Max Welling (University of Amsterdam)
PostDoc Duration: 01 July 2018 - 30 June 2024
Exchange Duration: 01 September 2020 - 31 August 2022
Thumb ticker md julius von k%c3%bcgelgen  phd student

Independent causal mechanisms in machine learning

Julius von Kügelgen (Ph.D. Student)

Machine learning (ML) approaches often operate under the assumption of independent and identically distributed (i.i.d.) random variables, and many of the impressive recent achievements can be phrased as supervised learning problems in such an i.i.d. setting. Due to changes in the environment, different measurement d...

Primary Host: Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
Exchange Host: Adrian Weller (University of Cambridge & The Alan Turing Institute)
PhD Duration: 01 September 2018 - 28 February 2023
Exchange Duration: 01 September 2018 - 31 August 2019
Thumb ticker md gresele luigi

Independent Component Analysis: linear and nonlinear, single and multi-view. Identifiability and estimation algorithms

Luigi Gresele (Ph.D. Student)

Independent Component Analysis (ICA) provides a principled framework for unsupervised feature extraction and blind source separation, with ubiquitous applications in signal processing, astronomy and neuroimaging. In the multi-view setting, the aim is to extract common sources of variability from multiple related obs...

Primary Host: Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
Exchange Host: Aapo Hyvärinen (INRIA & University of Helsinki)
PhD Duration: 01 October 2017 - Ongoing
Exchange Duration: 01 October 2019 - 31 December 2019
Thumb ticker md mustafa mert %c3%87elikok  phd student

Interactive AI with a Theory of Mind

Mustafa Mert Çelikok (Ph.D. Student)

In human-AI collaboration, learning a good model of the human is important for an autonomous learning system which aims to help its users in the most efficient way possible. Unfortunately, the data in human-AI interaction is scarce due to the online nature of the tasks. Additional difficulties arise from the bounded...

Primary Host: Samuel Kaski (Aalto University & Finnish Centre for AI)
Exchange Host: Frans A. Oliehoek (Delft University of Technology)
PhD Duration: 01 February 2019 - 01 February 2023
Exchange Duration: 15 September 2020 - 15 March 2021
Thumb ticker md zhuo su  phd student

Learning compact and efficient feature representations

Zhuo Su (Ph.D. Student)

Energy efficient sensing and computing is vital at all levels, from the smallest sensor like the chip to ultra high performance processors and systems like the cloud, especially in the post Moore's Law era. Energy efficient AI enables AI to move beyond the cloud and to reach the edge, which is critical to the progre...

Primary Host: Li Liu (University of Oulu)
Exchange Host: Max Welling (University of Amsterdam)
PhD Duration: 01 October 2018 - 31 December 2022
Exchange Duration: 01 March 2021 - 31 August 2021
Thumb ticker md despoina paschalidou

Learning Deep Models with Primitive-based Representations

Despoina Paschalidou (Ph.D. Student)

My research so far seeks to give an answer to a very simple question, how can we teach machines to learn to see in 3D? Or in other words, what is the best representation, that would allow us to capture the world such that a machine would be able to robustly perceive it? Humans develop a common-sense understanding of...

Primary Host: Andreas Geiger (University of Tübingen & Max Planck Institute for Intelligent Systems)
Exchange Host: Luc Van Gool (ETH Zürich & KU Leuven)
PhD Duration: 01 April 2017 - 31 March 2021
Exchange Duration: 01 February 2019 - 31 July 2020
Thumb ticker md xu chen  phd student

Learning real-world perception in simulations

Xu Chen (Ph.D. Student)

The difficulty of acquiring annotated real-world data has limited the applicability of deep learning in many computer vision tasks. As one way to overcome this limitation, training deep networks with synthetic images from simulation has demonstrated its potential. However, current simulations still lack diversity an...

Primary Host: Otmar Hilliges (ETH Zürich)
Exchange Host: Andreas Geiger (University of Tübingen & Max Planck Institute for Intelligent Systems)
PhD Duration: 01 March 2019 - 28 February 2023
Exchange Duration: 01 January 2021 - 31 December 2021
Thumb ticker md w01 small

Learning Robotic Manipulation from Instructional Videos

Vladimir Petrik (PostDoc)

The objective of the project is to enable robots to learn new manipulation skills from instructional videos available online. We will study how instructional videos could be used to overcome the sparse reward problem in reinforcement learning. The sparse reward complicates reinforcement learning by assigning the rew...

Primary Host: Josef Sivic (INRIA & Czech Technical University)
Exchange Host: Ivan Laptev (INRIA)
PostDoc Duration: 01 April 2019 - 31 March 2020
Exchange Duration: 01 October 2019 - 31 January 2020
Thumb ticker md mineeva olga  phd student

Machine learning approach for multi-scale genomics problems

Olga Mineeva (Ph.D. Student)

In the age of rapid growth of available biological sequencing data enabled by the recent advances in sequencing technologies, there is an opportunity to answer biological and health-related questions at a more detailed level. At the same time, the amount of data allows the use of sophisticated methods, such that dee...

Primary Host: Gunnar Rätsch (ETH Zürich)
Exchange Host: Isabel Valera (Max Planck Institute for Intelligent Systems)
PhD Duration: 01 November 2018 - 31 October 2022
Exchange Duration: 01 January 2020 - 30 June 2020
Thumb ticker md giulia muzio  phd student

Machine Learning for Biological Network Analysis

Giulia Muzio (Ph.D. Student)

The main objective of my project is to develop methods for network-based genome-wide association studies (GWAS) that combine computational efficiency, statistical power and interpretability, thereby enabling the discovery of biological pathways underlying complex phenotypic traits. GWAS aim to identify statistical ...

Primary Host: Karsten Borgwardt (ETH Zürich)
Exchange Host: Volker Tresp (LMU München & Siemens AG)
PhD Duration: 01 September 2019 - 31 August 2022
Exchange Duration: 01 February 2021 - 30 April 2021
Thumb ticker md fernando iglesias suarez  postdoc

Machine learning for improving climate models

Fernando Iglesias-Suarez (PostDoc)

Earth system and climate models are fundamental to understanding and projecting climate change. Although they have improved significantly over the last decades, considerable biases compared to observations and uncertainties in their projections still remain. We will take a new approach by harvesting output from high...

Primary Host: Veronika Eyring (German Aerospace Center (DLR) & University of Bremen)
Exchange Host: Gustau Camps-Valls (Universitat de València)
PostDoc Duration: 01 December 2019 - Ongoing
Exchange Duration: 01 July 2020 - 31 December 2022
Thumb ticker md john bradshaw  phd student

Machine Learning for Molecules

John Bradshaw (Ph.D. Student)

Machine Learning has enormous potential in augmenting scientists' capabilities in discovering novel drugs or new materials. To achieve this we need to develop ML models that can accurately predict properties of molecules and their interactions, as well as techniques that enable intelligent searching of discrete and ...

Primary Host: José Miguel Hernández-Lobato (University of Cambridge)
Exchange Host: Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
PhD Duration: 01 October 2016 - Ongoing
Exchange Duration: 01 November 2018 - 31 December 2019
Thumb ticker md mohamed ibrahim  phd student

Machine Learning for the Fusion of Remote Sensing and Tweets Data for Green Space Analysis

Mohamed Ibrahim (Ph.D. Student)

There is increasing evidence that people with higher access to urban green spaces have better mental health and well-being. This project aims to examine the impact of urban green spaces on mental health, and what features play the biggest role. The combination of airborne or space-borne remote sensing images and geo...

Primary Host: Xiaoxiang Zhu (German Space Center (DLR) & Technical University of Munich)
Exchange Host: Devis Tuia (Wageningen University and Research)
PhD Duration: 01 October 2019 - 30 September 2023
Exchange Duration: 01 September 2020 - 31 December 2020
Thumb ticker md omar rivasplata  phd student

PAC-Bayes Analysis and Derived Algorithms

Omar Rivasplata (Ph.D. Student)

The main focus of my current research is on generalization bounds, specifically the kind called PAC-Bayes bounds, in relation to the phenomenon referred to as "generalization" in machine learning. The generalization problem has an easy-read description: After an algorithm has been trained based on a finite list of e...

Primary Host: John Shawe-Taylor (UCL)
Exchange Host: Csaba Szepesvari (Google DeepMind & University of Alberta)
PhD Duration: 27 November 2017 - 26 November 2020
Exchange Duration: 01 April 2020 - 30 June 2020
Thumb ticker md vincent fortuin  phd student

Priors and Inference for Deep Probabilistic Models

Vincent Fortuin (Ph.D. Student)

While deep learning techniques have led to impressive advances in supervised and representation learning, this was mostly in domains where large homogeneous sets of structured data are available. In contrast, probabilistic models are more data-efficient and often provide better interpretability as well as uncertaint...

Primary Host: Gunnar Rätsch (ETH Zürich)
Exchange Host: Richard E. Turner (University of Cambridge)
PhD Duration: 01 November 2017 - Ongoing
Exchange Duration: 01 August 2019 - 01 November 2019
Thumb ticker md joonas j%c3%a4lk%c3%b6  phd student

Privacy-preserving data sharing via probabilistic models

Joonas Jälkö (Ph.D. Student)

Widespread sharing of data would facilitate rapid progress in data science. However, due to privacy constraints, sensitive data cannot be made public. My research aims to learn a generative model from the sensitive data under strict privacy guarantees from differential privacy. The generative model is then used to d...

Primary Host: Samuel Kaski (Aalto University & Finnish Centre for AI)
Exchange Host: Mihaela van der Schaar (University of Cambridge, The Alan Turing Institute & University of California)
PhD Duration: 16 November 2018 - 16 November 2022
Exchange Duration: - Ongoing
Thumb ticker md matteo turchetta  phd student

Safety and robustness in reinforcement learning

Matteo Turchetta (Ph.D. Student)

Reinforcement learning has achieved impressive results in recent years through learning by trial and error. However, many real-world applications are subject to safety constraints that should not be violated at any time. In these cases, autonomous agents that can reason about safety while exploring and learning abou...

Primary Host: Andreas Krause (ETH Zürich)
Exchange Host: Sebastian Trimpe (Max Planck Institute for Intelligent Systems)
PhD Duration: 26 September 2016 - 31 March 2021
Exchange Duration: 26 September 2018 - 26 September 2019
Thumb ticker md yuxing xie  phd student

Semantic Labeling of Multisensory 3D Point Clouds

Yuxing Xie (Ph.D. Student)

Benefiting from the unprecedented technology development of sensors, platforms and algorithms for 3D data acquisition and generation, point clouds are becoming more significant and accessible than before. In addition to widely-used LiDAR point clouds, satellite stereo imagery- and InSAR- based 3D data also cannot be...

Primary Host: Xiaoxiang Zhu (German Space Center (DLR) & Technical University of Munich)
Exchange Host: Konrad Schindler (ETH Zürich)
PhD Duration: 01 August 2018 - Ongoing
Exchange Duration: 01 September 2020 - 31 December 2020
Thumb ticker md niki kilbertus  phd student

Socially Beneficial Machine Learning

Niki Kilbertus (Ph.D. Student)

As machine learning touches upon all areas of our daily lives, it is increasingly deployed to make or support consequential decisions about individuals. Such applications raise concerns about privacy violations, the fairness of algorithms, as well as the long-term impact automated decisions might have on individuals...

Primary Host: Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
Exchange Host: Carl Edward Rasmussen (University of Cambridge)
PhD Duration: 01 October 2016 - Ongoing
Exchange Duration: 01 September 2017 - 30 June 2018
Thumb ticker md philippe wenk  phd student

Statistical Modeling of Dynamical Systems

Philippe Wenk (Ph.D. Student)

This dissertation project aims at providing a robust, scalable inference technique for parametric models of time series. In particular, it focuses on Gaussian process based collocation methods, investigating the weaknesses of existing ideas and developing new algorithms for parameter inference in systems of ODEs and...

Primary Host: Andreas Krause (ETH Zürich)
Exchange Host: Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
PhD Duration: 01 May 2018 - 01 May 2021
Exchange Duration: 01 May 2020 - 01 August 2020
Thumb ticker md anant raj

Stochastic Convex Optimization for Over-Parametrized models

Anant Raj (Ph.D. Student)

Over-parametrized models are frequently occurring phenomena in machine learning which comes with nice properties. In this work, we investigate methods to optimize such models. Our goal is to show faster convergence rate for traditional 1st order methods on such problems without extra assumptions and with similar com...

Primary Host: Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
Exchange Host: Francis Bach (INRIA & Ecole Normale Superieure)
PhD Duration: 01 August 2015 - Ongoing
Exchange Duration: 04 October 2019 - 31 March 2020
Thumb ticker md francesco locatello  phd student

Structured probabilistic inference via efficient constrained optimization

Francesco Locatello (Ph.D. Student)

In my research, I focus on enforcing desirable properties to the solution of learning algorithms, such as incorporating human beliefs, natural constraints, and causal structures. This translates to faster, more accurate, and more flexible models, which directly relates to real-world impact. I tackle this challenge...

Primary Host: Gunnar Rätsch (ETH Zürich)
Exchange Host: Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
PhD Duration: 01 October 2016 - 31 December 2020
Exchange Duration: 01 September 2017 - 31 July 2019
Thumb ticker md paul kishan rubenstein  phd student

Theory of latent feature learning

Paul Kishan Rubenstein (Ph.D. Student)

Low dimensional, abstract or otherwise 'simple' structure occurs widely across machine learning. This project advances theoretical understanding in a variety of areas. These are: causality, in which a theory of micro-macro abstractions is developed; independent component analysis, in which new identifiability result...

Primary Host: Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
Exchange Host: Carl Edward Rasmussen (University of Cambridge)
PhD Duration: 01 October 2015 - 30 June 2020
Exchange Duration: 01 October 2015 - 30 September 2016
Thumb ticker md alessandro davide ialongo  phd student

Uncertainty Quantification in Dynamical Systems

Alessandro Davide Ialongo (Ph.D. Student)

Many real-world systems are not static, they evolve through time. Modelling them as dynamical systems enables us to correctly account for non-stationarity and is a natural choice for sequential datasets. Especially in the low data regime, correctly quantifying predictive uncertainty is crucial to ensure we do not ta...

Primary Host: Carl Edward Rasmussen (University of Cambridge)
Exchange Host: Bernhard Schölkopf (Max Planck Institute for Intelligent Systems)
PhD Duration: 01 October 2016 - 31 May 2021
Exchange Duration: 01 November 2018 - 30 April 2020
Thumb ticker md mancini massimiliano

Zero shot adaptation and learning

Massimiliano Mancini (Ph.D. Student)

Domain Adaptation is a transfer learning scenario where the goal is to build a model addressing a task, e.g. classification, in a target domain with no or few images are labeled. Given a large amount of labeled data in a domain, i.e. the source, with a different input distribution from the target, e.g. synthetic to ...

Primary Host: Barbara Caputo (Politecnico di Torino & Italian Institute of Technology)
Exchange Host: Zeynep Akata (University of Tübingen & Max Planck Institute for Informatics)
PhD Duration: 01 November 2016 - 31 October 2020
Exchange Duration: 01 March 2020 - 30 June 2020