ELLIS PhD & Postdoc Program
The ELLIS PhD & Postdoc Program supports excellent young researchers across Europe by connecting them to leading researchers and offering a variety of networking and training activities, including boot camps, summer schools and workshops. ELLIS PhDs and postdocs conduct cutting-edge curiosity-driven research in machine learning or a related research area with the goal of publishing in top-tier conferences in the field.
2020 Call for the PhD Program *closed*
The call for applications for the ELLIS PhD Program closed on December 1, 2020. After an initial pre-screen, 850 completed applications from nearly 70 different countries were released to ELLIS faculty for further review. These numbers clearly show that the program has generated a significant buzz among young scientists well beyond Europe. Applications will be reviewed in the coming weeks and interviews for the PhD program are set to be held in February 2021.
The ELLIS PhD & Postdoc Program
Tracks
There are two tracks within the ELLIS PhD & Postdoc Program: the academic track and the industry track. These tracks have separate requirements for admission and criteria for activity during the appointment, but otherwise offer the same benefits, network and resources to the applicant.
Academic track
PhD students and postdocs in the academic track strive for international collaboration as they partner with two European academic institutions in their research. These candidates are supervised by one ELLIS fellow/scholar and one ELLIS fellow/scholar/member from different countries, and they visit the exchange institution for min. 6 months (the partitioning of this time is flexible). Normally, the exchange is partially sponsored by the exchange institution. There are two ways to become an ELLIS PhD Student or Postdoc (see below):
- via our central application process (if you want to start a PhD with one of the ELLIS fellows/scholars);
- via a nomination process (if you’re already working with one of the ELLIS fellows/scholars as a PhD or postdoc).
Industry track
The industry track is open to PhD and postdoc candidates that will be part of a collaboration between an academic institution and an industry partner, and will spend time conducting research at the industry partner during their PhD or postdoc. The candidate will spend a minimum of 50% of their time at the academic institution, and at least 6 months (cumulative) with the industry partner.
For this track, both advisors may be located in the same country. One advisor will represent the academic institution and the other the industry partner. Both the academic and industry advisor must be ELLIS members and at least one of them a fellow or scholar. Currently, the industry track is only open to applicants through nomination by an ELLIS fellow or scholar (see point 2 below).
How to join ELLIS as a PhD or postdoc
1. Central application
Students may apply through the central ELLIS application portal to become a PhD student within the ELLIS network (deadline once a year in December, the current call for applications can be found here). During their application, applicants choose from a set of ELLIS research areas and indicate preferences for ELLIS fellows/scholars they would like to work with. Afterwards, the applicants will be matched to the most relevant ELLIS fellows/scholars for interviews. If an offer is made, an agreement is signed which specifies the main supervisor, the co-supervisor (from a different country), the funding as well as the exchange and graduation plan. Students will be funded through the advisor’s resources and get their degree from the host institution. Please read our FAQs for more information.
Currently, the central application process is only for prospective PhD students. Postdocs are only recruited non-centrally via the nomination process described in 2.
2. Nomination
ELLIS fellows/scholars may nominate PhD students or postdocs who are working with them to become ELLIS PhD students or postdocs in the academic or industry track. Note that postdocs of either track and PhD industry track candidates are currently only recruited via nomination. Please see this page for more details about the requirements and the nomination process.
Reference
You can download a pdf by clicking on the image below to find all the information about the ELLIS PhD and Postdoc Program in one go.
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 (EPFL & Harvard University) |
| PhD Duration: | 01 November 2019 - 31 October 2022 |
| Exchange Duration: | 18 February 2020 - 31 March 2020 |
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) |
| PhD Duration: | 01 June 2017 - 01 June 2022 |
| Exchange Duration: | 01 June 2018 - 01 June 2019 |
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 |
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 - 31 December 2020 |
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) |
| Exchange Host: | Richard E. Turner (University of Cambridge) |
| PostDoc Duration: | 01 July 2019 - 30 June 2021 |
| Exchange Duration: | 01 June 2020 - 30 June 2021 |
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 |
Comparison of heterogeneous or uncertain network structures
Diane Duroux (Ph.D. Student)
Seeking reproducibility of findings is an essential part of research. This becomes a tedious and cumbersome task when big data with dependant features or multiple potentially interdependent data sources become available. Earlier work has shown that this is already the case for GWAIS compared to easier GWAS. The main...
| Primary Host: | Kristel Van Steen (University of Liège & KU Leuven) |
| Exchange Host: | Karsten Borgwardt (ETH Zürich) |
| PhD Duration: | 15 March 2019 - 15 March 2022 |
| Exchange Duration: | 01 February 2021 - 30 April 2021 |
Explainable AI for Nature Conservation
Diego Marcos (PostDoc)
As Deep Learning gets better at visual tasks, including species identification, the learned reasoning behind its decisions gets increasingly obscure. This is in contrast with the procedures developed by taxonomists, the experts in charge of defining the hierarchy of natural species, for manual species recognition....
| Primary Host: | Devis Tuia (EPFL) |
| Exchange Host: | Zeynep Akata (University of Tübingen) |
| PostDoc Duration: | 01 February 2019 - 31 January 2023 |
| Exchange Duration: | 01 August 2020 - 31 October 2020 |
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 P. 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 |
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 |
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 |
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 (Johannes Kepler University Linz) |
| Exchange Host: | Max Welling (University of Amsterdam) |
| PostDoc Duration: | 01 July 2018 - 30 June 2024 |
| Exchange Duration: | 01 September 2020 - 31 August 2022 |
Independent causal mechanisms in machine learning
Julius von Kügelgen (Ph.D. Student)
Due to changes in environment, measurement device, experimental condition, or sample selection bias, the commonly-made assumption of independent and identically distributed (i.i.d.) random variables underlying many machine learning algorithms is often violated in practice. The perspective of causal modelling offers ...
| 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 |
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 (University of Helsinki) |
| PhD Duration: | 01 October 2017 - Ongoing |
| Exchange Duration: | 01 October 2019 - 31 December 2019 |
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 & University of Manchester) |
| 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 |
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 |
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 |
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 |
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 (Czech Technical University, École Normale Supérieure & INRIA) |
| Exchange Host: | Ivan Laptev (INRIA) |
| PostDoc Duration: | 01 April 2019 - 31 March 2020 |
| Exchange Duration: | 01 October 2019 - 31 January 2020 |
Machine Learning and Causal Inference to Optimize Genomic Interventions using Disease State Representations
Bowen Fan (Ph.D. Student)
The human genome contains a torrent of information that gives clues not only about human origin, evolution, biological function, but also diseases. The goal of my project aims at developing novel machine learning techniques to better understand the complex genomic data and also other forms of data that can represent...
| Primary Host: | Karsten Borgwardt (ETH Zürich) |
| Exchange Host: | Kristel Van Steen (University of Liège & KU Leuven) |
| PhD Duration: | 01 March 2020 - 31 December 2022 |
| Exchange Duration: | 01 November 2020 - 31 January 2021 |
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 (Saarland University & Max Planck Institute for Intelligent Systems) |
| PhD Duration: | 01 November 2018 - 31 October 2022 |
| Exchange Duration: | 01 January 2020 - 30 June 2020 |
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 Munich & Siemens) |
| PhD Duration: | 01 September 2019 - 31 August 2022 |
| Exchange Duration: | 01 February 2021 - 30 April 2021 |
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 |
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 |
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 (EPFL) |
| PhD Duration: | 01 October 2019 - 30 September 2023 |
| Exchange Duration: | 01 September 2020 - 31 December 2020 |
Neural Architecture Search
Binxin Ru (Ph.D. Student)
The success of machine learning algorithms relies heavily on the appropriate choices of model architectures and hyperaparameters. Designing a model often requires strong expertise and selecting these hyperparameters are traditionally done via laborious trial and error. This has created strong demand for ways to auto...
| Primary Host: | Michael A. Osborne (University of Oxford) |
| Exchange Host: | Frank Hutter (University of Freiburg) |
| PhD Duration: | 01 October 2017 - 31 December 2021 |
| Exchange Duration: | 01 July 2021 - 31 October 2021 |
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 (University College London) |
| Exchange Host: | Csaba Szepesvari (DeepMind & University of Alberta) |
| PhD Duration: | 27 November 2017 - 26 November 2020 |
| Exchange Duration: | 01 April 2020 - 30 June 2020 |
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 |
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 & University of Manchester) |
| 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 |
Relaxed gradient estimators for structured probabilistic models
Max Paulus (Ph.D. Student)
Gradient computation is the methodological backbone of deep learning, but computing gradients can be challenging, in particular for some structured probabilistic models. Such models are of interest for a number of reasons, including improving interpretability, incorporating problem-specific constraints or improving ...
| Primary Host: | Andreas Krause (ETH Zürich) |
| Exchange Host: | Chris J. Maddison (University of Toronto & DeepMind) |
| PhD Duration: | 01 April 2018 - Ongoing |
| Exchange Duration: | 01 June 2019 - 31 December 2019 |
Representation Learning with Deep Generative Models
Andrea Dittadi (Ph.D. Student)
Learning useful representations from data with little or no supervision is a key challenge in artificial intelligence. Firstly, while labeled data is typically expensive, vast amounts of unlabeled data are available. Secondly, although the usefulness of a representation depends on the downstream task, it should be p...
| Primary Host: | Ole Winther (University of Copenhagen & Technical University of Denmark) |
| Exchange Host: | Bernhard Schölkopf (Max Planck Institute for Intelligent Systems) |
| PhD Duration: | 15 March 2018 - Ongoing |
| Exchange Duration: | 23 February 2020 - 31 August 2020 |
Robust and Reproducible Neural Architecture Search
Arber Zela (Ph.D. Student)
Neural Architecture Search (NAS) is the next logical step towards the automation of deep learning systems, due to its potential to achieve state-of-the-art performance on various tasks and remove the need of manually designing neural network architectures. The first black-box NAS methods required a vast amount of co...
| Primary Host: | Frank Hutter (University of Freiburg) |
| Exchange Host: | Yee Whye Teh (University of Oxford & DeepMind) |
| PhD Duration: | 01 March 2019 - 01 December 2021 |
| Exchange Duration: | - Ongoing |
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 (RWTH Aachen University) |
| PhD Duration: | 26 September 2016 - 31 March 2021 |
| Exchange Duration: | 26 September 2018 - 26 September 2019 |
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 |
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 |
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 |
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 & École Normale Supérieure) |
| PhD Duration: | 01 August 2015 - Ongoing |
| Exchange Duration: | 04 October 2019 - 31 March 2020 |
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 |
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 |
Trustworthy Machine Learning
Nikola Konstantinov (Ph.D. Student)
Key to the recent success of machine learning algorithms is the availability of large data sets for training models. The scale and variability of the needed data, however, often enforces its collection from various, potentially unreliable sources. Previous work has shown that machine learning models are vulnerable t...
| Primary Host: | Christoph H. Lampert (IST Austria) |
| Exchange Host: | Nicolò Cesa-Bianchi (Università degli Studi di Milano) |
| PhD Duration: | 15 September 2017 - 15 September 2021 |
| Exchange Duration: | 15 February 2021 - 15 May 2021 |
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 |
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) |
| PhD Duration: | 01 November 2016 - 31 October 2020 |
| Exchange Duration: | 01 March 2020 - 30 June 2020 |
