ELLIS PhD & Postdoc Scholars at EPFL

The ELLIS PhD program is a key pillar of the ELLIS initiative whose goal is to foster and educate the best talent in machine learning and related research areas by pairing outstanding students with leading academic and industrial researchers in Europe. The program also offers a variety of networking and training activities, including summer schools and workshops. Each PhD student is co-supervised by one ELLIS fellow/scholar or unit faculty and one ELLIS fellow/scholar, unit faculty or member based in different European countries. Students conduct an exchange of at least 6 months with the international advisor during their degree. One of the advisors may also come from industry, in which case the student will collaborate closely with the industry partner, and spend min. 6 months conducting research at the industrial lab.

A new interdisciplinary track has also recently been introduced, in which students are co-supervised by an ELLIS fellow/scholar and a tenured faculty (if they are not an ELLIS fellow/scholar themselves), whose main expertise is different than machine learning/AI (for instance, biology, law or social sciences and humanities). For more information, the specific requirements for each track can be found here.

2024 Call for applications *not open yet*

*Apply by November 15, 2024, to join the ELLIS PhD Program in 2025*

Interested candidates should apply online through the ELLIS application portal by November 15th, 2024, 23:59 CET. Applicants first need to register on the portal. After registering, applicants will receive their login details for the portal and can submit their application via apply.ellis.eu.

Please read the ELLIS FAQs and webpage before applying, as well as the details below. Only complete applications will be considered.

Important dates:

  • October 2024: Application portal opens
  • November 15, 2024, 23:59 CET: application deadline (firm)
  • November/December 2024: review stage
  • January/February 2025: interview stage
  • Late February/March 2025: decisions
  • Program start: there is no common start for the PhD (depends on the advisor/institution)

ELLIS values diversity and seeks to increase the number of women in areas where they are underrepresented. We therefore explicitly encourage women to apply. We are also committed to recruiting more people living with disabilities and strongly encourage them to apply.

Admission to the program is competitive. In a typical round, less than 5% of all registered applicants, and between 5-10% of eligible applicants are accepted. Based on previous rounds, we expect that about 150 advisors in the ELLIS network will be participating in the upcoming round.

For additional information: please visit the 2024 ELLIS call for applications webpage. 

 

EPFL ELLIS Candidates 

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 sensorimotor paradigms.

My PhD project is a collaborative project between the Mathis lab who studies motor adaptation and the Bethge lab who studies robust ML methods under domain shifts. We will build computational models of neural activity and behavior to closer study neural mechanisms of continual learning during motor adaptation using tools from machine learning. We will compare representations arising in adaptive ML models with those present in the brain.

Motor adaptation tasks offer the unique opportunity to precisely control distribution shift, difficulty and learning objectives and link the biological findings to machine learning using insights from dynamical systems and network theory.

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; January 2021- January 2022

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. These procedures lead users to follow an identification key, a structured set of attribute observations, to reach a final conclusion.

I will be working towards incorporating this structured reasoning into Deep Learning models for species recognition such that their results become more interpretable, hopefully helping experts to spot mistakes or even yet-to-be-described species, and offering amateur users an expert explanation that can help them become experts themselves.

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

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-tagged social media data can enhance the measurement of the quantity and quality of urban green spaces. Remote sensing imagery can allow us to identify and classify green spaces. Social media data can represent human activity near the identified green spaces.

We hope to gain insight into qualitative characteristics of urban green spaces that could be useful for city planners and has a positive impact on citizens’ mental health.

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

Generative models in Geometric Deep Learning
Clément Vignac (Ph.D. Student)

Clément’s work focuses on the design of neural architectures for structured data: sets, graphs and point clouds. These problems have in common a large symmetry group, which is the invariance to all possible permutations of the points. In order to design architectures that are both computationally and data efficient…

Primary Host: Pascal Frossard (EPFL)
Exchange Host: Max Welling (University of Amsterdam)

PhD Duration: 01 November 2019 – 03 May 2023
Exchange Duration: 01 September 2021 – 31 December 2021

Foundations of Regularization in Deep Learning 
Linara Adilova (Ph.D. Student)

 
Regularization lies at the core of successful training state-of-the-art deep neural networks. It allows to control overfitting and allows to obtain good generalization even with massively overparametrized models. Regularization influences the training process both implicitly – through the properties of optimizers – and explicitly – by using a regularized loss function, dropout, batch normalization, etc. The goal of this Project is to shed more light on the foundations of regularization techniques employed in deep learning and to formally ground empirical results using the insights from the regularization theory.
 
Primary Host: Asja Fischer (Ruhr University Bochum)
Exchange Host: Martin Jaggi (EPFL)

PhD Duration: 01 February 2021 – 31 March 2024
Exchange Duration: 01 June 2022 – 31 December 2022

A geometric look to understand generalization and robustness of deep learning
Guillermo Ortiz Jiménez (Ph.D. Student)

Years of a fierce competition towards the best results have naturally selected the fittest deep learning algorithms. However, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trust, and fairness of modern AI systems. Understanding deep learning is, thus, fundamental for its long-term success; and Guillermo’s work is shedding new light on the fundamental mechanisms behind it. By studying the complex interaction between data, architecture, and optimization algorithm, Guillermo is exposing key insights about the way neural networks see the world; and is working towards applying these results to the design of more reliable neural networks. The University of Oxford hosts some of the leading researchers in AI robustness worldwide, so we hope that Guillermo’s exchange will result in a fruitful collaboration.
 
 
Primary Host: Pascal Frossard(EPFL)
Exchange Host: Philip H. S. Torr (University of Oxford)
 
PhD Duration: 01 November 2018 – 01 May 2023
Exchange Duration: 01 January 2022 – 30 June 2022

A game theoretic framework for reinforcement learning and imitation learning
Luca Viano (Ph.D. Student)

Reinforcement Learning and Imitation Learning achieved impressive empirical results in recent years. Still, little is known about the theoretical properties of commonly used algorithms. This PhD project aims to use insights from the field of game theory and optimization to develop algorithms with mathematical guaran…
 
Primary Host: Volkan Cevher  (EPFL)
Exchange Host: Gergely Neu  (Universitat Pompeu Fabra)
 
PhD Duration: 01 September 2021 – 01 September 2025
Exchange Duration: 01 March 2023 – 01 September 2023


Feedback models for real-time robotic cloth manipulation
Oriol Barbany (Ph.D. Student)

Object rigidity is still one of the most common assumptions in robotic grasping and manipulation. Nevertheless, many daily life objects like cables, plastics, and clothes present non-negligible deformations. Manipulating garments remains a challenging topic due to the high flexibility of textiles and their nearly in…
 
Primary Host: Carme Torras   (Universitat Politècnica de Catalunya)
Exchange Host: Amir Zamir   (EPFL)

PhD Duration: 01 July 2021 – 31 May 2024
Exchange Duration: 01 January 2023 – 31 May 2023

List of all Ellis PhD Students and PostDocs