ELLIS PhD & Postdoc Scholars at EPFL
The ELLIS PhD & PostDoc program supports excellent PhDs & PostDocs in machine learning related areas across Europe by giving them access to leading European researchers as well as boot camps, summer schools and workshops of the ELLIS programs. ELLIS PhDs & PostDocs are supervised by one ELLIS fellow/scholar and one ELLIS member from different countries.
They conduct cutting-edge curiosity-driven research in machine learning or a related research area and visit the exchange institution for an extended research stay (1 year).
2020 Call for the PhD Program *closed*
A primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives from A. Mathis, S. Schneider, J. Lauer and M. W. Mathis has been published in the October 2020 issue of Neuron.
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.
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.
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…