Meet the Fellows
Fellows 2024
Fafamé Edwige Akpoly
Benin
Supervisor: Kira Rehfeld
Stephen Kiilu
Kenya
Supervisor: Carsten Eickhoff
Immaculate Wanjiru Kimani
Kenya
Supervisor: Dominik Papies
Pauline Ornela Megne Choudja
Cameroon
Supervisor: Thomas Küstner
Berthine Nyunga Mpinda
Democratic Republic of Congo
Supervisor: Hendrik Lensch
Fellows 2023
Amel Abdulraheem
Albert Agisha
Albert Agisha
Democratic Republic of the Congo
Supervisor: Ulrike von Luxburg
Machine learning and statistics for climate networks
In this project, we investigate novel methods to assess the uncertainty in climate network construction and estimation procedures. The goal will be to create uncertainty estimates, not by network subsampling, but by bootstrapping the underlying time series directly. Different approaches will be compared, both on a theoretical and implementation level and connected to background knowledge in climate science.
Ifeoma Veronica Nwabufo
Ifeoma Veronica Nwabufo
Nigeria
Supervisor: Philipp Berens
Unsupervised learning in medical data science
The project will look at unsupervised learning methods recently developed in the lab to embed medical images via contrastive learning. It will explore the resulting embedding, study which data transformations are important and whether it can be related to disease/demographic factors.
Mendrika Rakotomanga
Mendrika Rakotomanga
Madagascar
Supervisor: Bernhard Schölkopf
Bayesian inference of gravitational waves with machine learning
This project is concerned with the evaluation and development of machine learning methods for gravitational wave inference. One focus is the analysis of current methods with regard to possible biases and their impact on large-scale analyses. In this context, the project also aims to develop methods to mitigate possible inaccuracies.
Fenosoa Randrianjatovo
Fenosoa Randrianjatovo
Madagascar
Supervisor: Claire Vernade
About random strategies in (non-stationary) reinforcement learning
Reinforcement learning (RL) algorithms have so far been developed mainly for steady-state environments and have difficulty adapting when the system dynamics or the reward function changes. Posterior sampling and Thompson sampling were identified early on as efficient strategies in RL, in part due to their randomised strategies. While some algorithms such as UCRL have recently been adapted to non-stationary environments, no randomised strategy has been proposed yet. In this project, we aim to propose a randomised -and more practical- algorithm that builds on posterior sampling and is capable of achieving sublinear regret. We will start by studying a (possibly context-dependent) bandit problem and then extend our findings to more complex RL models.
Fellows 2022