Cluster of Excellence – Machine Learning for Science

Meet the Fellows

Fellows 2025

The five AIMS Fellows in Tübingen. From left to Right: Claire Olivic Dongmo, Ignatius Boadi, Alvine Fandio, Détagnon Bernic Gbaguidi and Duaa Alshareif.
Alvine Fandio
Cameroon

Supervisor: Martin Butz

Research project: 
tbd

Claire Olivic Dongmo
Cameroon

Supervisor: Nicole Ludwig

Research project: 
Impact of extreme weather events on solar energy production

Détagnon Bernic Gbaguidi
Benin

Supervisor: Nico Pfeifer

Research project: 
TabPFN to fight infectious diseases

Ignatius Boadi
Ghana

Supervisor: Kerstin Ritter

Research project: 
Longitudinal brain imaging in at risk relatives of SPG4 Patients

Duaa Alshareif
Sudan

Supervisor: Michael Franke

Research project: 
AnaphoraGym: a Benchmark for Anaphora Resolution in Language Models

Fellows 2024

The 5 AIMS Fellows in Tübingen. From left to right: Fifamé Edwige Akpoly, Pauline Ornela Megne Choudja, Stephen Kiilu, Berthine Nyunga Mpinda and Immaculate Wanjiru Kimani.
Fifamé Edwige Akpoly
Fifamé Edwige Akpoly
Benin

Supervisor: Kira Rehfeld

Research project: 
Rainfall Forecasting in the Sahel Region Using Machine Learning Techniques

Stephen Kiilu
Stephen Kiilu
Kenya

Supervisor: Carsten Eickhoff

Research project: 
Integration of Knowledge in LLMs

Immaculate Wanjiru Kimani
Immaculate Wanjiru Kimani
Kenya

Supervisor: Dominik Papies

Research project: 
Exploring Financial Inclusion through Social Media Influencers in Emerging Economies

Pauline Ornela Megne Choudja
Pauline Ornela Megne Choudja
Cameroon

Supervisor: Thomas Küstner

Research project: 
Longitudinal Tracking and Segmentation of Lesions in PET/CT Hybrid Imaging

Berthine Nyunga Mpinda
Berthine Nyunga Mpinda
Democratic Republic of Congo

Supervisor: Hendrik Lensch

Research project: 
Explainable Medical Image Classification

Fellows 2023

The 5 AIMS Fellows 2023 stand together as a group
The 5 AIMS Fellows in Tübingen. From left to right: Mendrika Rakotomanga, Albert Agisha, Ifeoma Nwabufo, Amel Abdelraheem, and Fenosoa Randrianjatovo

Amel Abdulraheem

Amel Abdulraheem
Sudan

Supervisor: Christian Baumgartner

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

Group photo of the 5 AIMS-Fellows 2022

Bolaji Bamiro


| We are mourning the loss of Bolaji who tragically died in a car accident on September 19,  2023. Our thoughts are with Bolaji's family and friends. We fondly remember our time with her. |
 

Portrait of AIMS Fellow Bolaji Bamiro and a description of why she participates in the program
Portrait of AIMS Fellow Faisal Mohamed and a description of why he participates in the program

Faisal Mohamed

Portrait of AIMS Fellow Tatenda Emma Matika and a description of why she participates in the program

Tatenda Emma Matika

Portrait of AIMS Fellow Tshenolo Thato Daumas and a description of why she participates in the program

Tshenolo Thato Daumas

Portrait of AIMS Fellow Wafaa Mohammed and a description of why she participates in the program

Wafaa Mohammed