Curriculum Vitae

Education

Ph.D. in Cognitive Science, Universität Osnabrück, 2025
MSc. in Mathematical Sciences, African Institute of Mathematical Sciences, 2021
MSc. in Computer Science and Engineering, University of Dhaka, 2020
BSc. in Computer Science, University of Makeni, 2017

Work experience

May 2025-till Postdoctoral Researcher in Biomedical Data Science
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Germany
- Duties include:
  - Conduct extensive research in biomedical AI for early diagnosis of cancer and Alzheimer’s disease in a privacy-presented and trustworthy setting using uni-modal and multimodal deep learning techniques.
  - Supervise bachelor and masters thesis students.
  - Published papers in top healthcare-focused and general machine learning conferences and journals including NeurIPS, ICLR, MICCAI, Springer Nature BioData Mining, MDPI Medical Data Mining, etc.
  - Work with medical industry partners to translate research into practice.
November 2021-December 2024 Research Associate
- Research Training Group (RTG) in Computational Cognition, Universität Osnabrück
- Duties included: Conduct basic research at the interface of Cognitive Science and Deep Learning with particular emphasis on building computational models of Individual Recognition – a ubiquitous phenomenon in social species like humans.
February 2017-July 2018 : Research Teaching Assistant
- University of Makeni
- Duties included: Teaching undergraduate Computer Science courses in Principles of Programming Languages, Introducion to Computing, Web Programming, Relational Database Systems theory and practice
- Supervisor: Lakshamana Namamula Rao
2018: Research Assistant
- Limkokwing University of Creative Technology
- Duties included: Teaching undergraduate Computer Science courses in Principles of Programming Languages and Introducion to Computing
- Supervisor: Aiah James
February2017-July 2018 : College Registrar
- African Accents International Institute of Computer Technology
- Duties included: Oversight of Enrollment, coodination of outreach activities
- Supervisor: Mustapha Jalloh

Conferences, Workshops & Seminars

05/2018 – 07/2018 – UNESCO-ICHEI, China
- Seminar on ICT in Higher Education Innovation for African Countries. Attained knowledge in the role of informatization of higher education to improve learning outcomes. The seminar entailed hands-on training in Big Data management with Apache Spark and predictive analytics with Machine Learning techniques to improve learning outcomes.
31/07/2022 – 06/08/2022 – German Primate Center in Göttingen, Germany
- Bridging the technological gap – spreading technological innovations in the study of the human and non-human mind. A workshop that brought early-stage researchers to share ideas on cutting-edge technologies to facilitate animal behavior and cognition research. Link: here
6/08/2022 – 07/08/2022 – Insitute of Cognitive Science in Osnabrück University, Germany
- Bridging the Gap between Biological and Artificial Neural Networks” at the (Research Training Group (RTG) in Computational Cognition). Co-organized the workshop that brings together experts from diverse but intersecting fields pursuing research in understanding the mechanisms that give rise to intelligence in biological and artificial systems. Link here

Summer Schools

27/12/2024 - 31/12/2024 - The MUST Deep Learning Bootcamp, North-West University, South Africa
- This bootcamp covered the breadth and depth of deep learning from basic principles like backpropagation, to attention mechanisms and reinforcement learning. Link here
11/09/2022 – 16/09/2022 - Mediterranean Machine Learning school,Università Milano Bicocca, Milan, Italy
- The Mediterranean Machine Learning (M2L) summer school comprised 5 days of keynotes, lectures, and practical sessions covering the breadth and depth of deep learning from cutting-edge research to practice. Link here
11/07/2022 – 29/07/2022 - Neuromatch Academy: Deep Learning, Virtual
- The Deep Learning (DL) course covers team-based hands-on training in the theoretical underpinnings and practical approaches of current DL algorithms. The three weeks of intensive training involve the complete DL workflow from data curation to exploratory data analysis to model design, implementation, evaluation, and reporting. These algorithms range from Logitic Regressors to Transformers and everything in between.Link here
07/2021 – 07/2021 - Eastern European Machine Learning Summer School, Virtual
- Covered the breadth and depth of deep learning from basic principles like backpropagation, to attention mechanisms and reinforcement learning. Link here
07/2020 – 08/2020 - Oxford Machine Learning Summer School, UK
- Bayesian ML, Computer Vision, Natural Language Processing, Reinforcement Learning, Causal ML, Topological ML, and Transfer Learning. Link here
07/2020 – 08/2020 - Machine Learning Summer School-INDO, Indonesia
- Machine Learning Fundamentals, TensorFlow, Convolutional Neural Network, Advanced Topics in Equivariance and Inductive Biases, Bayesian Machine Learning, Information Theoretic Objectives, and Robust Generalization, Computational Morphology, Reinforcement Learning, Probabilistic Generative Models, VAEs and Flows, Deep Learning for NLP, Graph Neural Nets, Transfer Learning for NLP, Multilingual NLP, Super-resolution and its Application to Computer Vision Tasks. Link here

Additional Certifications

08/2023 - 09/2023 - IBM AI Engineering Professional Certificate, Virtual
- Introduction to Deep Learning & Neural Networks with Keras. Link here
02/2023 - 08/2023 - IBM Data Science Professional Certificate, Virtual
- Specialization Certificate. Link here
03/2023 - 04/2023 - University of Michigan, Virtual
- Python Data Structures. Link here
08/2020 – 09/2020 - MathWorks Training Service, MOOC
- Deep Learning for Image Recognition. Using Pre-trained Networks, Managing Collections of Data, and Performing Transfer Learning.
08/2019 – 06/2020 - Imperial College London, Virtual
- Mathematics for Machine Learning Specialization: Linear Algebra, Multivariate Calculus, and Principal Component Analysis. Link here
09/2016 – 10/2016 - On-Campus Internship Programme (OCIP), University of Makeni, Sierra Leone
- Installation and configuration of software, computer networking, troubleshooting, and maintenance.

Skills

Computing
- Knowledge of algorithm discovery and representation
- Agile and defensive programming
- Mathematical Modelling and Optimization
- Software Engineering processes
Leadership Skills

Publications

An unsupervised XAI framework for dementia detection with context enrichment

Singh, D., Brima, Y., Levin, F., Becker, M., Hiller, B., Hermann, A., Villar-Munoz, I., Beichert, L., Bernhardt, A., Buerger, K. and Butryn, M., 2025. An unsupervised XAI framework for dementia detection with context enrichment. Scientific reports, 15(1), p.39554

A systematic review of low-rank and local low-rank matrix approximation in big data medical imaging

Hamlomo, S., Atemkeng, M., Brima, Y. et al. A systematic review of low-rank and local low-rank matrix approximation in big data medical imaging. Neural Comput & Applic (2025). https://doi.org/10.1007/s00521-025-11055-2

Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction

Brima, Y., et al. (2024). Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction. MDPI Information, 15(2), 114. DOI: 10.3390/info15020114

Saliency-driven Explainable Deep Learning in Medical Imaging: Bridging Visual Explainability and Statistical Quantitative Analysis

Brima, Y., Atemkeng, M. (2024). Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis. BioData Mining, 17, 18. DOI: 10.1186/s13040-024-00370-4

Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images

Nhlapho, W., Atemkeng, M., Brima, Y., et al. (2024). Bridging the Gap: Exploring Interpretability in Deep Learning Models for Brain Tumor Detection and Diagnosis from MRI Images. Information, 15(4), 182. DOI: 10.3390/info15040182

Learning Disentangled Audio Representations through Controlled Synthesis

Brima, Y., et al. (2023). Learning Disentangled Audio Representations through Controlled Synthesis. ICLR Tiny Papers Track 2024.

Learning Disentangled Speech Representations

Brima, Y., Krumnack, U., Pika, S., & Heidemann, G. (2023). Learning Disentangled Speech Representations. New in Machine Learning Workshop, NeurIPS 2023.

Self-Supervised Learning of Speech Representation via Redundancy Reduction

Brima, Yusuf (2023): Self-Supervised Learning of Speech Representation via Redundancy Reduction. DC@KI2023: Proceedings of Doctoral Consortium at KI 2023. DOI: 10.18420/ki2023-dc-02. Gesellschaft für Informatik e.V.. pp. 11-19. Doctoral Consortium at KI 2023. Berlin. 45195

A Mathematical Framework for Understanding Recognition Systems

Brima, Yusuf. "A Mathematical Framework for Understanding Recognition Systems" biorxiv preprint biorxiv:2023.06.08.544240 (2023).

Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification

Brima, Y., Kamal Tushar, M. H., Kabir, U., Islam, T. (2022). Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification. Dhaka University Journal of Applied Science and Engineering, 6(2), 14–29. DOI: 10.3329/dujase.v6i2.59215

Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray Images

Brima, Y., Atemkeng, M., Tankio Djiokap, S., Ebiele, J., & Tchakounté, F. (2021). Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray Images. Diagnostics, 11(8), 1480. DOI: 10.3390/diagnostics11081480

Brain MRI Dataset

Yusuf Brima, Mossadek Hossain Kamal, Upama Kabir, and Tariqul Islam (2021). "Brain MRI Dataset." Figshare/Dataset. 1(1).

Talks

Multimodal Federated Learning for Robust Lung Cancer Prognosis

February 19, 2025

Talk at Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Bonn, Germany

Assessing Explainability in Deep Learning for Medical Image Analysis

December 16, 2024

Talk at Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Bonn, Germany

Trustworthy Healthcare AI for Mental Health Risk Prediction

December 10, 2024

Talk at Neurobiology Research Unit, Copenhagen University Hospital, Copenhagen, Denmark

Assessing Fusarium Oxysporum Disease Severity in Cotton Using Unmanned Aerial System Images and a Hybrid Domain Adaptation Deep Learning Time Series Model

July 10, 2024

Talk at Leibniz Institute for Agricultural Engineering and Bioeconomy, Potsdam, Germany

Causal Representation Learning

March 13, 2024

Talk at Computer Vision Colloquium, Osnabrueck University, Osnabrück, Germany

Disentangled Speech Representation Learning via Self-Supervision

December 10, 2023

Poster Presentation at NeurIPS 2023, New Orleans, USA

Self-Supervised Learning of Speech Representation via Redundancy Reduction

July 26, 2023

Oral Presentation at KI 2023 – 46th German Conference on Artificial Intelligence, Berlin, Germany

Supervised Contrastive Representation Learning for Individual Recognition in the Wild

November 16, 2022

Talk at Language and Communication colloquium, Institute of Cognitive Science, Osnabrueck University, Germany, Building 50, Room 50/E07, Wachsbleiche 27, Osnabrueck, Germany

Supervised Contrastive Deep Learning for Individual Recognition in Chimpanzees in the Wild

August 08, 2022

Talk at Department of Comparative Cultural Psychology, Max Planck Institute for Evolutionary Anthropology, Germany, Virtual

Supervised Contrastive Deep Learning for Individual Recognition in Chimpanzees in the Wild

July 31, 2022

Talk at Language and Communication colloquium, Institute of Cognitive Science, Osnabrueck University, Germany, Building 50, Room 50/E07, Wachsbleiche 27, Osnabrueck, Germany

Transfer Learning for the Detection and Classification of traditional pneumonia and pneumonia induced by the COVID-19 from Chest X-ray Images

June 27, 2021

Talk at African Institute of Mathematical Sciences (AIMS), Rwanda, AIMS Centre Remera, Kigali, Rwanda

Yusuf Brima, Ph.D.