Yusuf Brima, Ph.D.

Yusuf Brima, Ph.D.

Machine Learning Research

Posts by Collection

mentorship

Guides to Securing Scholarships Overseas

Published:

Objectives

The democratization of the Internet has opened vistas of opportunities for young people coming from underprivileged backgrounds to gain scholarships for further studies. This massive outpour of information has also saturated facts with fiction about what it takes to gain a prestigious scholarship program amid the ever-growing competitiveness among candidates for a few slot of opportunities. Thus, our aim in this webinar, the first of its kind (at least to our knowledge for the West African context), is to distill the essence of a scholarship, the does and don't in one's quest to get a scholarship and a general thinking paradigm to have to become a successful scholar. The paradigm in question is problem-solving and leadership-oriented ways of thinking because to be successful, one has to be so in the selfless service to humankind.

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Sampe documents and general instructions for scholarship application

Published:

As mentioned in my previous post, scholarships are offered to candidiate base on specific eligibility criteria which could range from merit (whatever its definition might be) to diversity and inclusion of underrepresented groups. However, the common denominator is, these candidates need financial assistance for professional development.

One key advice which you should bear in mind in your hunt for scholarship opportunities is to avoid plagiarism. I know, we all at one point in time have to stand on the shoulders of giants in order to grow but give credit to whom it is due. Intellectual honesty is very important. You may wonder, why am I taking a tangent and being stoic on this subject?! Simple. Primarily because it is very crucial not only in getting a scholarship but to toward your profession success (or failure) altogether. Pay attention to this truism. You are going to get suggestively tempted to not be original but doing so could be at your detriment. So avoid using other people's motivation letters, study plan or similar documents as your own. Be yourself. Tell your own unique story. That will help you stand out.

Having got that out of the way, here are a few sample documents and templates to help inspire you develop your own application portfolio. This is not exhaustive, neither it is the best there exist. However, it could be a great first step in developing an intuition of how these documents are developed.

Motivation Letter Template
Study Plan Sample
Research Proposal Sample (disclaimer: it's Computer Science-based but the ideas and structure should translate easily to other STEM-based disciplines maybe even beyond)

This page is a work-in-progress and will continue to be expanded upon to aid more smooth application process for potential candidates. Read more

people

Origene Tuyishimire

Published:

Origene Tuyishimire is an alumnus of the African Institute of Mathematical Sciences (AIMS), Rwanda. Read more

portfolio

publications

Brain MRI Dataset

Published in Figshare/Dataset, 2021

Published a dataset that was curated in collaboration between the Computer Science and Engineering Department, University of Dhaka and the National Institute of Neuroscience, Bangladesh. It comprise 5,285 T1-weighted contrast- enhanced brain MRI images belonging to 38 categories. Read more

Recommended citation: Yusuf Brima, Mossadek Hossain Kamal, Upama Kabir, and Tariqul Islam (2021). "Brain MRI Dataset." Figshare/Dataset. 1(1). https://doi.org/10.6084/m9.figshare.14778750.v2

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

Published in Diagnostics, 11(8), 1480 (2021), 2021

This study explores the use of transfer learning for detecting and diagnosing different types of pneumonia, including COVID-19-induced pneumonia, from chest X-ray images. The work evaluates the performance of various deep learning models in assisting medical professionals with automated diagnostics. Read more

Recommended citation: 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 https://doi.org/10.3390/diagnostics11081480

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

Published in Dhaka University Journal of Applied Science and Engineering, 6(2), 14–29 (2022), 2022

This paper presents a deep transfer learning approach for multi-class classification of brain MRI images, evaluating its effectiveness in differentiating various neurological conditions. Read more

Recommended citation: 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 https://doi.org/10.3329/dujase.v6i2.59215

A Mathematical Framework for Understanding Recognition Systems

Published in arXiv preprint biorxiv:2023.06.08.544240, 2023

We propose a theoretical framework for biological recognition, integrating category theory, information theory, dynamical systems modeling, and optimization theory. This framework defines individual recognition (IR) and class-level recognition (CR) as functors between stimuli and responses. We identify five conditions for robust IR systems, termed “signature systems,” and model them as attractor states, offering insights into communication and language evolution. Read more

Recommended citation: Brima, Yusuf. "A Mathematical Framework for Understanding Recognition Systems" biorxiv preprint biorxiv:2023.06.08.544240 (2023). https://doi.org/10.1101/2023.06.08.544240

Self-Supervised Learning of Speech Representation via Redundancy Reduction

Published in Gesellschaft für Informatik e.V.. pp. 11-19. Doctoral Consortium at KI 2023. Berlin. 45195, 2023

This work aims to investigate a novel self-supervised learning (SSL) method for speech representation that leverages redundancy reduction techniques to learn robust representations capturing speaker characteristics. Our proposed approach builds upon the Barlow Twins framework, introduced in computer vision, and we adapt it to speech processing. The primary objective is to assess the quality of the learned representations through comprehensive evaluations of various downstream tasks, including speaker identification, gender recognition, emotion recognition, and more. Read more

Recommended citation: 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 https://doi.org/10.18420/ki2023-dc-02

Learning Disentangled Speech Representations

Published in New in Machine Learning Workshop, NeurIPS 2023, 2023

This study investigates methods for learning disentangled representations of speech signals, focusing on separating different speech-related factors such as speaker identity, linguistic content, and emotional state. The work applies novel deep learning approaches to improve representation learning in speech processing. Read more

Recommended citation: Brima, Y., Krumnack, U., Pika, S., & Heidemann, G. (2023). Learning Disentangled Speech Representations. New in Machine Learning Workshop, NeurIPS 2023. https://openreview.net/forum?id=3ox1TfKeRF

Learning Disentangled Audio Representations through Controlled Synthesis

Published in ICLR Tiny Papers Track 2024, 2023

This paper introduces a novel approach to learning disentangled speech representations through controlled synthesis, enabling better interpretability and generalization in speech processing tasks. Read more

Recommended citation: Brima, Y., et al. (2023). Learning Disentangled Audio Representations through Controlled Synthesis. ICLR Tiny Papers Track 2024. https://openreview.net/forum?id=Fn9ORH8PLl

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

Published in Information, 15(4), 182 (2024), 2024

This paper investigates the interpretability of deep learning models in detecting and diagnosing brain tumors from MRI images. It evaluates various explainability techniques and their effectiveness in clinical applications. Read more

Recommended citation: 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 https://doi.org/10.3390/info15040182

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

Published in BioData Mining, 17, 18 (2024), 2024

This work explores explainable deep learning techniques in medical imaging, bridging visual explainability with statistical quantitative analysis. It proposes a novel approach that enhances the interpretability of AI models applied to medical diagnostics. Read more

Recommended citation: 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 https://doi.org/10.1186/s13040-024-00370-4

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

Published in MDPI Information, 15(2), 114 (2024), 2024

This paper explores self-supervised learning for speech representation, leveraging redundancy reduction techniques to learn robust representations. It adapts the Barlow Twins framework to speech processing and evaluates its effectiveness in various downstream tasks. Read more

Recommended citation: 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 https://doi.org/10.3390/info15020114

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

Published in Neural Computing and Applications, 2025

Medical imaging datasets pose challenges in storage, transmission, and analysis due to their complexity. Low-rank matrix approximation (LORMA) and its variant, LLORMA, offer efficient data representation, with LLORMA gaining preference since 2015 for capturing structural details. This review examines their applications, limitations in similarity measurement, and the potential of semantic segmentation for improvement. We also explore their extension to structured data, the impact of missing values, and propose a hybrid optimization approach to enhance patch selection and overall applicability in medical imaging. Read more

Recommended citation: 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 https://doi.org/10.1007/s00521-025-11055-2

An unsupervised XAI framework for dementia detection with context enrichment

Published in Scientific reports, 15, 1 (2025), 2025

This work introduces a framework that evaluates XAI methods by integrating neuroanatomical morphological features with CNN-generated relevance maps for disease classification. We trained a CNN using brain MRI scans from six cohorts: ADNI, AIBL, DELCODE, DESCRIBE, EDSD, and NIFD (N = 3253), including participants that were cognitively normal, with amnestic mild cognitive impairment, dementia due to Alzheimer’s disease and frontotemporal dementia Read more

Recommended citation: 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 https://doi.org/10.1038/s41598-025-26227-2

talks

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

Published:

Poster title: “Supervised Contrastive Deep Learning for Individual Recognition in Chimpanzees in the Wild “ This talked peered into the study of the origin of human language as a communicative tool. It narrowed down on Individual Recogntition (IR) – the ability of an individual (a receiver) to uniquely identify another individual (a signaler) based on signature cues, a ubiquitous practice of animals that live in fission-fusion systems. IR is crucial to startification of groups, formation of trust and behaviour direction towards others. This talk connect the problem of IR from the perpective of a receiver as an computational problem modelled using deep learning. Read more

teaching

Principles of Programming Languages

Undergraduate courses, University of Makeni, Department of Computer Science, 2017

Course objectives: 

  • To understand and describe syntax and semantics of programming languages
  • To understand data, data types, and basic statements 
  • To understand call-return architecture and ways of implementing them
  • To understand object-orientation, concurrency, and event handling in programming languages
  • To develop programs in non-procedural programming paradigms
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Data Mining and Warehousing

Undergraduate courses, University of Makeni, Department of Computer Science, 2017

Unit I

Data mining – Introduction – Information and production factor – Data mining vs query tools– Data mining in marketing – Self learning computer systems – concept learning – Data mining and the data warehouse. Read more

Server-side programming with PHP

Undergraduate courses, University of Makeni, Department of Computer Science, 2017

Unit-1

PHP Basics: introduction, server installation, syntax, echo/print statement,datatypes,strings,constants,Datatypes,Strings,operators,control statements, loops, php functions, php functions with arguments, php arrays Read more

Relational Database Management Systems

Undergraduate courses, University of Makeni, Department of Computer Science, 2017

Unit-1: Introduction To Database Systems

Database Approach,Actors,DBMS Approach, File System, Database Applications Read more

PRINCIPLES OF PROGRAMMING LOGIC AND DESIGN

Undergraduate courses, Limkokwing University of Creative Technology, Faculty of Information and Communication Technology, 2018

Course objectives: 

  • To expose the basic concepts of system - design, implementation, and management
  • To provide an overview on computerized application program, terminology, conventions, and procedures
  • To developing structured program logic
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Deep Representation Learning Seminar

Graduate/Undergraduate Seminar, University of Osnabrück, Institute of Cognitive Science, 2023

Course Overview

This seminar focuses on Deep Representation Learning, exploring how deep neural networks acquire hierarchical and versatile representations during training. The course combines theoretical insights with practical applications, guiding students through the process of analyzing, evaluating, and improving representations in deep learning models. Participants will reproduce and adapt research papers, write scientific reports, and present their findings. Read more