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.
My experience was great personally and professionally. As I am very passionate about traveling and meeting people from different cultures, I learned a lot about the country and the people of Bangladesh. I still maintain ties with some friends from here. Read more
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.
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
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
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
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
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
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
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
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
Published in Accepted at Springer Neural Computing and Applications (NCAA), 2024
This work provides a systematic review of low-rank and local low-rank matrix approximation techniques in medical imaging, offering insights into future research directions and clinical applications. Read more
Recommended citation: Hamlomo, S., Atemkeng, M., Brima, Y., Nunhokee, C., Baxter, J. (2024). Advancing Low-Rank and Local Low-Rank Matrix Approximation in Medical Imaging: A Systematic Literature Review and Future Directions. arXiv:2402.14045. (Accepted at Springer Neural Computing and Applications (NCAA)) https://arxiv.org/abs/2402.14045
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
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
[MSc. Mathematical Sciences thesis] Transfer Learning for the Detection and Classification of traditional pneumonia and pneumonia induced by the COVID-19 from Chest X-ray Images. Available here Read more
Workshop Title: BRIDGING THE TECHNOLOGICAL GAP – SPREADING TECHNOLOGICAL INNOVATIONS IN THE STUDY OF THE HUMAN AND NON-HUMAN MIND Date: JULY 31ST, 2022 – AUGUST 6TH, 2022 URL: https://www.primate-cognition.eu/de/veranstaltungen/bridging-the-technological-gap-workshop.html — Read more
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
Talk title: “Supervised Contrastive Representation Learning for Individual Recognition in the Wild.” On-going reaching into the application of state-of-the-art deep learning techniques for speaker recognition in the wild. Read more
A research poster presentation on novel techniques for learning interpretable and robust speech representations through self-supervision. Available here Read more
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
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
