Hello! I am currently an Independent Researcher at ML Collective. Before that,
I received my bachelor's degree in Computer Science from the University for Development Studies.
I spent some time after that as a research assistant, exploring generative models and their applications in healthcare under the guidance of
Prof. Mohammed Daabo.
I also interned at the SMART Lab of North Dakota State University fortunate to be advised by
Prof. Armstrong Aboah.
My research interest lies at the intersection of Multimodal AI and Applications in Healthcare and Medecine.
I am also generally interested in Socially-Aware and Trustworthy Methodologies.
At ML Collective, I lead the Self-Supervised Learning for Small Datasets in Healthcare focus group. Our ultimate goal is to advance the next
generation of medical Ai systems, utilizing the small amount of medical data available in wild. In parallel, I lead the Multimodal AI Group,
with current focus on bridging the gap between multimodal AI applications in the African context (Check out our
AfriMMD POC Dataset at hugging face).
Outside my academic pursuits, I work as a full-stack engineer at
RGT.
In my free time, I enjoy growing new crops in my backyard.
| Dec. 2024 | ✨ I'm excited to share that I'll be virtually attending the NeurIPS 2024 Conference! Thanks to Gloabal South in AI. Let's connect on Whova. |
| Oct. 2024 | I'll be serving as a reviewer for the Machine Learning for Health (ML4H) Symposium 2024 |
| Sep 2024 | I'll be serving as a reviewer for WiML workshop co-located with NeurIPS 2024 |
| Aug 2024 | Accepted into the Black in AI's Emerging Leaders in AI Scholar's program |
| Jul 2024 | I'll be presenting our work on Multimodal Multilingual African Dataset at ML Collective's 22nd Research Jam. |
| Jul 2024 | Joined SMART Lab @ NDSU as a Research Intern. |
| Jun 2024 | Joined @Black in AI Community. |
| Apr 2024 | Reviewing applications for Deep Learning Indaba 2024. |
| Nov 2023 | Started RA @ C.K.T University of Technology and Applied Sciences. |
The scope of Ghanaian Language; Twi datasets is insufficiently diverse, hindering a multitude of applications. To address this, we are curating a comprehensive corpus from literature, media, and synthetic data, covering formal, informal, and conversational registers. Using advanced data collection methods and rigorous validation, including transformer-based models and expert human verification, we ensure a high-quality data repository for robust NLP applications.
This research project focuses on enhancing the reliability of Residue Number System (RNS) arithmetic operations through the development of effective fault tolerance techniques. We also proposed a novel integrated fault-tolerant technique that combines redundant residues, modular checksums, and Reed-Solomon codes for error detection & correction. This integrated approach aims to improve fault coverage and reliability in RNS arithmetic operations compared to existing solutions, with manageable overhead.
B.Sc. Computer Science
