About me

Hi, welcome. I'm currently a Principal Investigator and the Team Lead at the Team Lead at the Dig Connectivity Research Laboratory (DCRLab) in Kampala, Uganda. I am also recognized as a World’s Top 2% Scientist. I earned a Master’s degree in Information Technology Engineering (with a minor in Computer and Communication Networks) from Yazd University, Iran. He completed his Bachelor of Science in Information Technology at Ndejje University, Kampala, Uganda. In addition to his academic background, Wasswa has received specialized training from institutions such as the National Institutes of Health (NIH), the U.S. Department of Health and Human Services, and the Bloomberg School of Public Health in the U.S. His training includes courses in Data Quality, Monitoring and Evaluation Fundamentals, and Protecting Human Research Participants. Before my current role, I gained extensive professional and research experience in positions including System Administrator at Pacelabs, Community Data Officer at PACE-Uganda, Research Associate at TechnoServe and Mercy Corps, and Research Lead at the Socio-economic Data Centre (SEDC-Uganda), Living Goods, Data Manager at Population Services International, Uganda (PSI-Uganda), and a former Managing Director at Asmaah Charity Organisation. I have also undertaken specialized training at the U.S. National Institutes of Health (NIH) and the Bloomberg School of Public Health, focusing on Data Quality, Monitoring and Evaluation, and Protection of Human Research Participants. I am also the author and co-editor of several books on sustainability, technology, and inclusive development.

Recruiting

We currently have a book author position available. For more details, please check this post.

Research Goals

I work on making AI more Trustworthy, Lightweight, and Efficient. My research focuses on enhancing the generalization of modern deep learning models in out-of-distribution settings and dynamically changing environments, considering potential fairness and bias issues. I see these as fundamental challenges in building responsible AI and advancing toward AGI.

My PhD research focused on designing and developing a lightweight, efficient, generalised, and deployable deep learning framework for stress detection, identification, and classification. I believe real intelligence hinges on efficient knowledge modulation—how to effectively learn, store, retrieve, and compose knowledge—integrating high-level reasoning and verification processes.

This perspective drives my broad interests in meta-learning, continual learning, generative models, algorithmic fairness, Bayesian optimization, and reinforcement learning. I explore these areas from both theoretical and algorithmic standpoints, with a strong desire to apply them to high-impact domains such as scientific discovery, sustainable agriculture, and sustainable computing.

Career Highlights