Ernest Mwebaze


School of Computing & IT,
Makerere University
P.O. Box 7062,
Kampala, Uganda.

Office: Block B, Level 3, Rm 303

P: (+256) 772-121-272

E: emwebaze[at]

H: Mon - Fri: 8:00 AM to 5:00 PM

Article in the UK Niews on my research on crop disease surveillance.

Using smartphones for automated diagnosis in the field. See project site here...

Visit our research group where we try to solve local problems using AI

Research Statement

I am passionate about the application of computational techniques to solving real-world problems in developing countries.

Presently, my specific research interests are in developing the nexus betweeen computation and agriculture. Particularly I am interested in improving crop disease diagnostics through automation. The lack of quick and timely surveillance and diagnosis of crop diseases is a major factor in leading to high yield loss, particularly for the smallholder farmer.

The smallholder farmer particularly in developing countries is a very key person because he/she depends mainly on yield from his/her garden to feed his/her children, to send them to school, and afford healthcare.

To this end I am implementing a grant from the Bill & Melinda Gates foundation, where we are developing improved automated tools and techniques for surveillance and diagnosis of viral disease in cassava crops.

Go to project site

Previous/other Research

Prototype classification

Prototype classification schemes are supervised machine learning techniques that use a distance measure to quantify the similarity between new data and a trained prototype. I particularly looked at extending LVQ by the use of divergences as a distance measure.

Causal discovery

Here, I was looking at discovering causal structure from observational data. I came up with some new algorithms and novel ways of applying the algorithms to natural datasets for example to understand disease associations and how socio-economic factors affect food insecurity.

Mathematical modeling

I have also flirted with the field of mathematical modeling of infectious diseases. Here, I was using stochastic transmission models to model Trachoma, Ebola and Leprosy. Work here was mainly done with the modeling group at UCSF.

Diagnostics toolbox for disease detection in cassava plants.


I have received one grant from the Gates foundation and several research fellowships.

  • PEARL grant - Bill & Melinda Gates

    The PEARL program is a BMGF Program for Emerging Agricultural Leaders (PEARL). I got a grant entitled Automated mobile survey technology and spatial modeling of viral cassava diseases in Uganda. Go to project site....

  • Research scholar/tutorial assistant at MMED

    The Mathematical Modeling of Epidemiological Diseases (MMED) program is an annual clinic held in South Africa that tries to create linkages between scientists from the US and Africa around mathematical modeling of disease. I attended the first clinic as a student and the second as a tutorial/lab assitant.

  • Research scholar at UCSF

    I visited with a mathematical modeling group at the University of California, San Francisco in 2014 for 6 weeks. Part of the results of this was a paper on modeling of Ebola in West Africa.

  • Visiting scholar at MIT

    I visited with Massachussetts Institute of Technology (MIT) for over 3 months in 2015 under a program Empower The Teachers (ETT) under MIT International Science and Technology Initiatives (MISTI) program.

It is said a life of service is mans noblest calling. I look at my role as a teacher as a calling, and this defines the underlying philosophy I adhere to. My philosophy to teaching is captured by three core beliefs: (1) that students learn from what they see and experience more than from what they are told, (2) that problem solving and critical thinking are core for any learning experience to be worthwhile and (3) actively engaging the class through different learning approaches is key to an all inclusive learning experience. I have taught several courses over the years. Courses are hosted on the Makerere University LMS (MUELE).


  • CSC 2114 Artificial Intelligence

    This is an introductory course on AI. In this course I dwell on the key concepts of search, probabilistic reasoning, machine learning and some applications in computer vision.

  • BSE 4102 Ethics of Professional Engineers

    One of my favorite courses to teach to software engineers.

  • CSC 2214 Cryptology and Coding Theory

    Previous course I offered.

  • BSE 2203 Computer Networks and Data communication

    Previous course I offered.


  • MCS 8100 Artificial Intelligence

    Pre-requisite for this courses is an undergraduate course in AI or machine learning. Here students go to a greater depth in both theory and implementation of AI trending research.

  • MCN 7204: Mobile Application Programming

    Previous course I offered on programming mobile web apps, native apps and hybrid apps.

  • MCN 7101 Principles of Mobile Computing

    Previous course I offered on mobile broadband and ubiquitous computing.