activity
20192026
most citedMachine Learning for a Low-cost Air Pollution Network

2 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

eess.AS2026

WAXAL: A Large-Scale Multilingual African Language Speech Corpus

Abdoulaye Diack, Perry Nelson, Kwaku Agbesi +40

The advancement of speech technology has predominantly favored high-resource languages, creating a significant digital divide for speakers of most Sub-Saharan African languages. To…

cs.CL2025

Sunflower: A New Approach To Expanding Coverage of African Languages in Large Language Models

Benjamin Akera, Evelyn Nafula Ouma, Gilbert Yiga +11

There are more than 2000 living languages in Africa, most of which have been bypassed by advances in language technology. Current leading LLMs exhibit strong performance on a numbe…

cs.SE2025

Design and Evaluation of a Scalable Data Pipeline for AI-Driven Air Quality Monitoring in Low-Resource Settings

Richard Sserujongi, Daniel Ogenrwot, Nicholas Niwamanya +7

The increasing adoption of low-cost environmental sensors and AI-enabled applications has accelerated the demand for scalable and resilient data infrastructures, particularly in da…

cs.LG2023

Gaussian Processes for Monitoring Air-Quality in Kampala

Clara Stoddart, Lauren Shrack, Richard Sserunjogi +6

Monitoring air pollution is of vital importance to the overall health of the population. Unfortunately, devices that can measure air quality can be expensive, and many cities in lo…

cs.LG2022

Modelling calibration uncertainty in networks of environmental sensors

Michael Thomas Smith, Magnus Ross, Joel Ssematimba +4

Networks of low-cost sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complic…

stat.ML2022★ 2 cited

Adjoint-aided inference of Gaussian process driven differential equations

Paterne Gahungu, Christopher W Lanyon, Mauricio A Alvarez +3

Linear systems occur throughout engineering and the sciences, most notably as differential equations. In many cases the forcing function for the system is unknown, and interest lie…

cs.LG2019★ 2 cited

Machine Learning for a Low-cost Air Pollution Network

Michael T. Smith, Joel Ssematimba, Mauricio A. Alvarez +1

Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used. This leads to potentiall…