Pearl Rwauya
Scientific Data Analyst
Affiliation: NYU Abu Dhabi
Education: BS New York University, MS Computer Science University of York (Completing in 2025)
Research Websites: Mubadala Arabian Center for Climate and Environmental ScienceS (ACCESS)
Research Areas: Environmental monitoring systems, IoT integration for climate research, data visualization and dashboards, sensor data analysis, machine learning for environmental applications, MLOps pipelines, real-time data processing, predictive analytics for climate science, Python-based data solutions, sensor calibration methodologies, cloud and on-premise data infrastructure, automation in data workflows, AI-driven solutions for resource-constrained environments, NLP models for data analysis
Pearl Rwauya is a Scientific Data Analyst at Mubadala Arabian Center for Climate and Environmental Sciences where he manages climate and environmental data across various platforms, including CKAN, Python-based dashboards, and IoT systems. Rwauya has a strong foundation in IT infrastructure, cloud systems optimization, and automation pipelines, and he is responsible for seamless data collection, processing, and visualization for climate research. Rwauya earned his Bachelor of Science in Computer Engineering from New York University and is currently pursuing a Master of Science in Computer Science at the University of York. His career spans across multiple roles in data science, machine learning, and DevOps engineering.
Prior to joining NYUAD, Rwauya worked as a Research Engineer and Teaching Assistant at the University of Idaho. In this role, he developed Python-based applications for analyzing and post-processing sensor data used in environmental research. His work focused on improving data workflows for field-based phenotyping devices, where he collaborated closely with the mechanical engineering team to facilitate the accurate collection and analysis of sensor data. He contributed to sensor calibration methodologies, reducing errors in data collection and improving the reliability of environmental monitoring systems.
In addition to his academic work, Rwauya has gained valuable industry experience. At DBS Global, he played a key role in developing custom dashboards for business performance metrics and creating APIs to integrate SAP systems with Python-based solutions for real-time data analysis. He also designed web-based applications to streamline business operations, improve software efficiency, and troubleshoot coding issues. His work helped bridge the gap between SAP and non-SAP systems, enhancing data accessibility and supporting business-critical processes. In other roles, Rwauya contributed to machine learning solutions in resource-constrained environments. He worked on optimizing NLP models for low-resource devices and developed MLOps pipelines to automate testing, version control, and continuous deployment of machine learning models.
His research interests include climate data modeling, predictive analytics, and optimizing data management systems for environmental monitoring. Throughout his career, Rwauya has played a role in several projects, such as developing dashboards to visualize real-time weather station data, designing NLP models for sentiment analysis, and managing FHIR-compliant data storage systems. He has also collaborated with cross-functional teams to deploy enterprise solutions that enhance both business and research productivity. In addition to his technical expertise, Rwauya is passionate about creating sustainable and innovative data solutions that support environmental initiatives.
Research Summary
Currently at Mubadala ACCESS, Rwauya’s work centers around developing and managing scalable data solutions for climate and environmental research. His responsibilities include automating data workflows, building interactive dashboards, and ensuring that large-scale environmental datasets are accessible and actionable for researchers. His focus on integrating IoT systems and optimizing data pipelines supports ongoing research into climate impacts and environmental changes. During his time at the University of Idaho, Rwauya focused on developing applications to analyze and process sensor data, particularly in field-based research. He contributed to improving sensor accuracy and data reliability, which was essential for environmental monitoring projects.