Emile Niyitanga

Emile is an intern at Climate Adaptation Research and Consulting (CARC), a PlanAdapt affiliate, based in Kigali, Rwanda, under the framework of the Work-Integrated Learning (WIL) Program in collaboration with the African Institute for Mathematical Sciences (AIMS) in Rwanda.

He has an appropriate academic background bridging mechanical engineering and data science, with a focus on production engineering, spatio-temporal modelling, machine learning, and optimization for real-world applications. During his undergraduate studies, he worked on the design and optimization of a corrosion-resistant seed-sowing machine, addressing agricultural productivity and labour efficiency. He also completed a Mechanical Technician Internship at CIMERWA Plc, where he gained practical skills in preventive maintenance, welding, and mechanical fastening. His academic research in Data Science further includes predictive modelling of socio-demographic phenomena, neural network-based DNA sequence classification, and large-scale mobility data analysis using Python, R, Spark, and other computational tools to address complex problems. Emile’s academics have shaped his current passion for integrating engineering, data science, and sustainability, focusing on solutions that are locally relevant, community-centered, and designed to improve efficiency, resilience, and resource management.

He is passionate about developing locally relevant, community-centered solutions to practical engineering and data challenges, emphasizing sustainability and efficiency in applied projects. As an admirer of systematic and interdisciplinary research, his special focus is on integrating multiple perspectives, engineering design, statistical modelling, and computational methods to gain a deeper understanding of tools, strategies, and systems for solving real-world problems. This approach aims to produce actionable insights and applied solutions that can inform policy, enhance agricultural productivity, and optimise resource management in climate-sensitive and resource-limited contexts. Emile is keen about context-driven technological and data-driven interventions, tailored towards improving efficiency, reducing labour burdens, and supporting vulnerable populations and systems that face inequitable environmental and social challenges.

Emile holds an MSc in Mathematical Sciences (with a focus on Data Science) from the African Institute for Mathematical Sciences (AIMS) Rwanda, with a thesis on A Log-Gaussian Cox Process Approach to Modelling Spatio-Temporal Point Patterns. He also earned a BSc with Honour in Mechanical Engineering (with specialisation in Production Engineering) from the University of Rwanda, where he worked on the design and optimization of a seed-sowing machine used in agriculture.