Emmanuel Ombo
What inspired you to pursue this project and join the DAMC CDT?
My journey to being a part of the DAMC CDT started with a B.Eng. in Mechatronics and an MSc in Electronic and Electrical Engineering, where I explored robotics, control systems design, Internet-of-Things (IoT), and energy harvesting systems. During my postgraduate internships, I also worked on a diverse number of projects – from sensor fusion for autonomous navigation, developing unmanned aerial vehicle (UAV) simulations, to applying machine learning to health data challenges. These experiences sparked a fascination with automation and AI as tools for accelerating innovation and data analysis.
I chose this PhD project due to its alignment with my passion for combining robotics, computer vision, and data science to solve real-world problems. I was drawn to the prospect of building an automated system that would make experimentation faster, smarter, and more sustainable – moving beyond the traditional trial-and-error approach.
The DAMC CDT really stood out to me because of its interdisciplinary nature, unique development opportunities, and strong industrial links. In addition, the underlying commitment to digital innovation provides the perfect environment for me to grow as a researcher, develop cutting-edge skills, collaborate across diverse fields, and ensure my research creates a real-world impact.
What is your research project about, and what impact do you hope it will have?
My research project focuses on developing an experimental platform that integrates robotics, light scattering, microscopy, computer vision, and machine learning for high-throughput product formulation and materials analysis, specifically particle characterisation of colloidal systems –mixtures comprising microscopic particles dispersed in a medium. By combining automated systems with advanced analytics, the platform aims to accelerate the discovery and optimization of sustainable formulations, particularly for consumer products such as personal care items. I believe the impact of this research lies in its potential to significantly reduce development timelines, minimize waste, and improve reproducibility in chemical synthesis. By embedding automation and machine learning into materials chemistry, I hope this research will be a positive contribution to more sustainable and efficient approaches for materials innovation, benefiting both industry and the environment.
What has been the most exciting or rewarding part of your PhD journey so far? How does your project benefit from being part of an interdisciplinary CDT like DAMC?
The most rewarding aspect of my PhD journey so far has been having amazing colleagues who inspire me and also getting to collaborate with researchers from diverse disciplines which constantly broadens my perspectives. Being part of the DAMC CDT provides access to a vibrant network and training opportunities that go beyond traditional chemistry. This interdisciplinary environment is invaluable for my project, as it relies on the synergy between chemistry, automation, and computational methods to deliver impactful solutions for product formulation.