Seminars are currently held at 15:00-16:00 on Wednesdays in person or join by Teams. All are welcome to join.
2024/2025 Seminar Programme
Seminars are currently held at 15:00-16:00 on Wednesdays in person or join by Teams
Select the relevant date to view the abstract and biography of each speaker.
30/10/2024 15:00-16:00 (QM245)
Title: AI with a Human Face: Towards Human-Centred AI Solutions for SDGs
Speaker: Dr Makuochi Nkwo, CMS, University of Greenwich
Abstract
This talk reimagines how AI-powered smart city solutions can be designed to advance the realization of the United Nations Sustainable Development Goals (SDGs) such as sustainable cities and communities, and energy conservation while maintaining a human touch through thoughtful Human-Computer Interaction design approaches that ensure AI remains accessible, ethical, inclusive, and aligned with human values and community needs.
Brief biography
Dr Makuochi S. Nkwo is a lecturer and Human-Centred AI researcher at the University of Greenwich, London, UK. Makuochi’s current research focuses on responsible designs and innovations. He works at the intersection of human-computer interaction, artificial intelligence, digital ethics and governance, and their application to health, education, ecommerce, and sustainable future. While he has won grants from the Alan Turing Research Institute (2023) and the University of Greenwich ECA Pilot Project Fund (2024), his empirical research outputs using qualitative and quantitative methods have contributed significantly to addressing industry-based problems and sustainable development goals. He prioritizes exceptional leadership in institutions and organizations to drive benefits realization for stakeholders.
20/11/2024 15:00-16:00 (QM061)
Title: A Heuristic Informative Path-Planning Algorithm for Mapping Unknown Areas
Speaker: Dr Mobolaji Orisatoki, CMS, University of Greenwich
Abstract
Informative path planning algorithms play a crucial role in applications such as disaster management to efficiently gather information in unknown environments. This is, however, a complex problem that involves finding a globally optimal path that gathers the maximum amount of information (e.g., the largest map with a minimum travelling distance) while using partial and uncertain local measurements. This presentation introduces a novel heuristic algorithm that continuously evaluates the potential mapping gain across various sub-areas of a partially constructed map. These evaluations are then used to guide the robot's navigation in a locally optimal manner.
Biography
Mobolaji O. Orisatoki received the B.Sc. from The University of Greenwich , in 2006, and the MSc degree from Royal Holloway, University of London, in 2012, and the PGCE Institute of Education-University College London, in 2013 and completed PhD degree with the Department of Engineering and Design, University of Sussex, U.K in 2024. He worked as an Associate Lecturer with the Department of Engineering and Design, University of Sussex from 2019 to 2023. He is currently a Lecturer in Computer Science at the University of Greenwich. His research interests include path planning, system optimisation and control, system dynamics, and multi-agent systems.