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.
12/03/2025 15:00-16:00 (QM369)
Title: Revolutionising Human-Machine Interfaces: Harnessing Neurotechnology and AI to Restore/Enhance the Sense of Agency
Speaker: Dr Anirban Dutta
Presentation abstract:
Skill training and movement rehabilitation rely on optimising the sense of agency (SoA)—the feeling of control over one’s actions. In psychotherapy, suggestion and imagery are key tools for reshaping movement expectations. Here, suggestion guided imagery can help novices and patients reimagine movement scenarios with positive outcomes. We integrate these principles with an adaptive extended reality (XR)-based human-machine interface (HMI) to simulate ‘skilled’ movement states, enhancing neurorehabilitation and motor training.
Using a haptic robotic device, alongside multimodal brain imaging (fNIRS, EEG), we examine how action-effect contingencies shape SoA in motor learning tasks. Individuals with functional movement disorders (FMD) exhibit reduced sensory attenuation, disrupting perception-action binding in predictive coding frameworks. This dysfunction relates to impaired beta synchronisation (‘beta rebound’), affecting internal feedforward estimation in Bayesian sensorimotor integration.
We developed a Bayesian predictive coding approach to HMI that can optimally integrate top-down imagery and bottom-up sensory feedback, restoring/augmenting predictive mechanisms. This scalable, home-based skill training and rehabilitation approach aligns with healthcare priorities, offering innovative solutions for FMD rehabilitation and skill training.
Presenter's biodata:
Dr Anirban Dutta is a biomedical engineer and entrepreneur whose career bridges academia and industry, driving advancements in neuroengineering for neurorehabilitation. He holds degrees from Jadavpur University (B.E.), University of Florida (M.S.), Case Western Reserve University (Ph.D.), and Charité – Universitätsmedizin Berlin (M.Sc.). Dr Dutta has held pivotal roles at leading institutions worldwide, including Janelia Farm Research Campus (USA), Rehabilitation Institute of Chicago (USA), INRIA (France), and University Medical Center Göttingen (Germany). He also served as Assistant and Associate Professor of Research at the University at Buffalo, USA. Currently, he is a Associate Professor of Quantitative Biomedicine at the University of Birmingham, continuing his contributions to neurotechnology and healthcare innovation.
19/03/2025 15:00-16:00 (QM369)
Title: The bilevel optimisation renaissance through machine learning: lessons and challenges.
Speaker: Professor Alain Zemkoho
Presentation abstract:
Bilevel optimisation has been part of machine learning for over 4 decades now, although perhaps not always in an obvious way. The interconnection between the two topics started appearing more clearly in publications since about 20 years now, and in the last 10 years, the number of machine learning applications of bilevel optimisation has literally exploded. This rise of bilevel optimisation in machine learning has been highly positive, as it has come with many innovations in the theoretical and numerical perspectives in understanding and solving the problem, especially with the rebirth of the implicit function approach, which seemed to have been abandoned at some point. Overall, machine learning has set the bar very high for the whole field of bilevel optimisation with regards to the development of numerical methods and the associated convergence theory, as well as the introduction of efficient tools to speed up components such as derivative calculations among other things. However, it remains unclear how the techniques from the machine learning literature can be extended to other applications of bilevel programming. For instance, many machine learning loss functions and the special problem structures enable the fulfilment of some qualification conditions that will fail for multiple other applications of bilevel optimisation. We will start this talk with the definition of a bilevel optimisation and some applications in economics. We will then provide an overview of machine learning applications of bilevel optimisation, while giving a flavour of corresponding solution algorithms and their limitations. Subsequently, we will discuss possible paths for algorithms that can tackle more complicated machine learning applications of bilevel optimisation, while also highlighting lessons that can be learned for more general bilevel programs.
Presenter's biodata
Alain is a full professor of mathematical optimisation at the School of Mathematical Sciences within the University of Southampton where he is affiliated to the OR Group and CORMSIS. Prior to joining Southampton, he was a research fellow at the University of Birmingham and had previously worked as a research associate at the Technical University of Freiberg. Alain is currently an Alexander von Humboldt Experienced Fellow 2024-2026 with the Karlsruhe Institute of Technology in Germany, and is also a fellow of both the Institute of Mathematics & Its Applications and the Higher Education Academy. He previously served as a fellow of the Alan Turing Institute for Data Science and Artificial Intelligence for around 5 years.
Alain is broadly interested in nonconvex and nonsmooth continuous optimisation problems. More specifically, his primary research interests has so far revolved around optimisation problems with a hierarchical structure; in particular, he has worked extensively on two-level optimisation problems commonly known as bilevel programming problems. More recently, he has developed interest in machine learning modelling and theory, as well optimisation related algorithms.
19/02/2025 15:00-16:00 (QM063)
Title: Mathematical Optimisation of Integrated Supply Chain Systems with Response Time Constraints and Lateral Transhipments.
Speaker: Dr Samuel Zelibe
Presentation abstract:
Optimising inventory control and facility placement in two-echelon systems requires balancing cost and response time. In supply chain management, lateral transshipment enhances flexibility by enabling inventory sharing between service facilities. Here, we integrate lateral transshipment into a location-inventory system with response time constraints, improving cost efficiency and service reliability.
Using a continuous-time Markov process approach, we determine steady-state levels for on-hand inventory, lateral transshipment, and backorders in a system governed by an (S-1, S) policy. This predictive framework supports optimal decision-making in facility location and inventory distribution.
We developed a mixed-integer nonlinear programming model that integrates lateral transshipment into a two-echelon location-inventory system. This approach minimises total system cost while jointly optimising facility placement, customer assignments, and base-stock levels.
By leveraging Lagrange decomposition, we establish the model's convexity, ensuring efficient and optimal solutions. Computational testing using general algebraic modelling system confirms that lateral transshipment reduces costs compared to models without it. Additionally, we demonstrate that transshipment maintains cost consistency under varying response time constraints.
This scalable, data-driven optimisation framework aligns with industry priorities, offering innovative solutions for responsive supply chain management and inventory control.
Presenter's biodata
Dr Samuel Zelibe is a Lecturer in Mathematics (Data Science) at the School of Computing and Mathematical Sciences, University of Greenwich. His research focuses on operational research, mathematical optimisation, probabilistic modelling, and lifetime processes, with applications in supply chain systems, logistics, and healthcare . He has a particular interest in multi-echelon inventory systems, lateral transshipment strategies, and response-time constraints, working to develop mathematical frameworks that enhance efficiency and resilience in complex logistics networks. His work in these areas, have contributed to advancements in cost-effective decision-making and resource allocation. Presently, he is exploring intersections between mathematical optimisation and data science.
Alongside his research, Samuel is an experienced educator committed to promoting an engaging and inclusive learning environment. He has designed and delivered courses in optimisation, operational research, and data-driven decision-making, integrating innovative teaching methods to support student success. Through both his teaching and research, he aims to bridge the gap between theoretical mathematics and practical applications, driving impactful solutions across industries.
4/12/2024 15:00-16:00 (QM061)
Title: Bridging Algorithms and Applications: Virtual Testing in Biomedical and Composite Material Research
Speaker: Dr Michael Okereke
Presentation abstract:
This presentation explores the transformative role of computational modelling algorithms in addressing complex challenges in biomedical engineering and advanced material science. Focusing on virtual testing, it highlights applications in stent modelling for coronary artery disease, Parkinson’s disease research, composite material analysis, and constitutive model development for polymers. By leveraging computational approaches, these models simulate real-world behaviour, reducing reliance on experimental trials and enabling cost-effective, bespoke solutions. Case studies will demonstrate how algorithms bridge theory and application, offering insights into the mechanical behaviour of intricate systems. The session underscores the versatility of computational tools in driving innovation across multidisciplinary scientific domains.
Presenter's biodata:
Dr Michael Okereke is an Associate Professor of Engineering Mechanics at the University of Greenwich. He holds a Bachelor of Engineering in Mechanical Engineering and a PhD in Engineering Science, the later from the University of Oxford. Following his PhD, he worked as a Postdoctoral Researcher at Oxford University, specialising in the modelling and impact behaviour assessment of 3D reinforced textile composite materials.
Michael’s research interests include constitutive material model development, biomedical engineering applications, impact behaviour analysis of materials, composite materials modelling, and finite element analysis. He is also deeply involved in exploring the pedagogy of technology-enhanced learning, focusing on adapting to the challenges and opportunities presented by artificial intelligence in higher education.
He is the author of a postgraduate-level textbook and has published over 70 peer-reviewed articles in high-impact journals. His work integrates computational modelling with practical applications to drive innovation across engineering disciplines.
Michael’s dedication to excellence in teaching and learning has been recognised with the prestigious Principal Fellowship of the Higher Education Academy. Through his research and academic leadership, he continues to inspire innovation and foster the growth of future engineers and researchers.
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.
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 realisation 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 prioritises exceptional leadership in institutions and organisations to drive benefits realisation for stakeholders.