Panagiotis D. Ritsos

MEng PhD Essex, FHEA

Senior Lecturer in Visualization

XReality, Visualization and
Analytics (XRVA) Lab

Visualization, Data, Modelling and
Graphics (VDMG) research group,

School of Computer Science
and Engineering,

Bangor University,
Dean Street, Bangor,
Gwynedd, UK, LL57 1UT

PhDs in AI, Immersive Analytics and Edge Computing, and Hydrology (UKRI CDT),

We have some exciting PhD opprtunities, funded from our UKRI Centre for Doctoral Training in Artificial Intelligence, Machine Learning & Advanced Computing.

We are increasingly being immersed in a technology-mediated world, where the omni-presence of data introduces increased needs in mechanisms facilitating in-situ cognition, reasoning and sensemaking. In parallel, edge computing, facilitated by future networks, such as 5G, is transforming the way data is being processed and delivered from millions of devices around the world, bringing computing and analytics close to where the data is created. Building on these synergies, this project will investigate the use of edge-based object recognition using distributed neural networks (DNN), as a mechanism for in-situ registration and data processing for mobile, Web-based Immersive Analytics (IA) in Extended Reality (XR). Object-recognition can provide accurate and real-time registration, yet its practical application still faces important challenges. Current object-recognition systems are either self-contained, or cloud-based, yet face low latency and poor user experience respectively. Deep Learning, and DNNs, can provide effective solutions for object detection, and ameliorate these challenges. In addition, they have the potential to provide adaptive MR interfaces, and multimodal sensing capabilities useful for advanced IA experiences.

Project description:

Hydrological models are essential tools for simulating streamflow in river basins and are widely used for understanding, and forecasting, a river’s flood response to storm events. However, appropriate application of hydrological models requires a priori calibration of parameters using historical measured streamflow data. Previous research has shown that the relationship between hydrological model parameters and physical river basin properties (e.g., topography, soils, land use) is too complex to characterize using traditional statistical models. This limits the ability to determine how parameter values will modify if land use change alters the physical structure of a river basin. Recent advances in Artificial Intelligence (AI), specifically in Deep Learning (DL), have resulted in the ability to provide efficient high-dimensional interpolators that can handle data of multiple dimensions and heterogeneous information, such as those encountered in hydrological modelling. In this project, our goal is to develop AI techniques that can help improve the ability of hydrological models to predict the impact of land use change on river flood risk. Specifically, we propose a novel use of AI and information visualization to interactively relate hydrological model parameters to the physical properties of river basins. Our approach will involve development of DL techniques to extract high level abstractions in the hydrological model and physical river basin data, which can be used to test the impact of land management decisions on river flood risk. This abstraction will be made available to end-users via an interactive visualization interface to facilitate the flood risk investigation of multiple scenarios of land management changes (e.g., increase in urbanization by 10%). Our training dataset will include data from >1000 river basins across the UK, and the coupled AI-hydrological modelling workflow will be streamlined to operate on Supercomputing Wales High-Performance Computing (HPC) framework.

More Info:

More information on this exciting research can be found at

The deadline for applications is 12th February 2020; however applications will be accepted until all positions are filled.

For questions, please get in touch with me or Sopan