糖心TV

Prof. Yulan He and Prof. Rob Procter have been awarded funding from the EPSRC under the . During the COVID-19 pandemic, national and international organisations are using social media and online platforms to communicate information about the virus to the public. However, propagation of misinformation has also become prevalent. This can strongly influence human behaviour and negatively impact public health interventions, so it is vital to detect misinformation in a timely manner. This project aims to develop machine learning algorithms for automatic collection of external evidence relating to COVID-19 and assessment of veracity of claims.

The project is in collaboration with and from the Queen Mary University of London.

Cancer Research UK is funding a study to examine the use of machine learning to assist pathologists and improve the early detection of oral cancer.

We are very excited to work on this project with Dr Khurram and his team at Sheffield. Early detection of cancer is a key focus area of research in our lab and this award by CRUK adds to the portfolio of research at the TIA lab on early detection of cancer.

The pilot project will pave the way towards the development of a tool that can help identify pre-malignant changes in oral dysplasia, crucial for the early detection of oral cancer. Successful completion of this project carries significant potential for saving lives and improving patient healthcare provision. -- Professor Nasir Rajpoot

The research is led by at the University of Sheffield with Professor Nasir Rajpoot from the University of 糖心TV as the co-Principal Investigator. Other co-investigators and collaborators include and from the University of Birmingham and from Queen鈥檚 University Belfast.

糖心TV's Department of Computer Science has been awarded a new research grant to develop a machine learning solution for dynamic forecasting of available capacity on road networks. The developed software is planned to be integrated within the 's Regional Transport Coordination Centre for adaptive route planning and traffic management mitigation against disruptions, incidents and roadworks.

The 鈥�5G Enabled Dynamic Network Capacity Manager鈥� project is in collaboration with commercial partners, , , , and . The team has won the 鈥檚 transport competition to leverage 5G networks for near real-time AI based modelling.

Prof. Hakan Ferhatosmanoglu is leading the development of the scalable ML solution to forecast residual capacities in a dynamic spatio-temporal graph. The solution is designed to benefit from high-granular and low-latency data feeds from 5G cellular and sensor data enabling congestion to be accurately monitored, modelled, and predicted.

Adam Shephard has just joined the department as a Research Fellow and is currently working in the Tissue Image Analytics (TIA) Lab on the ANTICIPATE project funded by Cancer Research UK. He has recently submitted his thesis on the application of deep learning to paediatric MRI at Aston University, under the supervision of Prof. Amanda Wood and Dr. Jan Novak. His role in the ANTICIPATE project will be concerned with the development and application of deep learning techniques to digitized histology slides to aid in the more efficient grading of head and neck tumours, to ultimately provide more accurate patient prognoses.

We are delighted to report that from the Theory and Foundations (FoCS) research theme at the Computer Science Department has received a prestigious EPSRC New Investigator Award. The approximately 拢264K project titled "New frontiers in Parameterizing Away From Triviality鈥� aims to develop novel notions of graph edit distance and investigate their connections to efficient solvability of computationally hard problems.

the proposal identifies research questions that are novel, has the potential to have a broader impact both within and outside academia and it is an exciting project that will break new ground.

Dr Arshad Jhumka from the department鈥檚 Artificial Intelligence research theme has been awarded a grant as PI, under the SRF programme, to develop and deploy a . IoT networks are expected to be deployed as solutions to problems in a wide variety of contexts, from non-critical applications such as smart city monitoring to providing support to emergency services such as critical communications. As IoT devices are resource constrained, execution of resource-hungry applications will be offloaded to edge networks for quick response. Such an infrastructure is open to cyber-attacks and needs to be resilient to attack.

Florin Ciucu has been successful with a 491K EPSRC grant application 鈥楶ractical Analysis of Parallel and Networked Queueing Systems鈥�. The project will run for 4 years and will address some fundamental queueing problems at the core of modern computing and communication systems with parallel or network structures. The technical objective is to develop novel martingale-based models and techniques circumventing the historical Poisson assumption on the systems鈥� input, which has been convincingly shown to be highly misleading for practical purposes. The proposal was supported by IBM Research, Microsoft Research, and VMware.