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.
Dr Theo Damoulas (Department of Computer Science) along with Dr David Armstrong (Department of Physics) and Jevgenij Gamper (Department of Mathematics) have developed probabilistic machine learning algorithms that can separate out real planets from fake ones in the large samples of thousands of candidates found by telescope missions such as NASA’s Kepler and TESS. The results of which have led to fifty new confirmed planets, the first to be not only ranked but also probabilistically validated by machine learning.
The paper "Exoplanet Validation with Machine Learning: 50 new validated Kepler planets" has been accepted to the Monthly Notice of the Royal Astronomical Society, DOI: 10.1093/mnras/staa2498
Work performed by Computer Systems Engineering student Michael Shanta for his 3rd year project, supervised by Dr. Marina Cole and Dr. Siavash Esfahani in the School of Engineering, was written up in a paper that was recently accepted for presentation at the IEEE Sensors 2020 Conference.
For his 3rd year project Michael worked on developing machine learning techniques for an Electronic Nose in order to classify odours based on the sensor responses. The system aims to detect incontinence incidents, allowing alerts to be sent to relevant personnel from an IoT network via a cloud server.
A further piece of excellent news: Dr Paolo Turrini has been promoted to Associate Professor, effective from 1 September 2020. Many congratulations to Paolo, whose recommendation says:
Dr Turrini has maintained an internationally recognised publication trajectory, with papers appearing in highly-ranked journals and conferences. He has also grown his research group to 4 PhD students currently, and developed fruitful research collaborations with several academics in the department. … Dr Turrini has contributed to designing two 4th-year/MSc modules. He has been attentive to his teaching to an exemplary degree, resulting in consistently positive feedback from students...
Dr Arshad Jhumka from the department’s Artificial Intelligence research theme has been awarded a grant as PI, under the PETRAS SRF programme, to develop and deploy a trusted edge-based Internet of Things (IoT) network. 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 ‘Practical Analysis of Parallel and Networked Queueing Systems’. 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’ input, which has been convincingly shown to be highly misleading for practical purposes. The proposal was supported by IBM Research, Microsoft Research, and VMware.
Dr Victor Sanchez (PI) from the department's Artificial Intelligence research theme and Prof. Carsten Maple (Co-I) from Warwick Manufacturing Group have been further awarded a research grant by the Defence and Security Accelerator (DASA), which is part of the Ministry of Defence, to continue with Phase 2 of the project R-DIPS - "Real-time Detection of Concealment of Intent for Passenger Screening." The project, which began on October 2019 and ends on February 2021, aims at developing a machine learning and computer vision solution to track, in real-time, multiple individuals across a set of non-overlapping surveillance cameras to detect those with suspicious behaviours and movements within an airport. The project will improve the screening process of passengers to detect those attempting to mask nefarious intent. The R-DIPS project is an international collaboration with Prof. Chang-Tsun Li who is also affiliated with Deakin University, Australia.