We are happy to announce that Dr Arshad Jhumka, Dr Gihan Mudalige and Dr Victor Sanchez have been promoted to Reader, effective from 1 August 2021. Quoting from Victor's recommendation,

Dr Sanchez has brought in significant research funding as PI, and as CoI was key to the successful completion of the large IDENTITY project. The size and quality of Dr Sanchez’s research group is impressive, as well as his leadership in joint supervision with colleagues. ... Dr Sanchez is one of the most effective teachers in the department. Every module he has been responsible for has been successful, including in recent years our flagship Machine Learning popular option for 3rd-year undergraduates. Several times, Dr Sanchez has designed or completely redesigned modules, resulting in evidenced student satisfaction.

from Gihan's recommendation,

The career of Dr Gihan Mudalige in the department has been steadily and rapidly progressing upwards since his appointment as Assistant Professor in 2016, marked by such milestones as obtaining a Royal Society Industry Fellowship and being promoted to Associate Professor in 2018, shortly followed by the award of a £1.8M EPSRC-Rolls Royce grant, and taking up the leadership of the department’s high-performance computing research in 2020. ... Dr Mudalige has contributed considerably to the life of the department through his membership and leadership of the Computer Systems Engineering course committee, as well as having been responsible for internships and intercalated years.

and from the recommendation for Arshad,

The high regard of Dr Jhumka’s work by the international research community is clear from his best paper awards, editorial roles, invited talks, and wide network of collaborators. In the department, Dr Jhumka has been a leader in successfully bridging fundamental and applied research. His expertise has been recognized by a range of industrial and government partners, leading to several grants including the current substantial EPSRC PETRAS project. ... Dr Jhumka has introduced a number of practices into his teaching (such as an approach to coursework assignments by group work) that have been popular with students as well as subsequently adopted by other colleagues.

it remains to say many congratulations!

Shen Wang and Professor Feng Hao from the Systems and Security theme at the Department of Computer Science and Ehsan Toreini from Durham University, have had the paper ‘Anti-Counterfeiting for Polymer Banknotes Based on Polymer Substrate Fingerprinting’, published in the journal IEEE Transactions on Information Forensics and Security, in which they propose a novel technique called Polymer Substrate Fingerprinting, which can identify each banknote’s own unique, unclonable fingerprint.

The researchers have found that every polymer banknote has a unique "fingerprint", which is caused by the inevitable imperfection in the physical manufacturing process, whereby the opacity coating, a critical step during the production of polymer notes, leaves an uneven coating layer with a random dispersion of impurities in the ink. This imperfection results in random translucent patterns when a polymer banknote is back-lit by a light source.

Prof. Graham Cormode of the Department of Computer Science has been named among the 2020 Association for Computing Machinery (ACM) Fellows, for contributions to computer science. The ACM is the world's leading learned society for computer science. Prof. Cormode is recognised for his contributions to data summarisation and privacy enabling data management and analysis. His work on data streams and sketching has been widely implemented in many high tech companies and organisations.

Prof. Yulan He and Prof. Rob Procter have been awarded funding from the EPSRC under the UKRI’s COVID-19 call. 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 Prof. Maria Liakata and Dr. Arkaitz Zubiaga from the Queen Mary University of London.

Warwick'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 TfWM's Regional Transport Coordination Centre for adaptive route planning and traffic management mitigation against disruptions, incidents and roadworks.

The “5G Enabled Dynamic Network Capacity Manager” project is in collaboration with commercial partners, Blacc, Immense, one.network, and O2. The team has won the WM5G’s 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.

Suzanne Candanedo, who recently graduated from Computer Systems Engineering at the University of Warwick, has won the UKESF and UltraSoC Automotive Electronics Competition 2020.

The competition requires entrants to produce a 'think piece' about the future of cyber security for connected and autonomous vehicles, written along the lines of a blog post in style rather than a formal essay. You can read Suzy's winning entry here.

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.