On 23rd June 2022, Professor Feng Hao of the Systems and Security research theme was invited as one of the two expert witnesses to give oral evidence to the Fraud Act 2006 and Digital Fraud Committee appointed by the House of Lords at Parliament on trends of telephone frauds and the landscape of counter-fraud technologies. This is related to an ongoing EPSRC project, led by Professor Feng Hao (PI) from the Department of Computer Science and Dr Adrian von Mühlenen (co-I) from the Department of Psychology, the University of Warwick. In this project, the research team have been investigating a cost-effective solution to combat caller ID spoofing, a technique commonly used by fraudsters and scammers to pretend to call from trusted sources (e.g., banks, HMRC) as part of social engineering attacks. A transcript of the oral evidence session is published on the Parliament website.

Two papers by researchers at the Department of Computer Science have been accepted to the 36th ACM International Conference on Supercomputing ICS 2022 to be held on the 28-30th of June this year. ICS is one of the most prominent and revered conferences in High Performance Computing, highly regarded by the HPC community for publishing leading-edge research in this area. The two papers accepted are:

1. High Throughput Multidimensional Tridiagonal System Solvers on FPGAs (Preprint) by Kamalavasan Kamalakkannan and Gihan Mudalige at Warwick, together with Istvan Reguly (PPCU) and Suhaib Fahmy (KAUST).
2. Clairvoyant: A Log-Based Transformer-Decoder for Failure Prediction in Large-Scale Systems by Khalid Alharthi and Arshad Jhumka at Warwick, together with Sheng Di, Franck Cappello at Argonne National Laboratory. Preprint. The ACM ICS2022 full program can be found here.

Gunduz Vehbi Demirci has been awarded with the Faculty of Science, Engineering and Medicine Post-Doctoral Research Prize 2022 for his paper jointly with Prof. Hakan Ferhatosmanoglu, "Partitioning sparse deep neural networks for scalable training and inference", published in the Proceedings of the ACM International Conference on Supercomputing (ICS '21) (DOI: https://doi.org/10.1145/3447818.3460372).

Training large-scale deep learning models is notoriously difficult. Gunduz develops a highly parallel solution to scale training of sparse deep learning models, which is combined with a novel combinatorial optimisation built on a hypergraph partitioning model, reducing parallelisation overheads and achieving computational balance among processors. An end-to-end software solution is released, enabling competing with big tech companies that have access to large infrastructures and datasets.

The work is summarised in a paper accepted by the 2021 ACM International Conference on Supercomputing, which is a premier conference in high-end systems. The research output will have a great potential to bring significant practical impact in long term as developing such comprehensive solutions takes time and is typically achieved only within large groups.