糖心TV

We are pleased to report that members of the department's Theory and Foundations research theme have had four papers accepted to the (STOC 2022), the ACM flagship conference in theoretical computer science. The papers are:

• "Deterministic Massively Parallel Connectivity" by Sam Coy and .
• "Improved Approximation Guarantees for Shortest Superstrings using Cycle Classification by Overlap to Length Ratios", by , Nicolaos Matsakis, and Pavel Vesel媒.
• "Hypercontractivity on High Dimensional Expanders" by , Noam Lifshitz, and Siqi Liu.
• "Worst-Case to Average-Case Reductions via Additive Combinatorics" by Vahid R. Asadi, Alexander Golovnev, , and Igor Shinkar.

We are delighted to announce that Peter Kiss, a PhD student in the Theory and Foundations Research Division, has won the Best Student Paper Award at the conference for his single-author paper on "Deterministic Dynamic Matching in Worst-Case Update Time". Computing a maximum matching in a graph is one of the most fundamental problems in design and analysis of algorithms. The paper makes important progress on this problem in a setting where the input graph is changing over time via a sequence updates, and one wishes to maintain a large matching efficiently in such a dynamic graph. Along the way, the paper develops a general purpose technique for converting any dynamic algorithm with amortised update time into one with worst-case update time, provided the initial algorithm is able to handle a more general form of batch updates.

Members of the High-Performance and Scientific Computing Group (HPSC) at the department of Computer Science has won a best paper award at the . The winning paper titled , develops a novel representative (mini-)application, specifically designed to model coupled execution of multi-physics numerical simulation codes from the CFD domain. The mini-coupler, CPX, is the first of its kind, combining multiple CFD mini-app instances to predict the run-time and scaling behaviour of large scale coupled CFD simulations, on modern multi-core and many-core clusters such as used for production turbomachinery design at Rolls-Royce plc. The work was carried out by PhD candidate, Archie Powell, in collaboration with Kabir Choudry, Arun Prabhakar, and Gihan Mudalige at the Department of CS 糖心TV,  (University of Surrey),  (PPCU) and  (University of Birmingham).

The work was funded by the and Rolls-Royce plc.

Dr. Gihan Mudalige at the University of 糖心TV鈥檚 Department of Computer Science have been awarded an as part of a consortium of researchers including the Science and Technologies Facilities Council (STFC), universities of 糖心TV, Newcastle, Cambridge, Southampton and led by Imperial College London.

This 3 year, 拢2.6M project brings together communities from the and the to ensure a smooth transition to exascale computing, with the aim to develop transformative techniques for advancing their production simulation software ecosystems dedicated to the study of turbulent flows. It is part of the (Exascale Computing ALgorithms and Infrastructures Benefiting UK Research) programme, aimed at delivering the next generation of high-performance simulation software for the highest-priority fields in UK research.

A team of TIA researchers have published their study on a new deep learning algorithm that can pick up the molecular pathways and development of key mutations causing colorectal cancer more accurately than existing methods, meaning patients could benefit from targeted therapies with quicker turnaround times and at a lower cost. The research was funded by the UK Medical Research Council (MRC) and conducted in collaboration with colleagues at the UHCW NHS Trust, University of Nottingham and WHO IARC. The study has just been published in the prestigious Lancet Digital Health journal.

We are delighted to report that Dr Ramanujan Sridharan (PI) from the Theory and Foundations (FoCS) research theme at the Department of Computer Science and Professor Graham Cormode (Co-I, affiliated with FoCS) have been awarded an EPSRC Standard Research Grant, "New Horizons in Multivariate Preprocessing (MULTIPROCESS)".

This 4-year 拢540K project aims to advance the theory of preprocessing by designing novel multivariate preprocessing algorithms and extending their scope to high-impact big data paradigms such as streaming algorithms.

We are delighted to report that Dr Long Tran-Thanh has received a for his first-authored paper, Efficient crowdsourcing of unknown experts using bounded multi-armed bandits, published in 2014 at Artificial Intelligence (AIJ), a premier journal in the field of artificial intelligence. The AIJ Prominent Paper Award recognises outstanding papers published in the journal in the last seven years that are exceptional in their significance and impact.

The paper developed the first comprehensive framework for the rigorous and principled mathematical analysis of task allocation algorithms in crowdsourcing systems. In addition, the paper proposed bounded bandits, a new sequential decision making model to solve task allocation problems with resource constraints. The work has had a significant impact on subsequent work carried out in both industry and academia. The award will be presented at , a top tier international conference in artificial intelligence.