Mustafa Yasir, a former Warwick Department of Computer Science student who graduated in Summer 2023, wrote up and presented an academic paper on the work carried out as part of his third year project. The paper was accepted to the 3rd Annual Workshop on Graph Learning Benchmarks at KDD 2023, and was presented in California by Mustafa.

Mustafa's third year project idea, supervised by Dr Long Tran-Thanh and titled 'Extending the Graph Generation Models of GraphWorld', started whilst he was interning at Google last summer. Mustafa contacted some researchers at the company working in the Graph ML space, to ask for any relevant project ideas. He bumped into a team who had just published GraphWorld: a tool to change the way Graph Neural Networks are benchmarked, by creating synthetic graph datasets through graph generation models – as opposed to using real-world datasets that are limited in their generalisability and present a major issue facing the field of Graph Learning.

However, since GraphWorld only used a single graph generation model in this process, Mustafa integrated two additional models with the system, ran large-scale GNN benchmarking experiments with these models and published his code to Google’s official GraphWorld repository. The project provides a significant advancement to researchers across the field looking to benchmark models and guide the development of new architectures.

Dr Long Tran-Thanh commented:

What Mustafa and the GraphWorld team has been working on is very important for the machine learning and AI research communities. In particular, there has been a vocal criticism against the whole field that most models are trained on the same public datasets (e.g., ImageNet, MNIST, etc), therefore are not diverse enough. One way to mitigate this issue is to generate realistically looking synthetic data. This need is especially of importance in within the graph learning community. GraphWorld’s aim is to address this exact problem by creating a powerful and convenient tool that can generate a diverse set of graphs, ranging from large social network-style graphs to molecule-inspired ones. Joining this project with the Google researchers is a huge opportunity for Warwick students to participate in a very impactful project.

We are happy to announce that Dr Gihan Mudalige and Dr Victor Sanchez have both been promoted to Professor from 1st August 2023.

Many congratulations to our colleagues for all their achievements!

We are happy to announce five promotions in the department, with effect from 1st August 2023.

• Dr James Archbold has been promoted to Associate Professor (Teaching Focussed)
• Dr Richard Kirk has been promoted to Assistant Professor (Teaching Focussed)
• Dr Claire Rocks has been promoted to Reader (Teaching Focussed)
• Dr Ian Saunders has been promoted to Associate Professor (Teaching Focussed)
• Dr Sathya Subramanian has been promoted to Assistant Professor (Research Focussed)

Many congratulations to our colleagues for all their achievements!

Our teams from Warwick DCS have won both the 1st and 2nd place at the Zero Cost NAS Competition held in conjunction with the AutoML'22 conference.

Over the recent year, Neural Architecture Search (NAS) has attracted a lot of attention. While being able to automate the discovery of better performing neural architectures than hand-crafted ones, it comes at a great price, requiring thousands of GPU hours to perform the search. The Zero Cost NAS competition challenges the participants to design efficient proxies for NAS, using negligible computational resources to evaluate neural architectures.

In collaboration with the AutoCAML team at Samsung AI Cambridge (led by Dr. Hongkai Wen), our research students, Lichuan Xiang and Youyang Sha, proposed new zero-cost NAS metrics that exploit the compressibility of neural networks. Our metrics are extremely efficient to run (reducing search cost from weeks/days to minutes), and achieves impressive results across multiple search spaces and datasets. In the competition, our teams won both the 1st and 2nd places (using different scoring functions), and the performance gap with the 3rd winning team is almost 2x. Checkout our poster here.

Congratulations to Harita Dellaporta for receiving the Best Paper Award at the premier conference in Artificial Intelligence and Statistics (AISTATS) 2022 for her paper on Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap.

We are delighted to report that Dr Long Tran-Thanh has received a AIJ Prominent Paper Award 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 IJCAI 2021, a top tier international conference in artificial intelligence.

We are pleased to report that Dr Simon Graham, Senior Research Fellow from the Department of Computer Science, has received a SEM Faculty Thesis Prize.

Each year, the Faculty of Science, Engineering and Medicine (SEM) funds a prize for the best PhD/EngD thesis in each of its ten departments. Each department nominates a winner out of the applications received after a judging process as determined by the Faculty.

Dr Simon Graham commented regarding his award:

My research focussed on the development of computational tools for the automatic analysis of digitised cancerous tissue samples. Now, I am continuing my research in the area of computational pathology and soon hope to see it utilised in a clinical setting, where it may help improve cancer recognition and treatment planning. I would like to thank my supervisor Professor Nasir Rajpoot and all my collaborators within the Tissue Image Analytics Centre and at University Hospitals Coventry and Warwickshire for their support during my PhD.