We are seeking PhD candidates in the topic of Multiagent Systems and related areas, with particular emphasis on one or more of: computational social choice, algorithmic game theory, multiagent learning, and social and economic networks. The multiagent systems researchers at University of Warwick include Markus BrillLink opens in a new windowLink opens in a new window, Ramanujan SridharanLink opens in a new windowLink opens in a new window, Long Tran-ThanhLink opens in a new windowLink opens in a new window, Debmalya Mandal and Paolo TurriniLink opens in a new window

Seven papers authored by Computer Science researchers from Warwick have been accepted for publication at the 37th Conference on Neural Information Processing Systems, the leading international venue for machine learning research, which will be held on 10-16 December 2023 in New Orleans, Louisiana, USA:

• EV-Eye: Rethinking High-frequency Eye Tracking through the Lenses of Event Cameras, by Guangrong Zhao, Yurun Yang, Jingwei Liu, Ning Chen, Yiran Shen, Hongkai Wen, and Guohao Lan
• Fully Dynamic k-Clustering in Õ(k) Update Time, by Sayan Bhattacharya, Martin Costa, Silvio Lattanzi, and Nikos Parotsidis
• Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks, by Jiayuan Ye, Zhenyu Zhu, Fanghui Liu, Reza Shokri, and Volkan Cevher
• Learning a Neuron by a Shallow ReLU Network: Dynamics and Implicit Bias for Correlated Inputs, by Dmitry Chistikov, Matthias Englert, and Ranko Lazic
• On the Convergence of Shallow Transformers, by Yongtao Wu, Fanghui Liu, Grigorios Chrysos, and Volkan Cevher
• Towards Data-Agnostic Pruning At Initialization: What Makes a Good Sparse Mask? by Hoang Pham, The Anh Ta, Shiwei Liu, Lichuan Xiang, Dung Le, Hongkai Wen, and Long Tran-Thanh
• Towards Unbounded Machine Unlearning, by Meghdad Kurmanji, Peter Triantafillou, and Eleni Triantafillou

We are delighted to announce that Peter Kiss, a PhD student in the Theory and Foundations Research Division, has received the best student paper award at European Symposium on Algorithms (ESA) 2023, for his joint work with Joakim Bilkstad for the paper: "Incremental (1-eps)-approximate dynamic matching in O(poly(1/eps)) update time". The paper considers the problem of maintaining a large matching in a graph that is undergoing a sequence of edge insertions. They present an algorithm for this fundamental problem in dynamic graph algorithms, which has near-optimal approximation ratio and an update time that does not grow at all with the size of the input and is also polynomial in 1/\eps (the error parameter). In addition, their approach is simpler than previous algorithms on the same problem that achieved weaker guarantees.

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 pleased to announce that Dr Teddy Cunningham has been awarded a Faculty of Science, Engineering, and Medicine (SEM) PhD Thesis Prize. Each year, the SEM Faculty funds a prize for the best PhD/EngD thesis entered into the competition. Each department nominates a winner out of the applications received after a judging process as determined by the Faculty.

Teddy’s thesis is titled “Generating and Sharing Differentially Private Spatio-Temporal Data Using Real-World Knowledge”, and was supervised by Prof Hakan Ferhatosmanoglu. The thesis includes solutions for sharing trajectory data using local differential privacy, and incorporating constraints and relationships of data records into differential privacy that improves their utility while preserving the theoretical privacy guarantees. An example application is using road network information for improving the quality of privately shared location datasets.

Researchers from the Systems and Security theme, Department of Computer Science have created a new spin-out company, SEEV Technologies Ltd, to build end-to-end (E2E) verifiable e-voting systems for future elections. An E2E verifiable voting system allows every voter to verify that their vote is properly cast-as-intended, recorded-as-cast and tallied-as-recorded while preserving the voter's privacy. SEEV (self-enforcing e-voting) is a new paradigm of E2E voting technology that enables voters to fully verify the tallying integrity of an election without needing any trustworthy tallying authority, hence the system is "self-enforcing".

This joint spin-out from the University of Warwick and Newcastle University is built on an ERC-funded starting grant ("Self-Enforcing E-Voting System: Trustworthy Election in Presence of Corrupt Authorities", No. 306994, PI: Professor Feng Hao) initially hosted at Newcastle University and later transferred to the University of Warwick. The company is co-founded by Professor Feng Hao and Dr Siamak Shandahshti (co-inventors), and led by Dr Stewart Hefferman (CEO). SEEV has been prototyped and successfully tested in several trials in the past, supported by an ERC Proof of Concept grant (No. 677124), a Royal Society International collaboration award (CA\R1\180226), and an Innovate UK Cybersecurity Academic Startup Accelerator Programme (CASAP). SEEV Technologies Ltd has received seed funding from Oxford-based Global Initiative to build SEEV systems for real-world elections.

A University of Warwick press release is here.

Five papers from the Theory and Foundations (FoCS) Research Group and the Centre for Discrete Mathematics and its Applications (DIMAP) have been accepted to the 64th IEEE Symposium on Foundations of Computer Science (FOCS 2023), the IEEE flagship conference in theoretical computer science that will be held on November 6 - 9, 2023 in Santa Cruz, California, USA:

• "Chasing positive bodies" by Sayan Bhattacharya, Niv Buchbinder, Roie Levin, and Thatchaphol Saranurak.
• "Dynamic (1+epsilon)-approximate matching size in truly sublinear update time" by Sayan Bhattacharya, Peter Kiss, and Thatchaphol Saranurak.
• "Polynomial-time pseudodeterministic construction of primes" by Lijie Chen, Zhenjian Lu, Igor C. Oliveira, Hanlin Ren, and Rahul Santhanam.
• "Approximating edit distance in the fully dynamic model" by Tomasz Kociumaka, Anish Mukherjee, and Barna Saha.
• "Traversing combinatorial 0/1-polytopes via optimization" by Arturo Merino and Torsten Mütze.