Toby Flynn, PhD student in the department's High-Performance and Scientific Computing group, supervised by Prof. Gihan Mudalige together with Dr. Robert Manson-Sawko at IBM Research UK received the best paper award at the 38th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2024) last week in San Francisco US. IPDPS is one of the most prominent and high ranking conferences in parallel and distributed computing, now in its 38th year.

The paper titled "Performance-Portable Multiphase Flow Solutions with Discontinuous Galerkin Methods", details the development of a new performance portable solver workflow using Discontinuous Galerkin (DG) methods for developing multiphase flow simulations based on the OP2 domain-specific language. Results demonstrate scaling on both CPU and GPU systems including UK's national supercomputer, ARCHER2 at EPCC Edinburgh and the European Petascale Supercomputer, LUMI hosted by CSC Finland. The work is a collaboration with IBM Research UK supported by an iCASE award funded jointly by IBM and EPSRC.

The paper pre-print is available here.

Seven papers authored by Computer Science researchers from Warwick have been accepted for publication at the 41st International Conference on Machine Learning, one of the top three global venues for machine learning research, which will be held on 21-27 July 2024 in Vienna, Austria:

• Agent-Specific Effects: A Causal Effect Propagation Analysis in Multi-Agent MDPs, by Stelios Triantafyllou, Aleksa Sukovic, Debmalya Mandal, and Goran Radanovic
• Dynamic Facility Location in High Dimensional Euclidean Spaces, by Sayan Bhattacharya, Gramoz Goranci, Shaofeng Jiang, Yi Qian, and Yubo Zhang
• High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization, by Yihang Chen, Fanghui Liu, Taiji Suzuki, and Volkan Cevher
• Revisiting character-level adversarial attacks, by Elias Abad Rocamora, Yongtao Wu, Fanghui Liu, Grigorios Chrysos, and Volkan Cevher
• Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences, by Andi Nika, Debmalya Mandal, Parameswaran Kamalaruban, Georgios Tzannetos, Goran Radanovic, and Adish Singla
• To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models, by George-Octavian Bărbulescu and Peter Triantafillou
• Towards Neural Architecture Search through Hierarchical Generative Modeling, by Lichuan Xiang, Łukasz Dudziak, Mohamed Abdelfattah, Abhinav Mehrotra, Nicholas Lane, and Hongkai Wen

As part of a 2021/2022 MEng group project, Horia Druliac, Matthew Bardsley, Chris Riches, and Christian Dunn implemented a fully functional end-to-end (E2E) verifiable online voting system and conducted a successful trial among the residents of New Town in Kolkata, India during the 2022 Durga Puja festival celebration. This was the first time an E2E online voting system was built and tested in India. The feedback was overwhelmingly positive. Full details about the implementation, the trial and the voter feedback are written in a paper, published in the Journal of Information Security and Application. A free version of the paper is available on IACR e-print as a technical report. Also, see the earlier news item about this Durga Puja trial.

Professor Feng Hao, who supervised this group project, commented: “This is great teamwork. The four MEng students worked relentlessly for nearly a year, with good assistance from Luke Harrison and Professor Bimal Roy. The e-voting system was developed at an industry standard and worked flawlessly during the Durga Puja trial. Several government officials from India also helped us, providing invaluable support for the trial. We sincerely thank them in the acknowledgement section of the paper.”