The work of Professor Graham Cormode has been recognized with a “test of time” award. The ACM SIGMOD conference presents an award each year for the paper from SIGMOD 10-12 years previously that has had the biggest impact, and passed the “test-of-time”. The 2014 paper “PrivBayes: private data release via bayesian networks” (Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, Xiaokui Xiao) was selected for this honour. The award will be presented at the 2024 ACM SIGMOD Conference in Santiago.

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