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MEng e-voting project published in a journal paper

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.”


Latest academic promotions

We are happy to announce four promotions in the department:

Many congratulations to our colleagues for all their achievements!


Seven papers accepted to NeurIPS 2023

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

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