The Tissue Image Analytics (TIA) Centre is delighted to extend its congratulations to Dang Vu for winning the 2024 Best Thesis Prize for the Department of Computer Science. Dang is a former PhD student from the TIA Centre and his thesis was entitled “Handcrafted Representations for Whole Slide Images”.

On winning his award Dang has said “Winning this award is a great honour and a wonderful acknowledgment of my research. I'm grateful for the support and guidance I've received from my advisors and colleagues throughout this journey. This recognition inspires me to continue working hard and contributing to the field of computer science and medical research”.

The award for Dang comes on the back of former students from the TIA Centre winning 4 previous Best Science Faculty Thesis awards in previous years :-

2015 - Adnan Khan
2017 - Korsuk Sirinukunwattana
2019 - Talha Qaiser
2021 - Simon Graham

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