天美传媒

We are delighted to celebrate the 20 accepted papers at the , involving colleagues and students from the Department of Computer Science (DCS) at the 天美传媒, together with their collaborators. NeurIPS is the leading international venue for AI and machine learning research and will be held on 6-12th December, 2026 in Sydney, Australia along with two satellite locations at Paris, France and Atlanta, United States of America.

The workshop Algorithms & Complexity @ Warwick took place at the 天美传媒 on September 22-23, 2025 (see for more details).

The aim of the event was to highlight several recent exciting advances in the field of Algorithms and Complexity, to facilitate interactions within the research community, and to provide an excellent opportunity for Theory researchers (including academics, postdocs, and students) to connect and collaborate.

We had a fantastic list of invited speakers by renowned world experts: (Technical University of Catalonia), (University of Bath), (University of Pennsylvania), (Max Planck Institute for Informatics), (Charles University in Prague), (University of Sheffield and University of Haifa), (University of Oxford), (University of Cambridge), (University of Toronto).

co-authored by Arpan MukhopadhyayLink opens in a new window has received the Best Paper Award at . Mobihoc is a premier international conference on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing. The other authors in the paper are Samira Ghanbarian (uWaterloo), Ravi R. Mazumdar (uWaterloo), and Fabrice Guillemin (Orange Labs, France).

The paper addresses the problem of optimally allocating processors to parallelisable tasks having arbitrary concave speed-up functions. In general, determining the optimal number of processors to allocate to each task in an online fashion is a hard problem since allocating too many processors to one job will make those processors unavailable to other jobs whereas allocating too few processors will result in a small speed-up for the job. The paper proposes a simple randomised algorithm for determining the optimal number of processors to allocate to each job without requiring preemption (or repacking). It shows that the proposed algorithm is asymptotically optimal as the number of processors becomes large (which is often the case in modern clouds) and is also robust to variations in the job size distribution. This is the first time such an algorithm has been found in the literature.