Data Science News
ERC Consolidator Grant for Sayan Bhattacharya
We are happy to announce that an academic from our department, Dr Sayan Bhattacharya, is among the winners of ERC Consolidator Grants 2024. According to the European Research Council: "These grants, totalling €678 million, aim to support outstanding scientists and scholars as they establish their independent research teams and develop their most promising scientific ideas. The funding is provided through the EU's Horizon Europe programme."
Sayan Bhattacharya has been awarded a €2million ERC Consolidator grant for a 5-year project entitled "Towards a Dynamic Algorithms Centric Theory of Linear Programming" (DYNALP). The project aims to build a new theory exploring the interplay between two key concepts, Linear Programming and Dynamic Algorithms, which, in turn, will pave the way towards attacking outstanding open questions in the field of Theoretical Computer Science.
In the 2024 round, this was the only project from the United Kingdom that was awarded an ERC Consolidator Grant in Computer Science and Informatics (PE6 panel). The press release contains more information about the ERC funding programme.
Google PhD Fellowship for Martin Costa
We are delighted to announce that Martin Costa, a PhD student at the Theory and Foundations research division, has received a highly competitive Google PhD Fellowship for his work on designing clustering algorithms for dynamic datasets. The Fellowship comes in the form of an unrestricted gift from Google, of 60,000 USD per year, for up to two years. Under the category of "Algorithms and Theory", besides Martin only two other PhD students in Europe (from University of Cambridge and ETH Zurich) received a Google PhD Fellowship this year. Many congratulations to Martin for this achievement!
Best Paper Award at ACM Mobihoc 2024
A paperLink opens in a new window co-authored by Arpan MukhopadhyayLink opens in a new window has received the Best Paper Award at ACM Mobihoc 2024Link opens in a new window. 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.