We are excited to share that our team “TIAKong” secured leading positions in the recent PUMALink opens in a new window (Panoptic segmentation of nuclei and tissue in advanced Melanoma) Challenge, organized by the Department of Medical Oncology, University Medical Center Utrecht, in the Netherlands. With over 300 participants from around the globe, this challenge aimed to advance automated panoptic segmentation techniques for H&E-stained melanoma tissue images.

Led by our PhD students Jiaqi Lv and YiJie Zhu, and supported by Brinder Singh Chohan, Shan E Ahmed Raza, with an external collaborator Carmen Guadalupe Colin Tenorio from the Medical University of Vienna. TIAKong achieved first place in Track 1 and second place in Track 2. This outstanding performance underscores the team’s dedication to pushing the boundaries of medical imaging and improving our understanding of advanced melanoma.

We look forward to building on these results and sharing further developments of our panoptic segmentation model in the near future.

We are excited to announce that our team “TIAKong” secured leading positions in the recent Monkey Grand ChallengeLink opens in a new window, organized by the Department of Pathology, Radboudumc, Nijmegen, The Netherlands. Drawing more than 400 participants from around the globe, the challenge focused on automated detection and classification of mononuclear leukocytes in PAS-stained transplant kidney biopsy images.

Led by our PhD student Jiaqi Lv, and supported by Esha Nasir, Kesi Xu, Mostafa Jahanifar, Brinder Singh Chohan, Behnaz Elhaminia, and Shan E Ahmed Raza, TIAKong’s cell detection and classification model finished first place in the overall detection track and second place in the detection classification track.

The team is currently evaluating the model for publishing and sharing the code through open-source platforms. We look forward to sharing more updates in the near future.

A paper co-authored by Matthias C. Caro has been featured on the cover of PRX Quantum. PRX Quantum is a premier journal for quantum information science and technology research. The work was a collaboration with Haimeng Zhao (Caltech & Tsinghua), Laura Lewis (Caltech & Google), Ishaan Kannan (Caltech), Yihui Quek (Harvard & MIT) and Hsin-Yuan Huang (Caltech, Google & MIT).

Characterizing a quantum system by learning its state or unitary evolution is a key tool in developing quantum devices, with applications in practical quantum machine learning, benchmarking, and error mitigation. However, in general, this task requires exponentially many resources. Prior knowledge is required to circumvent this exponential bottleneck. The paper pinpoints the complexity for learning states and unitaries that can be implemented by quantum circuits with a bounded number of gates, a broad setting that is topical for current quantum technologies. When measuring efficiency with respect to the number of accesses to the unknown quantum state or unitary, the paper presents and implements algorithms that are provably optimally efficient. Thereby, this work establishes the equivalence between the complexity of learning quantum states or unitaries and the complexity of creating them. However, it also shows that the data processing necessarily requires exponential computation time under reasonable cryptographic assumptions.

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.

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!

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.

Neha Rino, a PhD student in the Theory and Foundations group in the Department of Computer Science and a member of the Cyber Security group at WMG, has won an Oded Maler award at FORMATS 2024.

The Oded Maler award is a distinction presented for the best paper of the International Conference on Formal Modeling and Analysis of Timed Systems (FORMATS). This year's edition of the conference was held in September in Calgary, Canada, jointly with QEST (International Conference on Quantitative Evaluation of SysTems) as a common research forum dedicated to quantitative modelling, analysis, and verification.

Neha's paper, "Efficiently Computable Distance-Based Robustness for a Practical Fragment of STL", is co-authored with Mohammed Foughali and Eugene Asarin, both from Université Paris Cité and IRIF in Paris, France, where Neha completed the Master's degree (ENS Paris-Saclay) prior to joining Warwick.

Neha's paper contributes to the research framework of quantitative monitoring, which is the analysis of individual executions of systems which yields numerical output (real numbers), rather than binary yes/no. The paper formulates and solves, by an efficient algorithm, a new problem of this kind: computing a real number that characterises to which extent the given execution of a real-time system satisfies its specification expressed in Signal Temporal Logic (STL).