The Blavatnik Awards for Young Scientists today announced the Finalists for the 2026 Blavatnik Awards for Young Scientists in the United Kingdom. The Awards recognise scientific advances by UK researchers across Life Sciences, Chemical Sciences, and Physical Sciences & Engineering. Now in its ninth year, each Blavatnik Awards Laureate will receive an unrestricted £100,000 (US$135,000) prize, while the remaining six Finalists will be awarded £30,000 (US$40,400) each. 

This year’s Finalists include:

 

Chemical Sciences Finalists

Michael J. Booth, PhD – University College London (UCL)

Mathew H. Horrocks, PhD – The University of Edinburgh

Maxie M. Roessler, DPhil – Imperial College London

 

Life Sciences Finalists

Nicholas R. Casewell, PhD – Liverpool School of Tropical Medicine

Thi Hoang Duong (Kelly) Nguyen, PhD – MRC Laboratory of Molecular Biology

Pontus Skoglund, PhD – The Francis Crick Institute

 

Physical Sciences & Engineering Finalists

Radha Boya, PhD – The University of Manchester

Paola Pinilla, PhD – University College London (UCL)

Iestyn Woolway, PhD – Bangor University

 

Argument mining (AM), aiming to extract and identify argumentative structures from natural language text, has become an established field in the NLP community. The main challenge in this task comes three-fold: the insufficiency of contextual information on targets, cross-domain adaptation across varying targets, and implicit argumentative information within the argument. Current approaches primarily address the first two challenges by improving the integration of target-related semantic information with arguments, while there has been little work on modeling all three aspects.

Estimating the number of triangles in a graph is a fundamental problem and has found applications in many fields. This problem has been widely studied in the context of graph stream processing. However, most of these algorithms are not robust or are limited to unweighted graphs. The reason why they do not robust is because most algorithms assume that the entire stream is predetermined before algorithm execution, rendering them vulnerable to adaptive inputs.

Real-world data typically exhibits long-tailed class distribution and contains label noise. Previous long-tail learning methods overlooked the prevalence of noisy labels in training. Moreover, the commonly used small-loss noisy label detection criterion fails in long-tail data.

Researchers at Qingdao University have developed a novel algorithm, Microbiome Elastic Feature Extraction (MEFE), that significantly improves the identification of microbiome biomarkers by incorporating phylogenetic, taxonomic, and functional relationships among microbes. This advancement addresses longstanding challenges in microbiome research, such as data sparsity and sequencing errors, potentially leading to more accurate disease diagnostics and personalized medicine. The findings were published on 15 January 2026 in Frontiers of Computer Science.

 

Professor Jongmin Kim’s research team at POSTECH develops ‘SUPER’ platform, significantly enhancing the performance and stability of gene regulatory devices.

A research team from Soochow University has developed a novel artificial intelligence (AI) method to improve emotion cause extraction in conversations, enabling machines to better understand the nuanced triggers behind human emotions. Published in Frontiers of Computer Science, this breakthrough addresses key challenges in identifying fine-grained emotional causes within complex dialogues, offering potential applications in mental health support, customer service chatbots, and human-computer interaction systems.

Whether a smartphone battery lasts longer or a new drug can be developed to treat incurable diseases depends on how stably the atoms constituting the material are bonded. The core of 'molecular design' lies in finding how to arrange these countless atoms to form the most stable molecule. Until now, this process has been as difficult as finding the lowest valley in a massive mountain range, requiring immense time and costs. Researchers at KAIST have developed a new technology that uses artificial intelligence to solve this process quickly and accurately.

In this work, the authors systematically advance macro-micro integration with feedback (MMIF) as a transformative paradigm for analyzing urban mega-mobility systems, synthesizing the state-of-the-art developments in typical constituent subsystems under this unified perspective. The MMIF paradigm bridges the gap between theoretical abstraction and empirical practice, contributing to scientifically sound urban development by harmonizing emergent patterns with granular behavioral dynamics. Building upon this paradigm, we investigate the key methods and technologies empowered by artificial intelligence (AI) that enable MMIF, and critically analyze the enduring challenges and prospective research directions. As urban mobility systems increasingly serve as testbeds for complexity science, the MMIF paradigm using AI promises to reshape interdisciplinary collaboration, offering a blueprint for building intelligent, adaptive, and human-centric cities.