In a new series of interviews, we're meeting some of the PhD students that were selected to take part in the Doctoral Consortium at the European Conference on Artificial Intelligence (ECAI 2025) . During the conference in Bologna, we caught up with Mario Mirabile who is studying for his PhD in trustworthy AI and multi-agent systems at the University of Santiago de Compostela and is a Research Fellow in human-AI interaction at the University of Bologna. Mario, along with co-authors Frida Hartman and Michele Dusi, was also the winner of the ECAI-2025 Diversity & Inclusion Competition, for work entitled . This award was presented at the closing ceremony of the conference. Could you start by giving us an introduction to the topic you are working on?

Former Harvard president Larry Summers has said he will step back from public commitments after his emails with disgraced financier Jeffrey Epstein were made public. I am deeply ashamed of my actions and recognise the pain they have caused, he said in a statement to CBS News, the BBC's US partner. I take full responsibility for my misguided decision to continue communicating with Mr Epstein. Emails released by Congress last week show Summers, a former US treasury secretary, communicated with Epstein until the day before the paedophile's 2019 arrest for sex trafficking minors. On Tuesday, House members are expected to vote on releasing all files related to the late sex offender.

The UK is to use artificial intelligence (AI) to tackle the rising numbers of infections that have become resistant to treatment. The project - a collaboration between the Fleming Initiative and the pharmaceutical company GSK - is a battle between superbugs and supercomputers. It aims to speed up the discovery of fresh antibiotics and deliver new ways of killing other threats, including deadly fungal infections. Overusing antibiotics drives bacteria to evolve resistance to infections, which means new drugs are a priority. Drug-resistant infections are a growing problem - one known as the silent pandemic.

Don't blindly trust what AI tells you, says Google's Sundar Pichai People should not blindly trust everything AI tools tell them, the boss of Google's parent company Alphabet told the BBC. In an exclusive interview, chief executive Sundar Pichai said that AI models are prone to errors and urged people to use them alongside other tools. Mr Pichai said it highlighted the importance of having a rich information ecosystem, rather than solely relying on AI technology. This is why people also use Google search, and we have other products that are more grounded in providing accurate information. While AI tools were helpful if you want to creatively write something, Mr Pichai said people have to learn to use these tools for what they're good at, and not blindly trust everything they say.

Google boss warns'no company is going to be immune' if AI bubble bursts Every company would be affected if the AI bubble were to burst, the head of Google's parent firm Alphabet has told the BBC. Speaking exclusively to BBC News, Sundar Pichai said while the growth of artificial intelligence (AI) investment had been an extraordinary moment, there was some irrationality in the current AI boom. It comes amid fears in Silicon Valley and beyond of a bubble as the value of AI tech companies has soared in recent months and companies spend big on the burgeoning industry. Asked whether Google would be immune to the impact of the AI bubble bursting, Mr Pichai said the tech giant could weather that potential storm, but also issued a warning. I think no company is going to be immune, including us, he said.

Is the fall of Pokrovsk inevitable? Is Trump losing patience with Putin? A Russian missile strike on the eastern Ukrainian city of Balakliia killed three people and wounded 10, including three children, a regional military official in the Kharkiv region said on Telegram on Monday. At least two people were killed and three were injured in Russian shelling of the Nikopol district in Ukraine's Dnipropetrovsk region, Vladyslav Haivanenko, the acting head of the Dnipropetrovsk Regional Military Administration, wrote on Facebook. Russian troops captured three villages across three Ukrainian regions, the RIA news agency cited the Russian Ministry of Defence as saying on Monday.

President Donald Trump has announced US embassies will give visa appointment priority to travellers with tickets to the 2026 World Cup. The Fifa Prioritised Appointment Scheduling System (Pass) will allow World Cup ticket-holders with long wait times to opt with Fifa for a prioritised interview, Trump said at the White House on Monday. Ticket-holders for the tournament - set for next June and July in the US, Canada and Mexico - will not be automatically granted a tourist visa, said Secretary of State Marco Rubio. But foreign nationals with tickets to World Cup football matches could get an interview at an embassy or consulate within six to eight weeks of applying, Rubio said. Your ticket is not a visa; it doesn't guarantee admission to the US, Rubio said, also at the White House on Monday.

Irregularly sampled time series (ISTS), characterized by non-uniform time intervals with natural missingness, are prevalent in real-world applications. Existing approaches for ISTS modeling primarily rely on observed values to impute unobserved ones or infer latent dynamics. However, these methods overlook a critical source of learning signal: the reconstruction error inherently produced during model training. Such error implicitly reflects how well a model captures the underlying data structure and can serve as an informative proxy for unobserved values. To exploit this insight, we propose iTimER, a simple yet effective self-supervised pre-training framework for ISTS representation learning. iTimER models the distribution of reconstruction errors over observed values and generates pseudo-observations for unobserved timestamps through a mixup strategy between sampled errors and the last available observations. This transforms unobserved timestamps into noise-aware training targets, enabling meaningful reconstruction signals. A Wasserstein metric aligns reconstruction error distributions between observed and pseudo-observed regions, while a contrastive learning objective enhances the discriminability of learned representations. Extensive experiments on classification, interpolation, and forecasting tasks demonstrate that iTimER consistently outperforms state-of-the-art methods under the ISTS setting.

Retinal image quality assessment (RIQA) supports computer-aided diagnosis of eye diseases. However, most tools classify only overall image quality, without indicating acquisition defects to guide recapture. This gap is mainly due to the high cost of detailed annotations. In this paper, we aim to mitigate this limitation by introducing a hybrid semi-supervised learning approach that combines manual labels for overall quality with pseudo-labels of quality details within a multi-task framework. Our objective is to obtain more interpretable RIQA models without requiring extensive manual labeling. Pseudo-labels are generated by a Teacher model trained on a small dataset and then used to fine-tune a pre-trained model in a multi-task setting. Using a ResNet-18 backbone, we show that these weak annotations improve quality assessment over single-task baselines (F1: 0.875 vs. 0.863 on EyeQ, and 0.778 vs. 0.763 on DeepDRiD), matching or surpassing existing methods. The multi-task model achieved performance statistically comparable to the Teacher for most detail prediction tasks (p > 0.05). In a newly annotated EyeQ subset released with this paper, our model performed similarly to experts, suggesting that pseudo-label noise aligns with expert variability. Our main finding is that the proposed semi-supervised approach not only improves overall quality assessment but also provides interpretable feedback on capture conditions (illumination, clarity, contrast). This enhances interpretability at no extra manual labeling cost and offers clinically actionable outputs to guide image recapture.

Early diagnosis of breast cancer is crucial, enabling the establishment of appropriate treatment plans and markedly enhancing patient prognosis. While direct magnetic resonance imaging-guided biopsy demonstrates promising performance in detecting cancer lesions, its practical application is limited by prolonged procedure times and high costs. To overcome these issues, an indirect MRI-guided biopsy that allows the procedure to be performed outside of the MRI room has been proposed, but it still faces challenges in creating an accurate real-time deformable breast model. In our study, we tackled this issue by developing a graph neural network (GNN)-based model capable of accurately predicting deformed breast cancer sites in real time during biopsy procedures. An individual-specific finite element (FE) model was developed by incorporating magnetic resonance (MR) image-derived structural information of the breast and tumor to simulate deformation behaviors. A GNN model was then employed, designed to process surface displacement and distance-based graph data, enabling accurate prediction of overall tissue displacement, including the deformation of the tumor region. The model was validated using phantom and real patient datasets, achieving an accuracy within 0.2 millimeters (mm) for cancer node displacement (RMSE) and a dice similarity coefficient (DSC) of 0.977 for spatial overlap with actual cancerous regions. Additionally, the model enabled real-time inference and achieved a speed-up of over 4,000 times in computational cost compared to conventional FE simulations. The proposed deformation-aware GNN model offers a promising solution for real-time tumor displacement prediction in breast biopsy, with high accuracy and real-time capability. Its integration with clinical procedures could significantly enhance the precision and efficiency of breast cancer diagnosis.