Emilio-Delgado | INTIA

International Summer School on LLM-based Agents for Software Engineering

Tue, 16 Sep 2025 00:00:00 +0000

From September 1 to 3, 2025, i3Lab (Quercus Software Engineering Group and INTIA, University of Extremadura) successfully organized the 1st International Summer School on LLM-Based Agents for Software Engineering (LLMA4SE). The event brought together researchers, professionals and students from academia and industry to explore the rapidly evolving field of LLM-based multi-agent systems and their applications in software engineering.

The opening day featured two keynote talks:

AgentWare in Software Engineering: A Taxonomy of LLM-Based Multi-Agent Systems, by Davide Di Ruscio, which sparked stimulating discussions.
Generative AI Models Running in Your Own Infrastructure, delivered by Jesús M. González Barahona, who offered deep insights on openness in AI models.

Participants also enjoyed a hands-on workshop on TDD with the Help of AI, led by Jesús M. González Barahona and Giovanni Rosa. The day concluded with a cultural highlight: a guided tour of the UNESCO World Heritage city of Cáceres.

The second day offered a balanced combination of research and practical applications. The morning began with three particularly interesting presentations:

Multi-agents for Code Generation, by Jie Zhang, addressing current challenges and approaches.
AI and Software Development: An Industry Paradigm Shift, by Mario Corchero Jiménez and David Naranjo, who provided an industry perspective.
From Workflow-based to Fully Agentic Applications: smolagents and LangGraph, by Antonio García Domínguez, who presented various agent development frameworks.

In the afternoon, Antonio García Domínguez guided participants in a highly practical workshop on developing agentic applications with human-in-the-loop using LangGraph. The day also included two enriching social activities: a guided visit to the Helga de Alvear Contemporary Art Museum and a social dinner at the Gran Hotel Don Manuel.

The final day featured particularly inspiring sessions:

Olivier Perard, on AI in Enterprise Architecture.
Marc Alier, with Safe AI in Education – The LAMB Project, focusing on history, ethics and risks.
Merve Astekin, with Software Maintenance with Agentic LLMs, showcasing sustainable AI projects.

The summer school concluded with an advanced workshop, Multi-Agent LLMs and Knowledge Graphs for Monitoring and Sustaining Software Systems, delivered by Adela N. Videsjorden and Simeon Andersen Tverdal.

With the success of this first edition still fresh, we are already working on the next one. We are fully confident that the second edition of our school will be as engaging and enriching as this inaugural event. Our goal is to maintain the high quality of content and the variety of discussions to establish a great meeting point for the community.

Stay tuned for upcoming announcements!

MetrikaBox: An open framework for experimenting with audio classification

Thu, 28 Aug 2025 00:00:00 +0000

AI-Based System for Assistance in Minimally Invasive Renal Procedures Using Mixed Reality. First Steps

Tue, 29 Jul 2025 00:00:00 +0000

Prompt Once, Segment Everything: Leveraging SAM 2 Potential for Infinite Medical Image Segmentation with a Single Prompt

Mon, 14 Apr 2025 00:00:00 +0000

Advancing precision in medical image segmentation: A performance analysis of loss functions for COVID-19 lung infection segmentation in computed tomography images

Fri, 01 Nov 2024 00:00:00 +0000

Precision in medical segmentation with composite loss functions

Wed, 25 Sep 2024 00:00:00 +0000

We have a new publication! Our latest paper focuses on analyzing how different loss functions impact the accuracy of medical segmentation with deep learning. Specifically, we evaluated three types (AUFL, DSCL and CE) applied to the detection of COVID-19 lung lesions in CT images, using advanced architectures such as TransUNet, ViT and U-Net.

The results are revealing: the AUFL composite loss function stands out for its accuracy and stability, consistently outperforming the others, especially when segmenting small lesions, such as ground-glass opacities (GGOs), so common in this pathology.

This publication consolidates a research line focused on improving models for precision clinical segmentation, with real applications in AI-assisted diagnosis and treatment scenarios. Our goal: to ensure reliable and reproducible segmentations, even in lower-resolution modalities such as CBCT.

Advancing precision in medical image segmentation: A performance analysis of loss functions for COVID-19 lung infection segmentation in computed tomography images is one of the outcomes of musicgenia, a project funded by Grant CPP2021-008491 from MICIU/AEI/10.13039/50100011033 and by the European Union through NextGenerationEU/PRTR.

Interested in understanding why AUFL achieves better results and how these functions compare across different architectures and imaging modalities? Contact us and we’ll explain it to you.

No More Training: SAM's Zero-Shot Transfer Capabilities for Cost-Efficient Medical Image Segmentation

Mon, 15 Jan 2024 00:00:00 +0000

SAM for medical image segmentation

Tue, 23 May 2023 15:00:53 +0200

The source code for this project is located in this repository.

MusicGenia

Fri, 12 May 2023 13:17:36 +0200

MusicGenia’s main goal consists in building a cloud-based platform to offer AI-generated production music as a service for content creators and media, which can be consumed online (live music generation) or offline (recorded music generation). The direct benefits of that platform are: (1) music free of royalties; (2) original music; (3) easy to find music suiting your content; and (4) music on stream (consume as much as you need, pay per seconds not per song).