Speakers

Reimagining Enterprise Services Through Language Models, Agent Automation Middleware, and Quality Control AI

AI-enabled agents are redefining enterprise and service operations through reasoning, autonomy, and self-optimization. The emerging wave of agentic AI systems, powered by large language and action models, promises to extend automation beyond static workflows toward adaptive, goal-driven intelligence. These systems can perceive, reason, and act across domains such as customer service, healthcare, banking, logistics, and telecom, dynamically optimizing performance, integrating with tools and services, and continuously improving through feedback. As these capabilities expand, robust quality control becomes a foundational requirement, ensuring reliability, trustworthiness, compliance, and operational consistency. Quality Control AI including grounded validation, automated safeguards, and feedback-driven correction enables agentic systems to operate safely and predictably at scale. This talk revisits the abstractions and middleware needed for AI-powered services, focusing on the convergence of service integration, agent self-improvement, multi-agent orchestration, and built-in quality control as foundations for the next generation of adaptive, robust, interoperable, and intelligent enterprise services.

Prof. Boualem Benatallah is a full professor of computing at Dublin City University (DCU, Ireland) since January 2022. He has had over 21 years as a research leader and academic at UNSW Sydney (Australia), where he served as senior lecturer, associate professor, professor, and Scientia Professor, before joining DCU. He is a Fellow of the IEEE.

His main research interests are in AI-enabled services, process AI, LLM-powered agents, quality control in crowdsourcing and AI services, service-oriented computing, and business process management. He has published more than 330 refereed papers, including more than 90 journal papers. Most of his papers appeared in very selective and reputable conferences and journals. His research attracted a large amount of competitive research funding through national and international grants from both government and industry. He supervised over 38 research students to completion. He was awarded the prestigious IEEE TCSVC Research Innovation Award for contributions to Model-driven Web Services Composition. He has also won multiple best paper awards at prestigious conferences and received the IBM Faculty Award. With his co-authors, he was recognized by IEEE TSE for one of the most influential papers of the journal’s 3rd decade and contributed a retrospective to its 50th anniversary issue in 2025.

Boualem has been general and PC chair of a number of international conferences and has served as guest editor of several special issues in leading journals. He was a member of the Steering Committee (SC) of the BPM conference. He is a member of the SC of the ICSOC and CoopIS conferences and serves on the editorial boards of several prestigious journals including ACM Transactions on the Web, IEEE Transactions on Services Computing, and ACM Computing Surveys. He was a member of the team (comprising multiple university, government, and industry partners) that founded and constructed the successful bid for the Smart Services CRC (Cooperative Research Centre, Australia). He was also research leader of the Data Curation Foundry research stream at the Data to Decisions CRC (Australia). He is funded investigator at the Insight Research Centre (Ireland).

The Salamandra Family: Advancing Open Foundation Models through Pre-Training and Post-Training recipes.

This talk presents the Salamandra family of open foundation models developed at the Barcelona Supercomputing Center (BSC), focusing on the engineering and research insights behind their design. We will discuss large-scale pre-training on HPC infrastructure, efficient fine-tuning and post-training methods for long-context reasoning, and ongoing work on multimodal and multilingual enhancements. The talk will also cover lessons learned in scaling distributed training, optimizing inference, and ensuring reproducibility in open-source environments. By sharing both successes and challenges, the goal is to contribute to a broader community effort toward transparent and efficient large-model development.

Daniel Tamayo Mela is a Research Engineer in Natural Language Processing at the Barcelona Supercomputing Center. He has worked on the development and large-scale pre-training of both encoder and decoder language models, including contributions to the Salamandra model family and to interpretability research. His work focuses on high-performance training infrastructure, knowledge distillation, and efficient model deployment, with an emphasis on improving the efficiency and scalability of LLM training pipelines.

Rethinking the “evaluation” of AI—powered systems

Abstract: AI-powered services follow a very different dev pattern than traditional ones. Very often, progress is measured as improvement in some quality score rather than new features. This is somewhat similar to what Karpathy calls "software 3.0", as opposed to software 1.0 ("traditional" sw dev) and 2.0 (ML model dev). This is not very different to how we train AI models: we iterate on a system, measure the quality, identify where to improve, and try to make progress by moving in the right direction. Many "human-powered" services behave in the same way. If measures of quality drive improvement cycles, then it is clear that such measures need to be "accurate". In fact, many argue that if we have a good eval, then AI itself can take care of improving the AI-powered service. On the other hand, the more noisy are the measures, the harder it will be for dev cycles to improve, if at all. The "eval" process is therefore foundational in AI services. Software 2.0 teams are aware of this and eval there is relatively more mature than in other software teams, where quality is strongly influenced by "traditional" testing models and methods from software 1.0. The harsh reality is that while eval is foundational and while AI makes the dev process "easier", the eval process is actually really hard. On top of that, and perhaps most importantly, teams without an AI-first culture tend to underestimate how hard that is. This talk will describe how brittle the "eval" process is for AI services and how we can make it more reliable, thereby dramatically accelerating the speed and efficiency at which our services improve. We will see the pitfalls we need to avoid and the tricks we can use to make our "eval" stronger. As we will see, the main "trick" is giving the right name to what we do. Per se, in this talk you will learn no new knowledge: I will simply stitch together many "obvious" concepts to paint what I hope is a useful picture of how to guide "eval" processes.

Fabio Casati is Professor at the University of Trento and principal AI architect for ServiceNow. Fabio focuses on designing, architecting and deploying AI-powered workflows for enterprise customers. On the research side, he is working on evaluations and governance of AI systems and on AI systems that serve needs of individuals and subjective point of views.

AgentScope: A Developer-Centric Framework for Building Scalable Multi-Agent AI Systems

AgentScope is an open-source, Python-based framework designed to simplify the development, orchestration, and deployment of LLM-powered multi-agent applications. Built with a modular and asynchronous architecture, AgentScope enables developers to create complex agentic workflows with high flexibility, reliability, and safety—including support for tool-based interactions, real-time communication, and production-grade sandboxed execution . In this talk, I will demonstrate how AgentScope lowers the barrier to building real-world multi-agent systems, showcase practical use cases, and discuss its role in advancing open-source generative AI ecosystems.

Dr. Farruh is an expert in generative AI with extensive experience combining theory with practice. He has participated in various AI-linked projects and researched foundation models. He has a notable track record with over ten technical/scientific publications.

Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance

In this talk, we present the Falcon-H1 series of large language models, ranging from 0.5B to 34B parameters. Falcon-H1 series introduces an innovative parallel–hybrid attention–state space (SSM) architecture, which combines the performance and precision of attention with the efficiency of SSM. The largest Falcon-H1-34B model rivals the performance of leading open models, such as Qwen-3-32B, upon release. The smallest Falcon-H1-0.5B model remains the top-performing model to date. In the talk, we will walk through the rigorous methodology behind building the Falcon-H1 models and highlight the key innovations. We cover (i) architectural design, including the experimentation behind the parallel–hybrid design with adjustable SSM/attention ratios and associated mixer parallelism for improved efficiency; (ii) training dynamics aspects, from stabilizing large learning rate training of mamba-based model to custom maximal update parametrization (μP); and (iii) data-related insights, including multi-epoch training and anti-curriculum data strategies.

Maksim Velikanov is a Senior Researcher at the Technology Innovation Institute (TII) in Abu Dhabi, where he is part of the Falcon Foundational Models team and has contributed to several generations of Falcon models, including Falcon-Mamba and Falcon-H1. His main focus is on large language model training dynamics, while also being involved in other aspects of the LLM lifecycle from architecture design to data strategy. Concurrently, Maksim is completing a PhD in Applied Mathematics at École Polytechnique, working on optimization and generalization of quadratic problems with power-law spectrum, for which he received Yandex Ilya Segalovich Award for young researchers. His background is in physics, with MSc and BSc work on condensed matter theory, and a gold medal at the 2013 International Physics Olympiad.

Mohamed El Amine Seddik is a Principal Researcher at the AI Research Centre of the Technology Innovation Institute (TII) in Abu Dhabi, where he leads the Post-Training & Reasoning Team. His research focuses on advancing the post-training and reasoning capabilities of large language models through innovative algorithmic and theoretical approaches. Before joining TII, he was a Machine Learning Researcher at the Huawei Research Center in Paris, where he worked on applied AI research. He previously held a postdoctoral position at École Polytechnique, conducting research at the intersection of graph neural networks and large language models. He obtained his Ph.D. from CentraleSupélec, Université Paris-Saclay, where his work focused on random matrix theory and its applications to modern machine learning. His research interests span foundational machine learning, random matrix theory, and both the theoretical and practical frontiers of large language models.

How Spotify builds with and for the open source community

At Spotify, openness is central to how we work. We contribute to the open-source ecosystem through research, publications, code, and platforms such as Backstage, while collaborating closely with the community that helps these projects thrive. As AI becomes increasingly integrated into R&D, our engagement with open source grows even more relevant, shaping how we build, share, and learn together. This talk will highlight how openness, community collaboration, and AI are coming together at Spotify to advance R&D experience and innovation.

Mounia Lalmas is Senior Director of Research at Spotify and Head of Tech Research in Personalisation, advancing state-of-the-art in personalization. Her work spans search, recommender systems, user engagement, and evaluation, with a growing focus on how Generative AI is transforming large-scale retrieval and recommendation. She also holds an honorary professorship at University College London and is a Distinguished Research Fellow at the University of Amsterdam. Previously, she was Director of Research at Yahoo and Professor of Information Retrieval at Queen Mary University of London. An active member of the search and recommender systems communities, she has co-chaired major conferences including SIGIR, WWW, WSDM, and CIKM, and authored over 260 papers. She was nominated for the VentureBeat Women in AI Awards for Research in 2022 and 2023.

MaxText: Achieving Simplicity and Top-Tier Performance for LLM Training with JAX

Training and scaling Large Language Models (Dense LLMs or MoEs) traditionally involves significant engineering complexity, often requiring specialized, low-level optimizations for specific hardware. This presentation introduces MaxText, an open-source, high-performance LLM framework built on JAX. We demonstrate how MaxText serves as a scalable and simple reference implementation for training on Google TPUs and GPUs. By leveraging the power of the JAX and XLA compilers, MaxText achieves state-of-the-art Model FLOPs Utilization (MFU) while remaining easy to modify. We will cover MaxText's architecture and its capabilities for both pre-training and post-training (including Supervised Fine-Tuning and Reinforcement Learning). Attendees will learn how MaxText provides a robust, forkable foundation for ambitious research and production projects, supporting a wide range of models like Llama, Gemma, and Mistral, DeepSeek out of the box.

Newfel Harrat is an engineering director at Google Cloud, where he oversees the Open Source Software AI/ML Software Stack. In this role, he focuses on AI/ML Frameworks designed to accelerate both model training and inference. Prior to his current position, Harrat led the Gemini Code Assist team at Google, where he was instrumental in developing AI-powered code generation tools that significantly enhanced developer productivity.

Newfel is dedicated to utilizing AI to enable developers and streamline their product development. He is also a fervent supporter of diversity and inclusion within the technology sector.

Newfel's career before Google Cloud included leading software development for Google Chrome OS.

Before joining Google, he led an engineering team focused on AI-driven incident prevention software for automotive and large enterprise fleets.

Newfel also held senior leadership positions at several Fortune 100 companies (Intel, Qualcomm, ADI), where he oversaw global software development teams.

He earned both a Bachelor of Science and a Master of Science degree in Electrical Engineering from the University of Massachusetts, specializing in Neural Network and Stochastic Signal Processing.

Open-Source Data & Tools for AI Model Development

Open-source large language models have experienced explosive growth over the last few years, matching and often surpassing the performance of closed-source Frontier AI models on various downstream tasks. Underlying this growth is the ecosystem that has rapidly evolved around open-source data and training software stacks, covering all phases of AI model development, ranging from real & synthetic data preparation to model training, fine-tuning, and benchmarking. In this talk, we will describe a suite of data preparation tools and recipes that IBM Research has developed for the training of the Granite model series and contributed to the open-source community.

Petros Zerfos, Ph.D., is Principal Research Scientist & Manager at IBM Research, Yorktown Heights, NY. He is the chief architect of the large-scale data engineering for IBM’s Granite AI Models and the platform for Generative AI model development and customization. He received his PhD and MSc in Computer Science from the University of California, Los Angeles (UCLA), CA, USA, and his M.Eng. in Electrical & Computer Engineering from the National Technical University of Athens, Greece. He has (co-) authored 65+ scientific papers published in top-tier journals & technical conferences in the areas of artificial intelligence, Big Data platforms, time series analysis, cloud and systems management, and has been designated IBM Master Inventor, having filed 60+ patents worldwide with 45 granted to date, while technologies that he has developed with his team have been productized in 20+ IBM products and services. Petros is also serving as technical advisor to the “Pharos” Greek AI Factory strategic initiative, providing strategic and technical guidance on the development of the EU AI Factory in Greece.

EuroLLM: A Family of Encoders and Decoders for Europe

In this talk, I will present EuroLLM, our family of multimodal encoder and decoder models developed to strengthen Europe’s multilingual and culturally aligned AI ecosystem. By emphasizing linguistic diversity, open collaboration, and data sovereignty, EuroLLM aims to advance natural language understanding and generation across European languages and domains.

I am an Associate Professor at CentraleSupélec and Co-founder / Chief Science Officer at Equall.ai, a legaltech startup where we developed the first LLM specialized for law and AI-driven workflows, enabling lawyers to work faster and more efficiently. My research focuses on Natural Language Processing and Large Language Models (LLMs), including large-scale training (EuroBERT, EuroLLM, SaulLM, CroissantLLM, TowerLLM), LLM efficiency and adaptation, and the development of novel evaluation metrics recognized at leading venues (ACL, EMNLP, NeurIPS, ICML, AAAI).