Ander Biguri (University of Cambridge, United Kingdom)
Research interests: GPU-accelerated tomographic reconstruction, data-driven methods.
Alexandre Bousse (University of Western Brittany, France)
Alexandre Bousse is a researcher at the French National Institute of Health and Medical Research (Inserm) and part of the Laboratoire de Traitement de l'Information Médicale (LaTIM),University of Western Brittany (UBO) since 2018. He received a PhD in Signal and Image Processing in December 2008 from the University of Rennes 1, and the Research Habilitation (HDR) in 2019 from UBO. From 2009 to 2018, he was a postdoctoral researcher at University College London (UCL), where he was involved in several image reconstruction projects, including two EPSRC grants, one FP7 grant, and funding from GE Healthcare. He later served as Principal Investigator of the ANR project MultiRecon. His research focuses on medical image reconstruction, with particular emphasis on CT and PET using model-based iterative reconstruction (MBIR) and AI-based techniques, including deep models. His work lies at the interface between theoretical developments and clinical translation. He also serves as an associate editor for IEEE Transactions on Radiation and Plasma Medical Sciences (TRPMS).
Research interests: model-based iterative reconstruction, machine learning techniques for medical image processing.
Sascha Diefenbacher (Institute for Theoretical Physics, Heidelberg University, Germany)
Sascha Diefenbacher is a researcher at the Institute for Theoretical Physics at Heidelberg University, where she leads a research group on generative AI for LHC physics. Her work lies at the intersection of machine learning and high-energy physics, with a particular focus on generative models, surrogate simulations, and data-driven methods for particle-physics analyses. She received her PhD from the University of Hamburg working on generative modeling of particle-physics data. Her research has covered fast calorimeter simulation, generative amplification of simulated event samples, machine-learning-based unfolding, anomaly detection in experimental data, and applications of modern generative models to detector and analysis workflows.
Research interests: generative machine learning, fast detector simulation, unfolding, LHC physics, surrogate models, uncertainty quantification.
Beatrix Hiesmayr (Quantum Particle Workgroup, University of Vienna, Austria / IT:U, Austria)
Beatrix Hiesmayr is a researcher and lecturer at the University of Vienna and head of the Quantum Particle Workgroup. Her research focuses on quantum phenomena at high and low energies, from very mathematical issues to experimental and technical ones. She has been focusing e.g. on decay of positronium as a biological marker, on classification and detection of entanglement and on testing collapse models in quantum theory.
Research interests: quantum mechanics, quantum information, positronium, medical physics, quantum entanglement detection.
Yufang Hou (IT:U, Austria)
Yufang Hou is a university professor at IT:U – Interdisciplinary Transformation University Austria. At IT:U, she leads the NLP group with a strong focus on large language model (LLM) governance (with a particular focus on content veracity), computational argumentation, fact-checking, knowledge and reasoning, and human-centred multimodal NLP applications in education, science, and health. Yufang has served as a best paper award co-chair at IJCNLP-AACL 2025, a Visa Chair for EACL 2024, and as a Senior Area Chair for EMNLP (2022 – 2024) and NAACL (2024 – 2025). In the past, she has co-organized the 8th Workshop on Argument Mining, the SustaiNLP workshop series from 2021 to 2023, the 1st Workshop on Argumentation Knowledge Graphs, and the first Quantitative Summarization – Key Point Analysis Shared Task.
Research interests: natural language processing, large language models, computational argumentation and fact-checking.
Kamila Kowalska (National Centre for Nuclear Research, Poland)
Professor at the National Center for Nuclear Research, specialized in high energy and elementary particle physics. Her recent research interest focuses on the intersection between particle physics and quantum information theory. Much of her current work investigates quantum entanglement within ultra-relativistic scattering processes, exploring how fundamental quantum properties can be probed at high-energy accelerators like the Large Hadron Collider.
Research interests: particle physics phenomenology, quantum entanglement in scattering processes
Tomasz Małkiewicz (Nordic e-Infrastructure Collaboration, Norway)
Tomasz Małkiewicz is a Management Group member of CSC – IT Center for Science Ltd. of Espoo, Finland, and the Director of Nordic e-Infrastructure Collaboration (NeIC). Current NeIC projects consolidate Nordic and Baltic competence sharing in the fields of quantum computing, AI, research software engineering, corporate data databases and resource sharing in partnership with national e-Infrastructure providers.
Research interests: supercomputers, physics with supercomputers, e-infrastructure
Agnieszka Pollo (National Centre for Nuclear Research, Poland)
Professor and Scientific Director of the National Center for Nuclear Research, and also a professor at the Faculty of Physics, Astronomy, and Applied Computer Science of the Jagiellonian University. Involved in the creation of VIMOS VLT Deep Survey (VVDS), a comprehensive deep galaxy spectroscopic redshift survey, later the head of the Polish node of the VIMOS Public Extragalactic Redshift Survey (VIPERS). Currently, leading of the Polish collaboration analyzing data from the Legacy Survey of Space and Time (LSST) based on observations the Vera Rubin Observatory in Chile which started its operations last year. In parallel, the head of the Polish consortium of the space project POLAR-2 which is planned to be launched in 2028, to start measurements of polarization of gamma-ray bursts.
Research interests: observational cosmology, extra-galactic astrophysics, statistical methods, machine learning, popularization of science.
Tobias C. Sutter (Quantum Particle Workgroup, University of Vienna, Austria)
Tobias C. Sutter is a PhD student in the Quantum Particle Workgroup at the University of Vienna. His research encompasses various aspects of quantum information theory and quantum-for-quantum machine learning. This includes finding ways to exploit quantum features such as the geometry of quantum states to optimize dissipative quantum neural networks, as well as characterizing mixed quantum states with respect to their entanglement structure and distillability.
Research interests: quantum information theory, quantum machine learning, quantum neural networks.
Gioacchino Vino (INFN, Italy)
Gioacchino Vino has been a technologist at the Italian National Institute of Nuclear Physics (INFN), Bari division, since 2020. With a background in electronic engineering, he specialized in high-performance scientific computing, combining a solid understanding of hardware (GPUs, FPGAs, computing architectures) with the design of software platforms for Machine Learning and Deep Learning in support of research in physics and medicine. He designs, maintains, and coordinates large-scale cloud-native MLOps platforms across INFN — within the AI_INFN project, the PaaS Orchestrator, Central Services, and the ReCaS-Bari datacenter — as well as within the national ICSC centre (National Research Centre for High Performance Computing, Big Data and Quantum Computing), built on technologies such as Kubernetes, Kueue, JupyterHub, MLflow, Ceph, and NVIDIA GPUs. At the ReCaS-Bari datacenter, he manages a platform that leverages next-generation bare-metal architectures (NVIDIA H100 accelerators, Ceph storage) to optimize the training of complex models. Additionally, he brings extensive experience in designing and coordinating Prometheus-based monitoring platforms and actively contributes to the development of the INFN PaaS Orchestrator, utilizing Apache Kafka.
Research interests: scientific computing platforms, container orchestration, Machine Learning infrastructures, GPU-accelerated computing, distributed systems.
Pietro Vischia (ICTEA, Universidad de Oviedo, Spain)
Ramón y Cajal senior researcher at the Universidad de Oviedo and ICTEA (Spain), Adjunct Professor at IITM. Graduated in 2016 from IST. He is the coordinator of the MODE (Machine-Learning-Optimized Design of Experiments) Collaboration, and the Machine Learning Coordinator of the CMS Experiment at CERN. Specialist in Machine Learning applied to High Energy Physics. Researcher in high-dimensional spaces via gradient descent, eventually powered by quantum algorithms, and on the extension of machine learning methods to realistic neurons with spiking networks, to be then implemented in neuromorphic hardware devices. Within CMS, he focuses on plugging inductive bias in machine learning algorithms for standard model Higgs physics (including the 2018 observation of the ttH process) and beyond-the-standard-model new physics searches in the Top, Higgs, and vector boson sectors.
Research interests: machine learning for high-energy physics, quantum algorithms, spiking neural networks.
The list of invited speakers in under construction and regularly updated.
