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Academia-Industry Matching Event (AIME25)

→ Europe/Budapest
HUN-REN Headquarters

HUN-REN Headquarters

1054 Budapest Alkotmány utca 29.
Description

HUN-REN Wigner RCP, together with the HEPTECH Network, is organizing the next

Academia-Industry Matching Event (AIME25)

in the topics

Artificial Intelligence, HPC, and Quantum Computing

The aim of this event is to bring together Academic researchers and Industry experts to share ideas, potential applications, and foster collaborations in the field of theoretical and practical aspects of Artificial Intelligence, High-Performance Computing (HPC), and Quantum Computing.

Earlier events: 2024, 2023, 2022, 2021, 2019, 2018

 

Patron

  • Roland Jakab (HUN-REN, CEO)

 

Confirmed keynote speakers

  • Roland Jakab (HUN-REN, CEO)
  • Örs Legeza (HUN-REN Wigner, Dynaflex)
  • Pikethy Árpád (IBM Hungary)
  • Gábor Vattay (ELTE)
  • Tamás Kozsik (ELTE)

 

Contributions to the conference are welcome.

Abstracts must contain a title shorter than 100 characters, the name and affiliation of the presenter and coauthors, and a maximum of 4000 characters of body text. Images should be sent separately from the text as the abstract will be reprocessed for display on the website.

The registration fee for non-students is 100 EUR (40,000 HUF), and for students, 20 EUR (8,000 HUF). For more information, click on the >>link<<.

Talk submission deadline: November 15, 2025

The Organizing Committee:
Gergely Gábor Barnaföldi
Gábor Bíró
Balázs Kacskovics
Orsolya Kálmán
Péter József Lévai
András Telcs

 

Support
Participants
40 View full list
  • Thursday 27 November
    • Opening: Welcome & Opening
      Convener: Peter Van (Wigner FK, RMI, Elméleti Fizika Osztály)
      • 1
        Welocme and Opening
    • Artificial Intelligence
      Convener: Peter Van (Wigner FK, RMI, Elméleti Fizika Osztály)
    • SciComp Presentation
      Convener: Andras Telcs
    • 10:45
      Coffee Break
    • Artificial Intelligence
      Convener: Andras Telcs
      • 5
        MI in Biology: Machine Learning and Protein Language Models to Improve Knowledge and Therapy
        Speaker: Tamas Hegedus (Semmelweis University)
        hegedus-aime-2025.pptx
      • 6
        RenyiAI focusing on patient journey analysis

        After a short introduction of the artifial intelliogence research group of the HUN-REN Alfréd Rényi Institute of Mathematics, including application of AI to theoretical (“Erdős”) problems, applications in energy management and applications to modernize the processing of archival documents using artificial intelligence we will focus on our flagship AI application project, on the patient journey analysis.
        High-fidelity patient journeys are essential for advancing predictive modeling in healthcare. In Hungary, large volumes of electronic health records are available from diverse sources, including administrative and clinical data. As a first step, we focus on leveraging structured administrative records to build temporally ordered patient histories suitable for predictive modeling. In parallel, we are developing tools and workflows to extract structured representations from unstructured patient documentation, with the long-term goal of integrating these modalities into richer, unified patient trajectories, enabling their use in downstream predictive tasks.
        the reserach and development project aims to utilize comprehensive patient journey datasets to enable the development of sequence-based predictive models for healthcare. Specifically, we focus on applying state-of-the-art transformer architectures to predict clinically relevant future health events, such as mortality or hospitalization. We also aim to investigate how such models can be adapted to the peculiarities of local healthcare data and explore their capacity to generate insights in real-world clinical settings.
        We constructed structured patient timelines from health data, and used these datasets to train a variety of machine learning models, including gradient boosting machines and deep learning architectures. Particular emphasis was placed on sequential models, such as transformer-based architectures, to capture the temporal dynamics of patient history. We evaluated the models’ performance using standard classification metrics and applied model interpretation techniques to understand feature relevance and temporal dependencies.
        Neural models consistently outperformed classical approaches in key prediction tasks, such as estimating six-month mortality risk. These models learned from complex, time-aware patient data and achieved high predictive accuracy. Furthermore, our interpretability analyses reveal clinically plausible patterns, such as the predictive role of recent diagnoses supporting the trustworthiness and usability of the models.
        .

        Speaker: Dezső Miklós (HUN-REN Alfréd Rényi Institute of Mathematics)
        AIME_miklosdezso.pptx
      • 7
        Representing Reality

        Despite the remarkable achievements of contemporary AI, it is increasingly evident that current architectures suffer from fundamental limitations. One of the most critical weaknesses lies in the representation—indeed, the modelling—of actual reality. A meaningful reality model requires a hierarchical system of partially independent features that together provide a coherent coordination of the environment.

        In this talk, I will outline the logic of reality representation and propose principles for constructing effective environmental models. I will discuss the LLT/ALT method for extracting relevant features from continuous data, as well as the use of RDF-style triplet representations for structuring discrete information.

        Speaker: Antal Jakovac (Wigner RCP, Department of Computational Sciences)
        AIME2025.pdf
        Jakovac_AIME2025.odp
    • 13:00
      Lunch
    • Quantum Computing and Technology
      Convener: Gergely Barnafoldi (Wigner RCP RMI of the Hungarian Academy of Sciences)
    • 16:00
      Coffee Break
    • Quantum Computing and Technology
      Convener: Orsolya Kalman (MTA Wigner FK)
  • Friday 28 November
    • High-performance Computing
      Convener: Gabor Biro (MTA Wigner FK)
      • 15
        Hungary to reach AI Exascale

        Hungary is building new supercomputer to be developed with EuroHPC collaboration, and implemented by DKF Ltd. In the presentation, I will present the updated schedule, and the importance of the machine in the current HPC-AI ecosystem. This hardware will be ready to satisfy requirements for AI and quantum simulation workloads.
        DKF is already preparing its HPC Platform ecosystem to be fully compatible with EuroHPC Hosting standards.
        I will present AI ecosystem improvements supporting multi-node runs to be able to support jobs to run on exascale, introducing novel AI development toolset.
        While our existing Komondor machine is already running full throttle, there is a need to improve efficiency-increasing measures to further improve resource utilization.

        Speaker: Zoltán Kiss (DKF Kft.)
        AIIME_HPC_202511_KZ.pptx
      • 16
        Developments for HPC Levente Supercomputer
        Speaker: Ádám Pintér
        AIME_konferencia_2025_Pintér_Ádám.v2.pdf
      • 17
        Hungarian AI Factory Antenna
        Speaker: Róbert Lovas (SZTAKI)
        AIME_HunAIFA-20251107.pptx
    • 10:30
      Coffee Break
    • High-performance Computing
      Convener: Balázs Kacskovics (HUN-REN Wigner Research Centre for Physics)
      • 18
        Designing topologial qubit with HPC
        Speaker: Balazs Ujfalussy (MTA WIGNER FK)
        AIME_bu_majorana.pptx
      • 19
        Recent advances in tensor network state methods: a journey from mathematical aspects towards industrial perspectives

        A brief overview of recent advances in tensor network state (TNS) methods are presented that have the potential to broaden their scope of application radically for strongly correlated quantum many body systems. Novel mathematical models for hybrid multiNode-multiGPU parallelization on high-performance computing (HPC) infrastructures will be discussed. Scaling analysis on NVIDIA DGX-A100 and DXG-H100 platforms reaching quarter petaflops performance on a single node will be presented. We also report cutting-edge performance results via mixed precision ab initio Density Matrix Renormalization Group (DMRG) electronic structure calculations, adapted for state-of-the-art NVIDIA Blackwell technology, utilizing the Ozaki scheme for emulating FP64 arithmetic through the use of fixed-point compute resources. Finally, we showcase recent results obtained on IBM superconducting quantum processor with up to 144 qubits, together with classical validation, using state-of-the-art tensor network simulations via a novel Basis Update Galerkin (BUG) method, establishing agreement between quantum and classical approaches. We close our presentation discussing future possibilities via utilization of Blackwell NVL72 technology in tree-like TNS calculations opening new research directions in material sciences and beyond.

        Speaker: Örs Legeza (Wigner FK)
        Legeza_aime2025_v01.pdf
      • 20
        Autonomous Networks - Knowledge bases in action

        The next chapter in the evolution of mobile networks is achieving higher and higher levels of autonomy, as autonomous networks decrease maintenance efforts and increase reliability. In this talk, we are going to explore what increased reliability really means and what key capabilities it depends on. We are also diving into the technical challenges in realizing these capabilities, with a focus on the role that knowledge bases play. We finish with the current state and potential next steps.

        Speaker: Adam Zlatniczki (Ericsson)
        Autonomous Networks - Knowledge bases in action.pptx
    • Closing Remarks
    • 12:35
      Lunch
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