HDDA-XV CONFERENCEHDDA-XV CONFERENCEHDDA-XV CONFERENCEHDDA-XV CONFERENCE
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  • RESEARCH CENTERS
  • CORE FACILITIES
    • Advanced Microscopy
    • Cell Culture
    • Molecular Cell Biology
    • Proteomics
    • Drug Discovery
    • Bioinformatics
    • Biomaterials
    • Electrophysiology and Behavior
    • Cognitive Neuroscience
    • Animal House
  • PEOPLE
    • Administration
    • Group Leader
    • Transition Scientist
    • Early Career Researchers
    • Students
  • EVENTS
    • Event Calendar
    • Critical Mind
    • SABITALKS
    • InFocus
    • CROSSTALKS
    • MODAS WS
    • SABITA Podcast
    • Social
  • ABOUT US
    • Our Mission
    • Gender Equality Policy

HDDA-XV CONFERENCE

We are pleased to invite you to join the 15th Conference on High Dimensional Data Analysis (HDDA-XV), which will be held at Istanbul Medipol University, Istanbul, Türkiye, in 12-15 August 2026. This workshop offers a premier forum where theory, methodology, and applications of high-dimensional statistics and data analysis converge. With the explosion of complex, large-scale data (for example from genomics, finance, environmental monitoring, social networks, and industrial sensing), the need for robust, scalable inference and computational methods has never been greater. The conference theme, ‘From data to actionable insight’, reflects the data-driven dynamics of the field and highlights current perspectives in this domain.

You are encouraged to submit contributed talks or posters, as well as propose special sessions focused on emerging and interdisciplinary themes. HDDA-XV will be offered in a hybrid format to allow both in-person and remote participation, fostering global accessibility. The conference is organized by the Multi-Omics Design and Analysis Studio (MODAS), which is part of the SABITA Institute at Istanbul Medipol University. We look forward to gathering an international community of researchers, practitioners and early-career scholars to exchange ideas, initiate collaborations and chart future directions in high-dimensional data analysis.

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