{"id":13209,"date":"2026-06-23T14:38:00","date_gmt":"2026-06-23T12:38:00","guid":{"rendered":"https:\/\/www.fgw-brandenburg.de\/?page_id=13209"},"modified":"2026-07-01T11:34:59","modified_gmt":"2026-07-01T09:34:59","slug":"bioanalytics-and-data-science","status":"publish","type":"page","link":"https:\/\/www.fgw-brandenburg.de\/en\/members\/professorial-chairs\/bioanalytics-and-data-science\/","title":{"rendered":"Bioanalytics and Data Science"},"content":{"rendered":"\n<div class=\"wp-block-columns\">\n<div class=\"wp-block-column\" style=\"flex-basis:55%\">\n<div style=\"height:120px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>The Chair of Bioanalytics and Data Science at the University of Potsdam (joint appointment with Fraunhofer IZI-BB) develops robust methods for pattern recognition and feature extraction from biomedical datasets.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Our research focuses on unsupervised learning, explainable AI, data fusion, as well as mathematical modeling and optimization. Methodological work is directly applied to bioanalytical applications such as point-of-care diagnostics or predictive biomarker identification.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column\" style=\"flex-basis:5%\"><\/div>\n\n\n\n<div class=\"wp-block-column\" style=\"flex-basis:40%\"><div class=\"about_person\">\n  <div class=\"about_person__img\"><img decoding=\"async\" src=\"https:\/\/www.fgw-brandenburg.de\/wp-content\/uploads\/2026\/06\/max_pfeffer.jpg\" alt=\"Prof. Max Pfeffer\" \/><a href=\"https:\/\/www.fgw-brandenburg.de\/en\/person\/prof-max-pfeffer\/\"><\/a><\/div>\n  <div class=\"about_person-content\">\n    <div class=\"about_person__name\">Prof. Max Pfeffer<\/div>\n    <div class=\"about_person__position\">Head of the Professorship Bioanalytics and Data Science<\/div>\n    <div class=\"about_peersont__address\">\n    <\/div>\n<a href=\"tel:+49331977230103\" class=\"about_person__phone\">phone: +49 (0) 331 977 230103<\/a><a href=\"mailto: max.pfeffer@fgw-brandenburg.de\" class=\"about_person__email\"> max.pfeffer[at]fgw-brandenburg.de<\/a>  <\/div>\n<\/div>\n\n\n\n<p><\/p>\n\n\n\n<p>Location 1: University of Potsdam, Am M\u00fchlenberg 9, Haus 62 (H-Lab), <br>Room 02.62.1.04<br>14476 Potsdam \u2013 Golm<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p>Location 2: Fraunhofer IZI-BB, Am M\u00fchlenberg 13, 14476 Potsdam \u2013 Golm<\/p>\n<\/div>\n<\/div>\n\n\n\n<div style=\"height:60px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-getwid-tabs\" data-active-tab=\"0\"><ul class=\"wp-block-getwid-tabs__nav-links\"><\/ul>\n<div class=\"wp-block-getwid-tabs__nav-link\"><span class=\"wp-block-getwid-tabs__title-wrapper\"><a href=\"#\"><span class=\"wp-block-getwid-tabs__title\">Research<\/span><\/a><\/span><\/div><div class=\"wp-block-getwid-tabs__tab-content-wrapper\"><div class=\"wp-block-getwid-tabs__tab-content\">\n<h2 class=\"wp-block-heading\" id=\"forschungsschwerpunkte\">Research Focus<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Unsupervised Learning &amp; Explainable AI<\/h3>\n\n\n\n<p>We use matrix and tensor decompositions for clustering, pattern recognition, and feature extraction. Additional constraints such as non-negativity or temporal regularity, improve interpretability and reduce noise. These methods can also enhance explainability in supervised learning.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Modeling &amp; Optimization<\/h3>\n\n\n\n<p>The group addresses the entire process chain from modeling and optimization to application. For complex and large datasets, we develop tailored models for unsupervised learning and data fusion. This yields high-dimensional optimization problems with many (often nonsmooth) constraints, necessitating the advancement of algorithms on smooth (matrix) manifolds.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Biomedical Applications<\/h3>\n\n\n\n<p>Our methods are applied to clustering cancer patients via integrative analysis of multi-omics data and to pattern recognition in time-series data, as well as to analysis and feature extraction for data generated by point-of-care diagnostic tools. Furthermore, the data structures can be used in quantum chemistry calculations and signal processing.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-getwid-tabs__nav-link\"><span class=\"wp-block-getwid-tabs__title-wrapper\"><a href=\"#\"><span class=\"wp-block-getwid-tabs__title\">Publications<\/span><\/a><\/span><\/div><div class=\"wp-block-getwid-tabs__tab-content-wrapper\"><div class=\"wp-block-getwid-tabs__tab-content\">\n<h2 class=\"wp-block-heading\">Selected Publications<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h.4cauxmcu3y37_l\">Preprints<\/h3>\n\n\n\n<p>M. Bachmayr, S. Kr\u00e4mer, <strong>M. Pfeffer<\/strong>:<br>Low-rank eigenvalue solvers for block-sparse matrix product states. (2026)<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/bachmayr2026\">bibtex<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/2604.16118\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<p>V. Zalbertus, <strong>M. Pfeffer<\/strong>, A. Schmeding:<br><em>Optimization on Weak Riemannian Manifolds.<\/em> (2026)<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/zalbertus2026\">bibtex<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/2603.25396\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<p>V. Borovik, H. Friedman, S. Ho\u015ften, <strong>M. Pfeffer<\/strong>:<br><em>Numerical Algebraic Geometry for Energy Computations on Tensor Train Varieties.<\/em> (2025)<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/borovik2025\">bibtex<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/2512.06939\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<p>R. Bergmann, H. Jasa, P. John, <strong>M. Pfeffer<\/strong>:<br><em>The Intrinsic Riemannian Proximal Gradient Method for <\/em><em>C<\/em><em>onvex Optimization.<\/em> (2025)<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/bergmann2025b\">bibtex<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/2507.16055\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<p>R. Bergmann, H. Jasa, P. John, <strong>M. Pfeffer<\/strong>:<br><em>The Intrinsic Riemannian Proximal Gradient Method for Nonconvex Optimization.<\/em> (2025)<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/bergmann2025a\">bibtex<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/2506.09775\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<p>K. Kour, S. Dolgov, P. Benner, M. Stoll, <strong>M. Pfeffer<\/strong>:<br><em>A weighted subspace exponential kernel for support tensor machines. <\/em>(2023)<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/kour2023\">bibtex<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/2302.08134\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h.m3pvj3nssy9r_l\">Journal Articles<\/h3>\n\n\n\n<p>C. Chatzis, C. Schenker, <strong>M. Pfeffer<\/strong>, E. Acar:<br><em>tPARAFAC2: Tracking evolving patterns in (incomplete) temporal data. <\/em>(2025)<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/chatzis2025\">bibtex<\/a>],[<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10618-025-01122-6\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/2407.01356\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<p>J. Koenig, <strong>M. Pfeffer<\/strong>, M. Stoll:<br><em>Efficient training of Gaussian processes with tensor product structure.<\/em><em><br><\/em>Computational Optimization and Applications&nbsp; (2025)<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/k\u00f6nig2025\">bibtex<\/a>],[<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10589-025-00707-7?utm_source=rct_congratemailt&amp;utm_medium=email&amp;utm_campaign=oa_20250712&amp;utm_content=10.1007\/s10589-025-00707-7\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/2312.15305\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<p>F. Reggiani, Z. El Rashed, M. Petito, <strong>M. Pfeffer<\/strong>, A. Morabito, E. T. Tanda, F. Spagnolo, M. Croce, U. Pfeffer, A. Amaro:<br><em>Machine Learning Methods for Gene Selection in Uveal Melanoma.<\/em><em><br><\/em>International Journal of Molecular Sciences 25(3) (2024)<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/reggiani2024\">bibtex<\/a>],[<a href=\"https:\/\/www.mdpi.com\/1422-0067\/25\/3\/1796\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>]<\/p>\n\n\n\n<p><strong>M. Pfeffer<\/strong>, J. Samper:<br><em>The cone of 5\u00d75 completely positive matrices.<\/em><br>Discrete &amp; Computational Geometry (2024)<em><br><\/em>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/pfeffer2024\">bibtex<\/a>],[<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s00454-023-00620-y\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/2108.11928\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<p>A. Amaro, <strong>M. Pfeffer<\/strong>, U. Pfeffer, F. Reggiani:<br><em>Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma.<\/em><em><br><\/em>Biomedicines 10(12) (2022)<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/amaro2022\">bibtex<\/a>],[<a href=\"https:\/\/www.mdpi.com\/2227-9059\/10\/12\/3240\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>]<\/p>\n\n\n\n<p>H. Eisenmann, F. Krahmer, <strong>M. Pfeffer<\/strong>, A. Uschmajew:<br><em>Riemannian thresholding methods for row-sparse and low-rank matrix recovery. <\/em><em><br><\/em>Numerical Algorithms (2022)<em><br><\/em>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/eisenmann2022\">bibtex<\/a>],[<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11075-022-01433-5\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/2103.02356\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<p>M. Bachmayr, M. G\u00f6tte, <strong>M. Pfeffer<\/strong>:<br><em>Particle number conservation and block structures in Matrix Product States.<\/em><br>Calcolo 59, 24 (2022)<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/bachmayr2022\">bibtex<\/a>],[<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s10092-022-00462-9\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/2104.13483\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<p>C. Krumnow, <strong>M. Pfeffer<\/strong>, A. Uschmajew:<br><em>Computing eigenspaces with low rank constraints.<\/em><em><br><\/em>SIAM Journal on Scientific Computing,&nbsp; 43 (2021) 1, p. 586-608<em><br><\/em>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/krumnow2021\">bibtex<\/a>],[<a href=\"https:\/\/epubs.siam.org\/doi\/abs\/10.1137\/19M1308384\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>],[<a href=\"https:\/\/www.mis.mpg.de\/publications\/preprints\/2019\/prepr2019-102.html\" target=\"_blank\" rel=\"noreferrer noopener\">preprint<\/a>]<\/p>\n\n\n\n<p>M. Eigel, M. Marschall, <strong>M. Pfeffer<\/strong>, R. Schneider:<br><em>Adaptive Stochastic Galerkin FEM for lognormal coefficients in hierarchical tensor representations.<\/em><em><br><\/em>Numerische Mathematik, 145 (2020) 3, p. 655-692<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/eigel2020\">bibtex<\/a>],[<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s00211-020-01123-1\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/1811.00319\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<p><strong>M. Pfeffer<\/strong>, A. Seigal, B. Sturmfels:<br><em>Learning paths from signature tensors.<\/em><br>SIAM journal on matrix analysis and applications, 40 (2019) 2, p. 394-416<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/pfeffer2019a\">bibtex<\/a>],[<a href=\"https:\/\/epubs.siam.org\/doi\/10.1137\/18M1212331\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/1809.01588\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>],[<a href=\"https:\/\/github.com\/maxpfeffer\/signature-recovery\" target=\"_blank\" rel=\"noreferrer noopener\">github<\/a>]<\/p>\n\n\n\n<p><strong>M. Pfeffer<\/strong>, A. Uschmajew, A. Amaro, U. Pfeffer:<br><em>Data fusion techniques for the integration of multi-domain genomic data from uveal melanoma.<\/em><br>Cancers, 11 (2019) 10, 1434<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/pfeffer2019b\">bibtex<\/a>],[<a href=\"https:\/\/www.mdpi.com\/2072-6694\/11\/10\/1434\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>],[<a href=\"https:\/\/www.mis.mpg.de\/publications\/preprints\/2019\/prepr2019-42.html\" target=\"_blank\" rel=\"noreferrer noopener\">preprint<\/a>]<\/p>\n\n\n\n<p>M. Eigel, <strong>M. Pfeffer<\/strong>, R. Schneider:<br><em>Adaptive stochastic Galerkin FEM with hierarchical tensor representations.<\/em><br>Numerische Mathematik, 136 (2017) 3, p. 765-803<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/eigel2017\">bibtex<\/a>],[<a href=\"https:\/\/link.springer.com\/article\/10.1007%2Fs00211-016-0850-x\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>],[<a href=\"https:\/\/www3.math.tu-berlin.de\/preprints\/files\/Preprint-29-2015.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">preprint<\/a>]<\/p>\n\n\n\n<p>S. Szalay, <strong>M. Pfeffer<\/strong>, V. Murg, G. Barcza, F. Verstraete, R. Schneider, \u00d6. Legeza:<br><em>Tensor product methods and entanglement optimization for ab initio quantum chemistry.<\/em><br>International journal of quantum chemistry, 115 (2015) 19, p. 1342-1391<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/szalay2015\">bibtex<\/a>],[<a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/full\/10.1002\/qua.24898\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/1412.5829\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h.fkyak6fcgcmm_l\">Conference Proceedings<\/h3>\n\n\n\n<p>C. Chatzis, <strong>M. Pfeffer<\/strong>, P. Lind, E. Acar:<br><em>A Time-aware tensor decomposition for tracking evolving patterns.<\/em><br>2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/chatzis2023\">bibtex<\/a>],[<a href=\"https:\/\/ieeexplore.ieee.org\/document\/10285943\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>],[<a href=\"https:\/\/arxiv.org\/abs\/2308.07126\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv<\/a>]<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h.a7sa5p9wyxsp_l\">Theses<\/h3>\n\n\n\n<p><strong>M. Pfeffer<\/strong>: <em>Tensor methods for the numerical solution of high-dimensional parametric partial differential equations.<\/em><br>Dissertation, Technische Universit\u00e4t Berlin, 2018<br>[<a href=\"https:\/\/sites.google.com\/view\/maxpfeffer\/publications\/pfeffer2018diss\">bibtex<\/a>],[<a href=\"https:\/\/depositonce.tu-berlin.de\/\/handle\/11303\/8170\" target=\"_blank\" rel=\"noreferrer noopener\">link<\/a>]<\/p>\n\n\n\n<p><strong>M. Pfeffer<\/strong>: <em>Aspects of second-order optimization on fixed rank tensor manifolds.<\/em><em><br><\/em>Masterarbeit, Technische Universit\u00e4t Berlin, 2015<\/p>\n\n\n\n<p><strong>M. Pfeffer<\/strong>: <em>Dynamical low rank approximation in novel TT format.<\/em><em><br><\/em>Bachelorarbeit, Technische Universit\u00e4t Berlin, 2011<\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n","protected":false},"excerpt":{"rendered":"<p>The Chair of Bioanalytics and Data Science at the University of Potsdam (joint appointment with Fraunhofer IZI-BB) develops robust methods for pattern recognition and feature extraction from biomedical datasets. Our research focuses on unsupervised learning, explainable AI, data fusion, as well as mathematical modeling and optimization. Methodological work is directly applied to bioanalytical applications such &#8230;<\/p>\n","protected":false},"author":116,"featured_media":0,"parent":2481,"menu_order":4,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"class_list":["post-13209","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Fakult\u00e4t f\u00fcr Gesundheitswissenschaften | Bioanalytics and Data Science<\/title>\n<meta name=\"description\" content=\"FGW Brandenburg: Eine Fakult\u00e4t \u2013 drei Hochschulen. Kompetenz in Gesundheitsforschung, Nachwuchsf\u00f6rderung und Wissenstransfer.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.fgw-brandenburg.de\/en\/members\/professorial-chairs\/bioanalytics-and-data-science\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften | Bioanalytics and Data Science\" \/>\n<meta property=\"og:description\" content=\"FGW Brandenburg: Eine Fakult\u00e4t \u2013 drei Hochschulen. Kompetenz in Gesundheitsforschung, Nachwuchsf\u00f6rderung und Wissenstransfer.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.fgw-brandenburg.de\/en\/members\/professorial-chairs\/bioanalytics-and-data-science\/\" \/>\n<meta property=\"og:site_name\" content=\"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften\" \/>\n<meta property=\"article:modified_time\" content=\"2026-07-01T09:34:59+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/members\\\/professorial-chairs\\\/bioanalytics-and-data-science\\\/\",\"url\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/members\\\/professorial-chairs\\\/bioanalytics-and-data-science\\\/\",\"name\":\"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften | Bioanalytics and Data Science\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/#website\"},\"datePublished\":\"2026-06-23T12:38:00+00:00\",\"dateModified\":\"2026-07-01T09:34:59+00:00\",\"description\":\"FGW Brandenburg: Eine Fakult\u00e4t \u2013 drei Hochschulen. Kompetenz in Gesundheitsforschung, Nachwuchsf\u00f6rderung und Wissenstransfer.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/members\\\/professorial-chairs\\\/bioanalytics-and-data-science\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/members\\\/professorial-chairs\\\/bioanalytics-and-data-science\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/members\\\/professorial-chairs\\\/bioanalytics-and-data-science\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Start\",\"item\":\"\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Members\",\"item\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/members\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Professorial chairs\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/#website\",\"url\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/\",\"name\":\"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften\",\"description\":\"FGW Brandenburg: Eine Fakult\u00e4t \u2013 drei Hochschulen.\",\"publisher\":{\"@id\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/#organization\",\"name\":\"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften\",\"url\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/wp-content\\\/uploads\\\/2021\\\/05\\\/logo.png\",\"contentUrl\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/wp-content\\\/uploads\\\/2021\\\/05\\\/logo.png\",\"width\":552,\"height\":178,\"caption\":\"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften\"},\"image\":{\"@id\":\"https:\\\/\\\/www.fgw-brandenburg.de\\\/en\\\/#\\\/schema\\\/logo\\\/image\\\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften | Bioanalytics and Data Science","description":"FGW Brandenburg: Eine Fakult\u00e4t \u2013 drei Hochschulen. Kompetenz in Gesundheitsforschung, Nachwuchsf\u00f6rderung und Wissenstransfer.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.fgw-brandenburg.de\/en\/members\/professorial-chairs\/bioanalytics-and-data-science\/","og_locale":"en_US","og_type":"article","og_title":"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften | Bioanalytics and Data Science","og_description":"FGW Brandenburg: Eine Fakult\u00e4t \u2013 drei Hochschulen. Kompetenz in Gesundheitsforschung, Nachwuchsf\u00f6rderung und Wissenstransfer.","og_url":"https:\/\/www.fgw-brandenburg.de\/en\/members\/professorial-chairs\/bioanalytics-and-data-science\/","og_site_name":"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften","article_modified_time":"2026-07-01T09:34:59+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/www.fgw-brandenburg.de\/en\/members\/professorial-chairs\/bioanalytics-and-data-science\/","url":"https:\/\/www.fgw-brandenburg.de\/en\/members\/professorial-chairs\/bioanalytics-and-data-science\/","name":"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften | Bioanalytics and Data Science","isPartOf":{"@id":"https:\/\/www.fgw-brandenburg.de\/en\/#website"},"datePublished":"2026-06-23T12:38:00+00:00","dateModified":"2026-07-01T09:34:59+00:00","description":"FGW Brandenburg: Eine Fakult\u00e4t \u2013 drei Hochschulen. Kompetenz in Gesundheitsforschung, Nachwuchsf\u00f6rderung und Wissenstransfer.","breadcrumb":{"@id":"https:\/\/www.fgw-brandenburg.de\/en\/members\/professorial-chairs\/bioanalytics-and-data-science\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.fgw-brandenburg.de\/en\/members\/professorial-chairs\/bioanalytics-and-data-science\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/www.fgw-brandenburg.de\/en\/members\/professorial-chairs\/bioanalytics-and-data-science\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Start","item":"\/"},{"@type":"ListItem","position":2,"name":"Members","item":"https:\/\/www.fgw-brandenburg.de\/en\/members\/"},{"@type":"ListItem","position":3,"name":"Professorial chairs"}]},{"@type":"WebSite","@id":"https:\/\/www.fgw-brandenburg.de\/en\/#website","url":"https:\/\/www.fgw-brandenburg.de\/en\/","name":"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften","description":"FGW Brandenburg: Eine Fakult\u00e4t \u2013 drei Hochschulen.","publisher":{"@id":"https:\/\/www.fgw-brandenburg.de\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.fgw-brandenburg.de\/en\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.fgw-brandenburg.de\/en\/#organization","name":"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften","url":"https:\/\/www.fgw-brandenburg.de\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.fgw-brandenburg.de\/en\/#\/schema\/logo\/image\/","url":"https:\/\/www.fgw-brandenburg.de\/wp-content\/uploads\/2021\/05\/logo.png","contentUrl":"https:\/\/www.fgw-brandenburg.de\/wp-content\/uploads\/2021\/05\/logo.png","width":552,"height":178,"caption":"Fakult\u00e4t f\u00fcr Gesundheitswissenschaften"},"image":{"@id":"https:\/\/www.fgw-brandenburg.de\/en\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/www.fgw-brandenburg.de\/en\/wp-json\/wp\/v2\/pages\/13209","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.fgw-brandenburg.de\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.fgw-brandenburg.de\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.fgw-brandenburg.de\/en\/wp-json\/wp\/v2\/users\/116"}],"replies":[{"embeddable":true,"href":"https:\/\/www.fgw-brandenburg.de\/en\/wp-json\/wp\/v2\/comments?post=13209"}],"version-history":[{"count":4,"href":"https:\/\/www.fgw-brandenburg.de\/en\/wp-json\/wp\/v2\/pages\/13209\/revisions"}],"predecessor-version":[{"id":13627,"href":"https:\/\/www.fgw-brandenburg.de\/en\/wp-json\/wp\/v2\/pages\/13209\/revisions\/13627"}],"up":[{"embeddable":true,"href":"https:\/\/www.fgw-brandenburg.de\/en\/wp-json\/wp\/v2\/pages\/2481"}],"wp:attachment":[{"href":"https:\/\/www.fgw-brandenburg.de\/en\/wp-json\/wp\/v2\/media?parent=13209"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}