{"id":12068,"date":"2023-07-06T15:41:25","date_gmt":"2023-07-06T13:41:25","guid":{"rendered":"https:\/\/new.sano.science\/?post_type=people&p=12068"},"modified":"2025-03-26T11:37:25","modified_gmt":"2025-03-26T10:37:25","slug":"szymon-mazurek","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/szymon-mazurek\/","title":{"rendered":"Szymon Mazurek"},"excerpt":{"rendered":"

Junior Scientific Programmer<\/p>\n","protected":false},"featured_media":18405,"template":"","people_teams":[19,33],"class_list":["post-12068","people","type-people","status-publish","has-post-thumbnail","hentry","people_teams-research","people_teams-computational-neuroscience"],"yoast_head":"\nSzymon Mazurek - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/people\/szymon-mazurek\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Szymon Mazurek\" \/>\n<meta property=\"og:description\" content=\"Junior Scientific Programmer\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/szymon-mazurek\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2025-03-26T10:37:25+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/Szymon-Mazurek-Sano-1024x1024.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"1024\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/people\\\/szymon-mazurek\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/szymon-mazurek\\\/\",\"name\":\"Szymon Mazurek - 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Currently pursuing a masters degree in Computer Science and Intelligent Systems at the same university. His BSc thesis was about application of deep learning based vision models to assess the aging process in veterinary subjects, a part of CyfroVet project run by ACC Cyfronet AGH in which he still actively participates. Currently working on his masters thesis at Sano in the project that aims to predict the seizure events with deep learning based on the brain EEG signals. Interested in applications of AI in the field of medicine. Privately active brazilian jiu-jitsu competitor, non-fiction books and RPG games lover.<\/p>\n","email":"","social_media":[{"icon":{"ID":11994,"id":11994,"title":"linkedin","filename":"linkedin.svg","filesize":914,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","link":"https:\/\/sano.science\/people\/maciej-malawski\/linkedin-2\/","alt":"","author":"5","description":"","caption":"","name":"linkedin-2","status":"inherit","uploaded_to":531,"date":"2023-07-06 11:24:13","modified":"2023-07-06 11:24:13","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":1,"height":1,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","medium-width":300,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","medium_large-width":768,"medium_large-height":1,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","large-width":1024,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","1536x1536-width":1,"1536x1536-height":1,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","2048x2048-width":1,"2048x2048-height":1}},"link":"https:\/\/www.linkedin.com\/in\/szymon-mazurek-71193017b\/","name":"LinkedIn"}],"tabs":false,"quote":"","position_with_team":{"text_before_link":"Junior Scientific Programmer in","link_text":"Computer Vision Data Science","text_after_link":"","link":"https:\/\/sano.science\/research-teams\/computer-vision-brain-and-more-lab\/"},"publications":[{"ID":21108,"post_author":"8","post_date":"2025-02-04 11:14:41","post_date_gmt":"2025-02-04 10:14:41","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-h4bvYG\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-szymon-mazurek-nbsp-rosmary-blanco-nbsp-joan-falco-roget-nbsp-alessandro-crimi\">Szymon Mazurek, Rosmary Blanco, Joan Falc\u00f3-Roget, Alessandro Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-moezvg\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-NnUHpg\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Electroencephalography (EEG) is currently the most used way to accurately diagnose epilepsy given its ability to measure hypersinchronized periods of brain activity known as seizures. However, EEG recordings are noisy and require trained practitioners for meaningful information to be extracted. Most importantly, further post hoc analyses are inherently time-consuming and subjective. Recent advances in artificial intelligence have paved the way to develop automated workflows easing the task of preprocessing and detecting epileptic activity from EEG. Yet, these models are ubiquitously difficult to interpret thus posing a challenge for its wide acceptance in clinical scenarios. Here, we propose a graph neural network enhanced with attention layers able to accurately and robustly identify pathological brain activity. We provide both feature and graph explanations for each prediction of the trained model. Crucially, we show how graph neural networks capture non-trivial dependencies between cortical regions that agree with the current clinical consensus. Altogether, these results highlight the fact that explainable artificial intelligence need not compromise its performance and represent an improvement in the applicability of artificial intelligence networks in clinical practice<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-moezvg\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-5FMDft\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: Szymon Mazurek, Rosmary Blanco, Joan Falc\u00f3-Roget, Alessandro Crimi<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-5FMDft\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI: <\/strong><a href=\"https:\/\/doi.org\/10.1109\/ISBI56570.2024.10635821\" target=\"_blank\" rel=\"noreferrer noopener\">10.1109\/ISBI56570.2024.10635821<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-bK0bPv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: EEG, epilepsy<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-moezvg\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_67a1db75a8da7\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/10635821\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Explainable Graph Neural Networks for EEG Classification and Seizure Detection in Epileptic Patients","post_excerpt":"Conference manuscript in 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"explainable-graph-neural-networks-for-eeg-classification-and-seizure-detection-in-epileptic-patients","to_ping":"","pinged":"","post_modified":"2026-01-31 19:21:33","post_modified_gmt":"2026-01-31 18:21:33","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=21108","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14958,"post_author":"8","post_date":"2024-01-16 13:49:12","post_date_gmt":"2024-01-16 12:49:12","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-5dqj2O\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Jan K. Argasi\u0144ski, Natalia Lipp, Szymon Mazurek<\/h2>\n<!-- \/wp:heading -->","post_title":"Electroencephalographic (EEG) correlates of Visually Induced Motion Sickness (VIMS) in the Virtual Reality (VR) based simulations","post_excerpt":"In: INTERACT 2023 (19th International Conference of Technical Committee 13 (Human- Computer Interaction) of IFIP (International Federation for Information Processing, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"electroencephalographic-eeg-correlates-of-visually-induced-motion-sickness-vims-in-the-virtual-reality-vr-based-simulations","to_ping":"","pinged":"","post_modified":"2026-01-31 19:23:11","post_modified_gmt":"2026-01-31 18:23:11","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14958","menu_order":15,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":21526,"post_author":"8","post_date":"2025-02-17 18:25:57","post_date_gmt":"2025-02-17 17:25:57","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-aKHf29\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-szymon-mazurek-nbsp-monika-pytlarz-nbsp-sylwia-malec-alessandro-crimi-nbsp\">Szymon Mazurek,  Monika Pytlarz,  Sylwia Malec, Alessandro Crimi <\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-vfGiFQ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-kaIHoN\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Advancements across various industries have been significantly propelled by artificial intelligence. However, the rapid proliferation of these technologies also raises environmental concerns, particularly due to the substantial carbon footprints associated with training computational models. Segmenting the fetal brain in medical imaging presents a challenge due to its small size and the limited quality of fast 2D sequences. Deep neural networks emerge as a promising solution to this issue. The development of larger models in this context requires significant data and computing resources, leading to increased energy consumption. Our research focuses on exploring model architectures and compression techniques that enhance energy efficiency. We aim to optimize the balance between accuracy and energy usage through strategies such as designing lightweight networks, conducting architecture searches, and utilizing optimized distributed training tools. We have identified several effective strategies, including optimizing data loading, employing modern optimizers, implementing distributed training strategies, and reducing the precision of floating-point operations in light model architectures while adjusting parameters to match available computing resources. Our findings confirm that these methods ensure satisfactory model performance with minimal energy consumption during the training of deep neural networks for medical image segmentation.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-vfGiFQ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-aRLcXy\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1007\/978-3-031-63772-8_5<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-vfGiFQ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_67b36f118fcac\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-63772-8_5\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Investigation of Energy-Efficient AI Model Architectures and Compression Techniques for \u201cGreen\u201d Fetal Brain Segmentation","post_excerpt":"2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"investigation-of-energy-efficient-ai-model-architectures-and-compression-techniques-for-green-fetal-brain-segmentation","to_ping":"","pinged":"","post_modified":"2025-02-17 18:30:06","post_modified_gmt":"2025-02-17 17:30:06","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=21526","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12068","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/people"}],"version-history":[{"count":15,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12068\/revisions"}],"predecessor-version":[{"id":22604,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12068\/revisions\/22604"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/18405"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=12068"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=12068"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}