{"id":12050,"date":"2023-07-06T15:21:47","date_gmt":"2023-07-06T13:21:47","guid":{"rendered":"https:\/\/new.sano.science\/?post_type=people&p=12050"},"modified":"2025-02-18T09:30:30","modified_gmt":"2025-02-18T08:30:30","slug":"sylwia-malec","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/sylwia-malec\/","title":{"rendered":"Sylwia Malec"},"excerpt":{"rendered":"

PhD Student in Computer Vision Data Science<\/p>\n","protected":false},"featured_media":12051,"template":"","people_teams":[24],"class_list":["post-12050","people","type-people","status-publish","has-post-thumbnail","hentry","people_teams-alumni"],"yoast_head":"\nSylwia Malec - 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\/sylwia-malec\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Sylwia Malec\" \/>\n<meta property=\"og:description\" content=\"PhD Student in Computer Vision Data Science\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/sylwia-malec\/\" \/>\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=\"2025-02-18T08:30:30+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/sylwia_malec.png\" \/>\n\t<meta property=\"og:image:width\" content=\"350\" \/>\n\t<meta property=\"og:image:height\" content=\"350\" \/>\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\\\/sylwia-malec\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/sylwia-malec\\\/\",\"name\":\"Sylwia Malec - 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Her bachelor thesis focused on obtaining 3D-printed pelvis directly from a patient’s CT scan. She gained practical experience as a software engineer and data scieSylwia holds a BSc from AGH University of Science and Technology. Her Bachelor\u2019s thesis focused on obtaining 3D printed pelvis directly from a patient\u2019s CT scan. She gained practical experience as a software engineer and data scientist in the industry, where she was focusing on Computer Vision solutions for public safety. In Sano, she works on challenges associated with medical imaging of brain tumors in collaboration with the University of Pennsylvania and the University of Zurich. Privately, she calls herself a renaissance soul due to having lots of interests and hobbies – she is a photomodel, traveler, yoga practitioner, just to name a few.<\/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\/sylwia-malec-795144150\/","name":"LinkedIn"}],"tabs":false,"quote":"","position_with_team":{"text_before_link":"PhD Student in","link_text":"Computer Vision Data Science","text_after_link":"","link":"https:\/\/sano.science\/research-teams\/computer-vision-brain-and-more-lab\/"},"publications":[{"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\/12050","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":3,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12050\/revisions"}],"predecessor-version":[{"id":21539,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/12050\/revisions\/21539"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/12051"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=12050"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=12050"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}