{"id":52,"date":"2021-10-11T13:54:27","date_gmt":"2021-10-11T13:54:27","guid":{"rendered":"http:\/\/www.eng.biu.ac.il\/lindeno\/?page_id=52"},"modified":"2021-11-08T16:35:15","modified_gmt":"2021-11-08T16:35:15","slug":"research","status":"publish","type":"page","link":"https:\/\/www.eng.biu.ac.il\/lindeno\/research\/","title":{"rendered":"Research"},"content":{"rendered":"\n

Machine learning has revolutionized the way we do data science. In particular, deep neural networks have closed the performance gap between humans and machines in disciplines such as vision, image processing, audio processing, and natural language processing. Nonetheless, learning from unstructured high-dimensional empirical observations (for example gene measurements) is usually impossible for humans and may be difficult for machines. In particular, machine learning becomes extremely challenging since empirical measurements are often unlabeled, noisy, sparse, imbalanced, and high dimensional. <\/p>\n\n\n\n

My main research goal is to develop automatic methods that would lead to novel scientific findings (in biology, physics, medicine, etc.). To achieve this goal I am currently developing deep learning methodologies that tackle the following tasks:<\/p>\n\n\n\n

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Feature Selection<\/strong><\/p>\n\n\n\n

Example paper<\/strong> - Feature selection using Stochastic Gates<\/a><\/p>\n<\/div><\/div>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n

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Feature Extraction\/ Manifold Learning<\/strong><\/p>\n\n\n\n

Example paper: <\/strong>LOCA: LOcal Conformal Autoencoder for standardized data coordinates<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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Multimodal Fusion<\/strong><\/p>\n\n\n\n

Example Paper:<\/strong> Multiview Diffusion Maps<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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Generative Models<\/strong><\/p>\n\n\n\n

Example Paper: <\/strong>Variational Diffusion Autoencoders with Random Walk Sampling<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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Biomedical Data Analysis<\/strong><\/p>\n\n\n\n

Example Paper: <\/strong>Alignment free identification of clones in B cell receptor repertoires<\/a><\/p>\n<\/div><\/div>\n\n\n\n

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Signal Processing<\/strong><\/p>\n\n\n\n

Example Paper: <\/strong>Multiview Kernels for Low-Dimensional Modeling of Seismic Events<\/a><\/p>\n<\/div><\/div>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Machine learning has revolutionized the way we do data science. In particular, deep neural networks have closed the performance gap between humans and machines in disciplines such as vision, image processing, audio processing, and natural language processing. Nonetheless, learning from unstructured high-dimensional empirical observations (for example gene measurements) is usually impossible for humans and may … Continue reading Research<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":93,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-52","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/pages\/52"}],"collection":[{"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/users\/93"}],"replies":[{"embeddable":true,"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/comments?post=52"}],"version-history":[{"count":13,"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/pages\/52\/revisions"}],"predecessor-version":[{"id":130,"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/pages\/52\/revisions\/130"}],"wp:attachment":[{"href":"https:\/\/www.eng.biu.ac.il\/lindeno\/wp-json\/wp\/v2\/media?parent=52"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}