{"id":2,"date":"2018-11-13T13:41:05","date_gmt":"2018-11-13T13:41:05","guid":{"rendered":"http:\/\/groups.cs.umass.edu\/infofusion\/?page_id=2"},"modified":"2026-05-18T22:55:07","modified_gmt":"2026-05-18T22:55:07","slug":"home","status":"publish","type":"page","link":"https:\/\/groups.cs.umass.edu\/infofusion\/","title":{"rendered":"Home"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"2\" class=\"elementor elementor-2\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6644125b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6644125b\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3d70db2d\" data-id=\"3d70db2d\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7e84c143 elementor-widget elementor-widget-text-editor\" data-id=\"7e84c143\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400\">Welcome to the homepage of the Information Fusion Lab at the <a href=\"https:\/\/www.cics.umass.edu\/\">College of Information and Computer Sciences<\/a>, University of Massachusetts Amherst!<\/span><\/p>\n<p><span style=\"font-weight: 400\">We are a team of researchers at UMass Amherst CICS working on ML for multimodal data, including methods that support a wide range of biomedical applications. Our research includes a wide variety of topics including deep learning for the fusion of multi-resolution time series, images and structured information, the incorporation of domain knowledge or saliency in imaging and integration of multiple views for MRI analysis. Most recently, we introduced new methods for normalizing flows and transfer of causal models. Please see our research page for a full list of projects and our <\/span><a href=\"https:\/\/github.com\/Information-Fusion-Lab-Umass\"><span style=\"font-weight: 400\">GitHub page<\/span><\/a><span style=\"font-weight: 400\"> for code releases.<\/span><\/p>\n<p><span style=\"font-weight: 400\">We have an excellent team of talented graduate students and undergrads. If you are a UMass student and are interested in joining the group, or are a prospective external collaborator, please see <a href=\"https:\/\/groups.cs.umass.edu\/infofusion\/join\/\">this page<\/a>.<\/span><\/p>\n<p><span style=\"font-weight: 400\">Here is some recent news about our team:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400\">PhD student Deep Chakraborty was awarded the <a href=\"https:\/\/www.cics.umass.edu\/news\/latest-news\/edward-riseman-and-allen-hanson-scholarship-established\">Edward Riseman and Allen Hanson Scholarship<\/a><\/li>\n<li style=\"font-weight: 400\">PhD student Ke Xiao was awarded the <a href=\"https:\/\/umass.academicworks.com\/opportunities\/7074\">Paul Utgoff Memorial Scholarship<\/a><\/li>\n<li>The paper &#8216;<a href=\"https:\/\/invertibleworkshop.github.io\/accepted_papers\/pdfs\/40.pdf\">Normalizing Flows Across Dimensions<\/a>&#8216;, lead author Eddie Cunningham, was presented at the ICML 2020 INNF+ workshop<\/li>\n<li>The paper &#8216;<a href=\"https:\/\/biases-invariances-generalization.github.io\/pdf\/big_26.pdf\">Structure Mapping for Transferability of Causal Models<\/a>&#8216;, lead author Purva Pruthi, was presented at the ICML 2020 BIG workshop<\/li>\n<\/ul>\n<p>About the College of Information and Computer Sciences: <br \/>CICS is internationally recognized for its research activities and has one of the highest ranked and most competitive graduate programs in the nation. With over 40 faculty affiliated with the <a href=\"https:\/\/ds.cs.umass.edu\/\">Center for Data Science<\/a>, the College is distinguished by its culture of collaboration and leadership in multidisciplinary research. The department is #11 in AI and #20 in Computer Science in the US, according to the US News graduate schools ranking system. According to csrankings, CICS is in the <a href=\"http:\/\/csrankings.org\/#\/index?ai&amp;vision&amp;mlmining&amp;nlp&amp;ir\">top 10 universities in the US on AI<\/a> and <a href=\"http:\/\/csrankings.org\/#\/index?ai&amp;vision&amp;mlmining&amp;nlp&amp;ir&amp;world\">#16 in the world<\/a>.<\/p>\n<h2>Recent Publications<\/h2>\n<div class=\"teachpress_pub_list\"><form name=\"tppublistform\" method=\"get\"><a name=\"tppubs\" id=\"tppubs\"><\/a><\/form><div class=\"tablenav\"><div class=\"tablenav-pages\"><span class=\"displaying-num\">26 entries<\/span> <a class=\"page-numbers button disabled\">&laquo;<\/a> <a class=\"page-numbers button disabled\">&lsaquo;<\/a> 1 of 6 <a href=\"https:\/\/groups.cs.umass.edu\/infofusion\/?limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"next page\" class=\"page-numbers button\">&rsaquo;<\/a> <a href=\"https:\/\/groups.cs.umass.edu\/infofusion\/?limit=6&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"last page\" class=\"page-numbers button\">&raquo;<\/a> <\/div><\/div><table class=\"teachpress_publication_list\"><tr>\r\n                    <td>\r\n                        <h3 class=\"tp_h3\" id=\"tp_h3_2025\">2025<\/h3>\r\n                    <\/td>\r\n                <\/tr><tr class=\"tp_publication tp_publication_proceedings\"><td class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Shankar, Shiv;  Fiterau, Madalina<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('24','tp_links')\" style=\"cursor:pointer;\">Augmenting Randomized Controlled Trials with Foundation Models as Synthetic Units<\/a> <span class=\"tp_pub_type tp_  proceedings\">Proceedings<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_publisher\">ACM, <\/span><span class=\"tp_pub_additional_number\">no. 32, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>, <span class=\"tp_pub_additional_isbn\">ISBN: 9798400722004<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_24\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('24','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_24\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('24','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_24\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@proceedings{,<br \/>\r\ntitle = {Augmenting Randomized Controlled Trials with Foundation Models as Synthetic Units},<br \/>\r\nauthor = {Shiv Shankar and Madalina Fiterau},<br \/>\r\ndoi = {10.1145\/3765612.3767256},<br \/>\r\nisbn = {9798400722004},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-07-02},<br \/>\r\nurldate = {2025-07-02},<br \/>\r\nbooktitle = {Proceedings of the 16th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics},<br \/>\r\nnumber = {32},<br \/>\r\npublisher = {ACM},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {proceedings}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('24','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_24\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3765612.3767256\" title=\"Follow DOI:10.1145\/3765612.3767256\" target=\"_blank\">doi:10.1145\/3765612.3767256<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('24','tp_links')\">Close<\/a><\/p><\/div><\/td><\/tr><tr class=\"tp_publication tp_publication_conference\"><td class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Shankar, Shiv;  Sinha, Ritwik;  Fiterau, Madalina<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('21','tp_links')\" style=\"cursor:pointer;\">Experimentation under Treatment Dependent Network Interference<\/a> <span class=\"tp_pub_type tp_  conference\">Conference<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_booktitle\">41st Conference on Uncertainty in Artificial Intelligence, <\/span><span class=\"tp_pub_additional_volume\">vol. 286, <\/span><span class=\"tp_pub_additional_publisher\">PMLR, <\/span><span class=\"tp_pub_additional_year\">2025<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_21\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('21','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_21\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('21','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_21\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@conference{nokey,<br \/>\r\ntitle = {Experimentation under Treatment Dependent Network Interference},<br \/>\r\nauthor = {Shiv Shankar and Ritwik Sinha and Madalina Fiterau},<br \/>\r\neditor = {Silvia Chiappa},<br \/>\r\nurl = {https:\/\/proceedings.mlr.press\/v286\/shankar25a.html},<br \/>\r\nyear  = {2025},<br \/>\r\ndate = {2025-02-12},<br \/>\r\nurldate = {2025-02-12},<br \/>\r\nbooktitle = {41st Conference on Uncertainty in Artificial Intelligence},<br \/>\r\nvolume = {286},<br \/>\r\npages = {3787-3808},<br \/>\r\npublisher = {PMLR},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {conference}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('21','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_21\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/proceedings.mlr.press\/v286\/shankar25a.html\" title=\"https:\/\/proceedings.mlr.press\/v286\/shankar25a.html\" target=\"_blank\">https:\/\/proceedings.mlr.press\/v286\/shankar25a.html<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('21','tp_links')\">Close<\/a><\/p><\/div><\/td><\/tr><tr>\r\n                    <td>\r\n                        <h3 class=\"tp_h3\" id=\"tp_h3_2024\">2024<\/h3>\r\n                    <\/td>\r\n                <\/tr><tr class=\"tp_publication tp_publication_article\"><td class=\"tp_pub_info\"><p class=\"tp_pub_author\">Yunfei Lou Iman Deznabi, Abhinav Shaw<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('20','tp_links')\" style=\"cursor:pointer;\">Dynamic Clustering via Branched Deep Learning Enhances Personalization of Stress Prediction from Mobile Sensor Data<\/a> <span class=\"tp_pub_type tp_  article\">Journal Article<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">In: <\/span><span class=\"tp_pub_additional_journal\">Nature Scientific Reports, <\/span><span class=\"tp_pub_additional_volume\">vol. 14, <\/span><span class=\"tp_pub_additional_number\">no. 6631, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_20\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('20','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_20\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('20','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_20\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{nokey,<br \/>\r\ntitle = {Dynamic Clustering via Branched Deep Learning Enhances Personalization of Stress Prediction from Mobile Sensor Data},<br \/>\r\nauthor = {Iman Deznabi, Yunfei Lou, Abhinav Shaw, Natcha Simsiri, Tauhidur Rahman, Madalina Fiterau},<br \/>\r\nurl = {https:\/\/www.nature.com\/articles\/s41598-024-56674-2},<br \/>\r\ndoi = {10.1038\/s41598-024-56674-2},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-03-19},<br \/>\r\nurldate = {2024-03-19},<br \/>\r\njournal = {Nature Scientific Reports},<br \/>\r\nvolume = {14},<br \/>\r\nnumber = {6631},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('20','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_20\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.nature.com\/articles\/s41598-024-56674-2\" title=\"https:\/\/www.nature.com\/articles\/s41598-024-56674-2\" target=\"_blank\">https:\/\/www.nature.com\/articles\/s41598-024-56674-2<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1038\/s41598-024-56674-2\" title=\"Follow DOI:10.1038\/s41598-024-56674-2\" target=\"_blank\">doi:10.1038\/s41598-024-56674-2<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('20','tp_links')\">Close<\/a><\/p><\/div><\/td><\/tr><tr class=\"tp_publication tp_publication_conference\"><td class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Chandak, Yash;  Shankar, Shiv;  Syrgkanis, Vasilis;  Brunskill, Emma<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('25','tp_links')\" style=\"cursor:pointer;\">Adaptive instrument design for indirect experiments<\/a> <span class=\"tp_pub_type tp_  conference\">Conference<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_booktitle\">International Conference on Learning Representations, <\/span><span class=\"tp_pub_additional_volume\">vol. 2024, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_25\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('25','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_25\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('25','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_25\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@conference{nokey,<br \/>\r\ntitle = {Adaptive instrument design for indirect experiments},<br \/>\r\nauthor = {Chandak, Yash and Shankar, Shiv and Syrgkanis, Vasilis and Brunskill, Emma},<br \/>\r\nurl = {https:\/\/people.cs.umass.edu\/~sshankar\/publications\/iclr24a.pdf},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-18},<br \/>\r\nbooktitle = {International Conference on Learning Representations},<br \/>\r\nvolume = {2024},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {conference}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('25','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_25\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-file-pdf\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/people.cs.umass.edu\/~sshankar\/publications\/iclr24a.pdf\" title=\"https:\/\/people.cs.umass.edu\/~sshankar\/publications\/iclr24a.pdf\" target=\"_blank\">https:\/\/people.cs.umass.edu\/~sshankar\/publications\/iclr24a.pdf<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('25','tp_links')\">Close<\/a><\/p><\/div><\/td><\/tr><tr class=\"tp_publication tp_publication_workshop\"><td class=\"tp_pub_info\"><p class=\"tp_pub_author\">Peeyush Kumar Iman Deznabi, Madalina Fiterau<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('19','tp_links')\" style=\"cursor:pointer;\">Zero-shot micro-climate prediction with deep learning<\/a> <span class=\"tp_pub_type tp_  workshop\">Workshop<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_19\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('19','tp_abstract')\" title=\"Show abstract\" style=\"cursor:pointer;\">Abstract<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_19\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('19','tp_links')\" title=\"Show links and resources\" style=\"cursor:pointer;\">Links<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_19\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('19','tp_bibtex')\" title=\"Show BibTeX entry\" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_19\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@workshop{Deznabi2024,<br \/>\r\ntitle = {Zero-shot micro-climate prediction with deep learning},<br \/>\r\nauthor = {Iman Deznabi, Peeyush Kumar, Madalina Fiterau},<br \/>\r\nurl = {https:\/\/arxiv.org\/abs\/2401.02665},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-05},<br \/>\r\nurldate = {2024-01-05},<br \/>\r\njournal = {Tackling Climate Change with Machine Learning workshop NeurIPS (2023)},<br \/>\r\nabstract = {Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations. To compound the issue, making local predictions often relies on sensor data that may not be accessible for a new, previously unmonitored location. In response to these challenges, we propose a novel zero-shot learning approach designed to forecast various climate measurements at new and unmonitored locations. Our method surpasses conventional weather forecasting techniques in predicting microclimate variables by leveraging knowledge extracted from other geographic locations.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {workshop}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('19','tp_bibtex')\">Close<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_19\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations. To compound the issue, making local predictions often relies on sensor data that may not be accessible for a new, previously unmonitored location. In response to these challenges, we propose a novel zero-shot learning approach designed to forecast various climate measurements at new and unmonitored locations. Our method surpasses conventional weather forecasting techniques in predicting microclimate variables by leveraging knowledge extracted from other geographic locations.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('19','tp_abstract')\">Close<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_19\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-arxiv\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/arxiv.org\/abs\/2401.02665\" title=\"https:\/\/arxiv.org\/abs\/2401.02665\" target=\"_blank\">https:\/\/arxiv.org\/abs\/2401.02665<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('19','tp_links')\">Close<\/a><\/p><\/div><\/td><\/tr><\/table><div class=\"tablenav\"><div class=\"tablenav-pages\"><span class=\"displaying-num\">26 entries<\/span> <a class=\"page-numbers button disabled\">&laquo;<\/a> <a class=\"page-numbers button disabled\">&lsaquo;<\/a> 1 of 6 <a href=\"https:\/\/groups.cs.umass.edu\/infofusion\/?limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"next page\" class=\"page-numbers button\">&rsaquo;<\/a> <a href=\"https:\/\/groups.cs.umass.edu\/infofusion\/?limit=6&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"last page\" class=\"page-numbers button\">&raquo;<\/a> <\/div><\/div><\/div>\n\n\n<p><\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-979d29e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"979d29e\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-867a86d\" data-id=\"867a86d\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e379c64 elementor-widget elementor-widget-html\" data-id=\"e379c64\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<p>And now, a brief but honest recommendation for your evening unwind: <a href=\"https:\/\/megacasino-live.es\/\">Megacasino Live<\/a> stands out for its transparent financial operations\u2014no hidden fees, no arbitrary withdrawal delays, and verified payout speeds averaging under 15 minutes for e-wallets. Real players, real timelines, real support\u2014available around the clock.<\/p>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Welcome to the homepage of the Information Fusion Lab at the College of Information and Computer Sciences, University of Massachusetts Amherst! We are a team of researchers at UMass Amherst CICS working on ML for multimodal data, including methods that support a wide range of biomedical applications. Our research includes a wide variety of topics &hellip; <a href=\"https:\/\/groups.cs.umass.edu\/infofusion\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Home&#8221;<\/span><\/a><\/p>\n","protected":false},"author":11,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-2","page","type-page","status-publish","hentry","group-blog","no-sidebar","hfeed"],"_links":{"self":[{"href":"https:\/\/groups.cs.umass.edu\/infofusion\/wp-json\/wp\/v2\/pages\/2","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/groups.cs.umass.edu\/infofusion\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/groups.cs.umass.edu\/infofusion\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/infofusion\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/groups.cs.umass.edu\/infofusion\/wp-json\/wp\/v2\/comments?post=2"}],"version-history":[{"count":31,"href":"https:\/\/groups.cs.umass.edu\/infofusion\/wp-json\/wp\/v2\/pages\/2\/revisions"}],"predecessor-version":[{"id":1382,"href":"https:\/\/groups.cs.umass.edu\/infofusion\/wp-json\/wp\/v2\/pages\/2\/revisions\/1382"}],"wp:attachment":[{"href":"https:\/\/groups.cs.umass.edu\/infofusion\/wp-json\/wp\/v2\/media?parent=2"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}