{"id":34,"date":"2009-02-24T15:19:03","date_gmt":"2009-02-24T20:19:03","guid":{"rendered":"http:\/\/www.cs.uri.edu\/wordpress\/?page_id=34"},"modified":"2026-04-21T10:21:30","modified_gmt":"2026-04-21T14:21:30","slug":"34-2","status":"publish","type":"page","link":"https:\/\/web.uri.edu\/cs\/","title":{"rendered":"Front"},"content":{"rendered":"<section class=\"cl-wrapper cl-panel-wrapper\"><div class=\"cl-panel  \"><figure><img decoding=\"async\" src=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/pathwaysimg.jpg\" srcset=\"\" alt=\"Pathways 2024 participants\"><\/figure><article><p><p><\/p>\n<h1>Welcome to CSta<\/h1>\n<p>We combine Computer Science, Statistics, AI, Data Science, and Cybersecurity to enhance multidisciplinary learning and research for undergrads and grads. Cross campus and industry collaborations involve faculty, students, scientists, artists, health care researchers, historians, and engineers.<\/p><\/article><\/div><\/section><\/p>\n\n<section class=\"cl-wrapper cl-boxout-wrapper\"><div class=\"cl-boxout  \"><h1>Undergraduate &amp; Graduate Courses<\/h1>\n\n<p>See our courses in Computer Science, Statistics, Data Science, and Cybersecurity, ranging from computing foundations to theory and statistics to systems and artificial intelligence.<\/p>\n\n\n<a class=\"cl-button   prominent\" href=\"https:\/\/web.uri.edu\/cs\/academics\/course-listing\/\" title=\"\">courses<\/a>\n<\/div><\/section>\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"cl-wrapper cl-card-wrapper\"><a class=\"cl-card  \" href=\"https:\/\/web.uri.edu\/cs\/academics\/#undergrad_studies\" title=\"\"><div class=\"cl-card-container media\"><img decoding=\"async\" src=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/feat_img_comp_sci.jpg\" srcset=\"\" alt=\"A monitor with code\"><\/div><div class=\"cl-card-container text\"><div class=\"cl-card-text\"><h2>Undergraduate Programs<\/h2><p>Our undergraduate curricula are aligned with the ACM &amp; IEEE Curriculum Guidelines. Courses in artificial intelligence, machine learning, computer vision, and human-centered computing. Experiential learning provides students with a practical and rounded curriculum.<\/p><\/div><\/div><\/a><\/div><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"cl-wrapper cl-card-wrapper\"><a class=\"cl-card  \" href=\"https:\/\/web.uri.edu\/cs\/academics\/#grad_studies\" title=\"\"><div class=\"cl-card-container media\"><img decoding=\"async\" src=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/computer_room_cover.jpg\" srcset=\"\" alt=\"\"><\/div><div class=\"cl-card-container text\"><div class=\"cl-card-text\"><h2>Graduate Programs<\/h2><p>We offer M.S. and Ph.D. degrees in Computer Science and Statistics, a graduate certificate in Cyber Security, and a PSM degree in Cyber Security. For selected full-time students, the department may offer teaching and research assistantships that pay a stipend and cover tuition, fees, and insurance.<\/p><\/div><\/div><\/a><\/div><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"cl-wrapper cl-card-wrapper\"><a class=\"cl-card  \" href=\"https:\/\/web.uri.edu\/cs\/research\/\" title=\"\"><div class=\"cl-card-container media\"><img decoding=\"async\" src=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/manifold6_600_wps.jpg\" srcset=\"\" alt=\"A three-dimensional manifold\"><\/div><div class=\"cl-card-container text\"><div class=\"cl-card-text\"><h2>Research<\/h2><p>As our department encompasses both computer science and statistics, the areas of research are wide-ranging.  Faculty and students collaborate across URI with industry and government.<\/p><\/div><\/div><\/a><\/div><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"cl-wrapper cl-card-wrapper\"><a class=\"cl-card  \" href=\"https:\/\/web.uri.edu\/cs\/academics\/icsp\/\" title=\"\"><div class=\"cl-card-container media\"><img decoding=\"async\" src=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/brandenburg_gate_600.jpg\" srcset=\"\" alt=\"International Computer Science Program student Richard Burke standing in front of the Brandenburg gate\"><\/div><div class=\"cl-card-container text\"><div class=\"cl-card-text\"><h2>International Program<\/h2><p>We collaborate with URI\u2019s International Engineering Program (IEP) and the Department of Languages to offer the unique International Computer Science Program (ICSP).<\/p><\/div><\/div><\/a><\/div><\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\">Announcements<\/h2>\n\n\n<ul class=\"display-posts-listing\"><li class=\"listing-item\"><a class=\"image\" href=\"https:\/\/web.uri.edu\/cs\/talk-260417\/\"><img loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"150\" src=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/cho_profile_300-150x150.jpg\" class=\"attachment-thumbnail size-thumbnail wp-post-image\" alt=\"Hoon Cho\" srcset=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/cho_profile_300-150x150.jpg 150w, https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/cho_profile_300.jpg 300w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/a> <a class=\"title\" href=\"https:\/\/web.uri.edu\/cs\/talk-260417\/\">[Talk] Hoon Cho: Enabling Collaborative Genomic Studies with Privacy<\/a> <span class=\"date\">(4\/15\/2026)<\/span> <span class=\"excerpt-dash\">-<\/span> <span class=\"excerpt\">When: Friday, April 17, 3:00 PMWhere: Tyler 055 AbstractThe sensitive nature of genomic data poses major challenges for data sharing and collaboration in biomedicine. Traditional safeguards often lead to fragmentation across data silos, hindering large-scale analysis. I will describe our recent work on secure federated (SF) algorithms, which combine cryptography and distributed computation to enable [&hellip;]<\/span><\/li><li class=\"listing-item\"><a class=\"image\" href=\"https:\/\/web.uri.edu\/cs\/shaun-wallace-named-2026-uri-ssirep-public-policy-fellow\/\"><img loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"150\" src=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/Shaun-Wallace_300-150x150.jpg\" class=\"attachment-thumbnail size-thumbnail wp-post-image\" alt=\"Shaun Wallace\" srcset=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/Shaun-Wallace_300-150x150.jpg 150w, https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/Shaun-Wallace_300.jpg 300w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/a> <a class=\"title\" href=\"https:\/\/web.uri.edu\/cs\/shaun-wallace-named-2026-uri-ssirep-public-policy-fellow\/\">Shaun Wallace Named 2026 URI SSIREP Public Policy Fellow<\/a> <span class=\"date\">(4\/3\/2026)<\/span> <span class=\"excerpt-dash\">-<\/span> <span class=\"excerpt\">Assistant Professor Shaun Wallace has been selected as a 2026 Public Policy Fellow through URI&#8217;s Social Science Institute for Research, Education, and Policy (SSIREP). Wallace&#8217;s fellowship project, &#8220;Exploring Cyber Dating Aggression in Real-Time Among Young Adults,&#8221; will prototype a web-based user-first privacy-preserving extraction pipeline for identifying cyber dating aggression (CDA) from their naturally occurring digital [&hellip;]<\/span><\/li><li class=\"listing-item\"><a class=\"image\" href=\"https:\/\/web.uri.edu\/cs\/talk-260410\/\"><img loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"150\" src=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/Anny-Claude-Joseph_300-150x150.jpg\" class=\"attachment-thumbnail size-thumbnail wp-post-image\" alt=\"Anny-Claude Joseph\" srcset=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/Anny-Claude-Joseph_300-150x150.jpg 150w, https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/Anny-Claude-Joseph_300.jpg 300w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/a> <a class=\"title\" href=\"https:\/\/web.uri.edu\/cs\/talk-260410\/\">[Talk] Anny-Claude Joseph: Causal Inference under Spatial Interference<\/a> <span class=\"date\">(4\/2\/2026)<\/span> <span class=\"excerpt-dash\">-<\/span> <span class=\"excerpt\">When: Friday, April 10, 3:00 PMWhere: Tyler 055 AbstractEnvironmental epidemiologists are increasingly interested in establishing causality between exposures and health outcomes. A popular model for causal inference is the Rubin Causal Model (RCM). An important assumption under RCM is no interference, that is, the potential outcomes of one unit in the study are not affected [&hellip;]<\/span><\/li><li class=\"listing-item\"><a class=\"image\" href=\"https:\/\/web.uri.edu\/cs\/talk-ds-260413\/\"><img loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"150\" src=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/IACR_logo_300-150x150.jpg\" class=\"attachment-thumbnail size-thumbnail wp-post-image\" alt=\"IACR\" srcset=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/IACR_logo_300-150x150.jpg 150w, https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/IACR_logo_300.jpg 300w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/a> <a class=\"title\" href=\"https:\/\/web.uri.edu\/cs\/talk-ds-260413\/\">[Talk] Data and Discussion DS event: Academic and Professional Opportunities<\/a> <span class=\"date\">(3\/31\/2026)<\/span> <span class=\"excerpt-dash\">-<\/span> <span class=\"excerpt\">When: Friday, April 3, 12-2 pm Where: LIB 166 Join us for an engaging Data Science event co-hosted with the Women in Data Science club. Our featured speaker is Alena Korshunova (MBA), a Principal Business Intelligence Analyst in Innovation, Analytics &amp; AI at FM Global. She will share insights into her career path and experience [&hellip;]<\/span><\/li><li class=\"listing-item\"><a class=\"image\" href=\"https:\/\/web.uri.edu\/cs\/talk-250403\/\"><img loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"150\" src=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/fox-tyler_300-150x150.jpg\" class=\"attachment-thumbnail size-thumbnail wp-post-image\" alt=\"Ryan Fox-Tyler\" srcset=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/fox-tyler_300-150x150.jpg 150w, https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/fox-tyler_300.jpg 300w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/a> <a class=\"title\" href=\"https:\/\/web.uri.edu\/cs\/talk-250403\/\">[Talk] Ryan Fox-Tyler: AI Agents in Production: The Gap Between What&#8217;s Possible and What&#8217;s Deployable<\/a> <span class=\"date\">(3\/30\/2026)<\/span> <span class=\"excerpt-dash\">-<\/span> <span class=\"excerpt\">When: Friday, April 3, 3:00 PMWhere: Tyler 055 AbstractEvery generation of developer infrastructure faces the same core tension: how do you give increasingly powerful systems the ability to act autonomously while maintaining the safety and governance guarantees that organizations require? For decades, this played out in distributed systems \u2014 microservices, data pipelines, and platform engineering [&hellip;]<\/span><\/li><li class=\"listing-item\"><a class=\"image\" href=\"https:\/\/web.uri.edu\/cs\/talk-260331\/\"><img loading=\"lazy\" decoding=\"async\" width=\"150\" height=\"150\" src=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/fig1_600-150x150.jpg\" class=\"attachment-thumbnail size-thumbnail wp-post-image\" alt=\"Optimization Example\" srcset=\"https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/fig1_600-150x150.jpg 150w, https:\/\/web.uri.edu\/cs\/wp-content\/uploads\/sites\/1531\/fig1_600-300x300.jpg 300w\" sizes=\"auto, (max-width: 150px) 100vw, 150px\" \/><\/a> <a class=\"title\" href=\"https:\/\/web.uri.edu\/cs\/talk-260331\/\">[Talk] LicketySPLIT: Near-Optimal Decision Trees in a SPLIT Second<\/a> <span class=\"date\">(3\/27\/2026)<\/span> <span class=\"excerpt-dash\">-<\/span> <span class=\"excerpt\">When: Tuesday, March 31, 11:00 AMWhere: Bliss Hall 190 AbstractDecision tree optimization is fundamental to interpretable machine learning. The most popular approach is to greedily search for the best feature at every decision point, which is fast but provably suboptimal. Recent approaches find the global optimum using branch and bound with dynamic programming, showing substantial [&hellip;]<\/span><\/li><\/ul>\n\n\n<a class=\"cl-button  \" href=\"https:\/\/web.uri.edu\/cs\/news-and-events\/\" title=\"\">more<\/a>\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Announcements<\/p>\n","protected":false},"author":4688,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-34","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/pages\/34","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/users\/4688"}],"replies":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/comments?post=34"}],"version-history":[{"count":4,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/pages\/34\/revisions"}],"predecessor-version":[{"id":18518,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/pages\/34\/revisions\/18518"}],"wp:attachment":[{"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/media?parent=34"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}