{"id":2021,"date":"2017-04-02T21:27:31","date_gmt":"2017-04-03T02:27:31","guid":{"rendered":"http:\/\/hachmannlab.cbe.buffalo.edu\/?p=2021"},"modified":"2019-04-15T11:12:08","modified_gmt":"2019-04-15T16:12:08","slug":"sykhere-brown-joins-the-group-as-undergraduate-no-12-sponsored-by-lsamp-summer-research-internship","status":"publish","type":"post","link":"https:\/\/hachmannlab.cbe.buffalo.edu\/index.php\/sykhere-brown-joins-the-group-as-undergraduate-no-12-sponsored-by-lsamp-summer-research-internship\/","title":{"rendered":"Sykhere Brown joins the group as undergraduate No 12, sponsored by LSAMP Summer Research Internship"},"content":{"rendered":"<p style=\"text-align: justify;\">\n<a href=\"\/index.php\/team\/undergraduate-students\/sykhere-brown\/\">Sykhere Brown<\/a>, a veteran of Johannes&#8217; CE 451 Computer-Aided Research class, joins the group as its 12th undergraduate research assistant. Sykhere will work with <a href=\"\/index.php\/team\/graduate-students\/mojtaba-haghighatlari\/\">Mojtaba<\/a> on machine learning problems, in particular on devising techniques to assess the range of applicability of data-derived models. His stay in the group during the summer is sponsored by an LSAMP Summer Research Internship.\n<\/p>\n<p style=\"text-align: justify;\">\nThe <a title=\"LSAMP\" href=\" http:\/\/engineering.buffalo.edu\/home\/outreach\/diversity\/lsamp.html\" target=\"_blank\" rel=\"noopener noreferrer\"> Louis Stokes Alliance for Minority Participation (LSAMP)<\/a> program seeks to diversify the STEM workforce by significantly increasing the numbers of students that successfully complete high-quality degree programs in the science, technology, engineering and mathematics (STEM) disciplines. Particular emphasis is placed on the support of groups that historically have been underrepresented in the STEM field, i.e., African-Americans, Alaskan Natives, Native Americans, Hispanic Americans, and Native Pacific Islanders. The <a title=\" LSAMP Summer Research Internship Program \" href=\" http:\/\/engineering.buffalo.edu\/home\/outreach\/diversity\/lsamp\/summer.html \" target=\"_blank\" rel=\"noopener noreferrer\"> LSAMP Summer Research Internship Program <\/a> is a 10-week intensive summer research experience for undergraduates sponsored by the NSF.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Sykhere Brown, a veteran of Johannes&#8217; CE 451 Computer-Aided Research class, joins the group as its 12th undergraduate research assistant. Sykhere will work with Mojtaba on machine learning problems, in particular on devising techniques to assess the range of applicability of data-derived models. His stay in the group during the summer is sponsored by an LSAMP Summer Research Internship. The Louis Stokes Alliance for Minority Participation (LSAMP) program seeks to diversify the STEM workforce by significantly increasing the numbers of students that successfully complete high-quality degree programs in the science, technology, engineering and mathematics (STEM) disciplines. Particular emphasis is placed on the support of groups that historically have been underrepresented in the STEM field, i.e., African-Americans, Alaskan Natives, Native Americans, Hispanic Americans, and Native Pacific Islanders. The LSAMP Summer Research Internship Program is a 10-week intensive summer research experience for undergraduates sponsored by the NSF.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[24,29,7,26],"tags":[],"class_list":["post-2021","post","type-post","status-publish","format-standard","hentry","category-awards","category-initiatives","category-news","category-team","entry","post-inner"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.9 - aioseo.com -->\n\t<meta name=\"description\" content=\"Sykhere Brown, a veteran of Johannes&#039; CE 451 Computer-Aided Research class, joins the group as its 12th undergraduate research assistant. Sykhere will work with Mojtaba on machine learning problems, in particular on devising techniques to assess the range of applicability of data-derived models. 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