{"id":34364,"date":"2015-11-02T14:40:51","date_gmt":"2015-11-02T18:40:51","guid":{"rendered":"https:\/\/www.bu.edu\/cise\/?p=34364"},"modified":"2022-01-24T23:03:57","modified_gmt":"2022-01-25T04:03:57","slug":"kulis-named-first-levine-career-development-professor","status":"publish","type":"post","link":"https:\/\/www.bu.edu\/cise\/kulis-named-first-levine-career-development-professor\/","title":{"rendered":"Kulis Named First Levine Career Development Professor"},"content":{"rendered":"

\u00a0<\/span>To Brian Kulis<\/a>, advances in machine learning and artificial intelligence bring with them the opportunity to mesh theory with real-world applications, like driverless cars and computers that can describe aloud the objects in front of them.<\/p>\n

\u201cYou want computers to be able to recognize what they are seeing in images and video,\u201d says Kulis, a College of Engineering assistant professor of electrical and computer engineering and systems engineering. \u201cFor instance, can it recognize all the objects in a picture? Or a more difficult problem would be, can it look at a video and describe in English what is happening in the video? That is a major application area for machine learning these days.\u201d<\/span><\/p>\n

Kulis\u2019 expertise in machine learning, along with his research in computer vision systems and other applications, brought him to BU this fall and has earned him the University\u2019s inaugural Peter J. Levine Career Development Professorship, which will be awarded annually to rising junior faculty in the electrical and computer engineering department. The professorship\u2019s three-year stipend will support scholarly and laboratory work. It was established by a\u00a0<\/span>gift from Peter J. Levine\u00a0<\/span><\/a>(ENG\u201983), a partner at the Silicon Valley venture capital firm\u00a0<\/span>Andreesen Horowitz<\/a>\u00a0and a part-time faculty member at Stanford University\u2019s Graduate School of Business.<\/span><\/p>\n

Kulis is a rising star in the machine learning field and the Levine professorship speaks to BU\u2019s recognition of his achievements thus far, says Kenneth R. Lutchen, dean of ENG, and is a commitment to helping Kulis build on his world-class research and teaching.<\/span><\/p>\n

Lutchen adds that Kulis, who earlier this year also received a National Science Foundation\u00a0<\/span>Faculty Early Career Development (CAREER) Award<\/a>\u00a0for research into machine learning systems, will be a critical faculty member of ENG\u2019s new master\u2019s degree specialization in data analytics.<\/span><\/p>\n

\u201cWe think it will be one of the most popular specializations we have, and it will be accessible not just to students in this department, but also to biomedical, mechanical engineering, and systems engineering students who will want to have this same specialization. Brian\u2019s expertise is perfectly aligned with teaching this,\u201d Lutchen says.<\/span><\/p>\n

Also a College of Arts & Sciences assistant professor of computer science, Kulis earned a bachelor\u2019s degree in computer science and mathematics at Cornell University and a doctorate in computer science at the University of Texas at Austin. He did postdoctoral work at the University of California, Berkeley, then spent three years on the faculty of Ohio State University before coming to BU.<\/span><\/p>\n

Millions or billions of data points<\/span><\/strong><\/p>\n

Data science is about managing huge data sets\u2014think millions or billions of data points, from an array of sources\u2014and programming computers to analyze the data and make predictions based on identified patterns. Advances in storing and analyzing these growing collections of information has made Big Data a hot field in both academia and industry, with\u00a0<\/span>Harvard Business Review<\/em><\/a>\u00a0pronouncing data scientist \u201cthe sexiest job of the 21st century.\u201d<\/span><\/p>\n

Those advances include artificial intelligence and machine learning, and they are what enable Kulis to develop exciting connections between theory and action. \u201cThere is a nice combination between the mathematics and the theoretical aspects of machine learning. It\u2019s a very applied field, trying to solve real problems,\u201d he says. \u201cThat balance is pretty rare.\u201d<\/span><\/p>\n

He describes his specific area of research as scalable nonparametric machine learning. While a traditional statistical model for analyzing a large amount of data would establish a model for performing the analysis, Kulis pursues a different method. In his research, the data itself determines how simple or complicated the analysis should be.<\/span><\/p>\n

An example of this approach, he says, is analyzing a large collection of documents for the content they contain. A parametric model would establish 10 clusters of documents to analyze, one each on a set topic. A nonparametric model would instead analyze all of the documents and determine how many topics should be included in the analysis. \u201cYou want the data itself to guide the discovery process, and so if there is a lot to say, then you want your algorithm to reveal that structure,\u201d he says. \u201cIt\u2019s a more flexible way to do analysis.\u201d<\/span><\/p>\n

The field is ripe for approaches that allow researchers from different fields\u2013biology and business, for example\u2013to apply machine learning techniques to develop new ways of looking at the data they collect. Kulis says he is looking forward to working with faculty and students from different BU departments both in research and in his courses. \u201cMachine learning brings together a lot of fields that for a long time have been fairly disjoined. When it comes to teaching, a lot of my excitement is in trying to bridge these different disciplines and to teach courses that bring together people from different areas,\u201d he says.<\/span><\/p>\n

The curriculum, Lutchen says, has relevance to the world at large: \u201cAs an engineering faculty, we want people to understand how these new tools and techniques can help society.\u201d<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

\u00a0To Brian Kulis, advances in machine learning and artificial intelligence bring with them the opportunity to mesh theory with real-world applications, like driverless cars and computers that can describe aloud the objects in front of them. \u201cYou want computers to be able to recognize what they are seeing in images and video,\u201d says Kulis, a […]<\/p>\n","protected":false},"author":10316,"featured_media":34372,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[127],"tags":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/posts\/34364"}],"collection":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/users\/10316"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/comments?post=34364"}],"version-history":[{"count":6,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/posts\/34364\/revisions"}],"predecessor-version":[{"id":35531,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/posts\/34364\/revisions\/35531"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/media\/34372"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/media?parent=34364"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/categories?post=34364"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bu.edu\/cise\/wp-json\/wp\/v2\/tags?post=34364"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}