{"id":13275,"date":"2017-12-20T13:27:13","date_gmt":"2017-12-20T18:27:13","guid":{"rendered":"https:\/\/www.bu.edu\/hic\/?p=13275"},"modified":"2017-12-21T16:32:07","modified_gmt":"2017-12-21T21:32:07","slug":"dec17-poster-sessions","status":"publish","type":"post","link":"https:\/\/www.bu.edu\/hic\/2017\/12\/20\/dec17-poster-sessions\/","title":{"rendered":"Over 200 Students Present Computational & Data-Driven Research at Hariri"},"content":{"rendered":"
[Return to Nexus Newsletter]<\/a><\/h6>\n

By\u00a0Sabrina Charania<\/span><\/p>\n

As part of its commitment to enriching the student research experience at BU, the Hariri Institute for Computing supports several computer science (CS)\u00a0courses that provide experiential learning opportunities to students. These opportunities give students a chance to exchange ideas with industry leaders and allow them to explore how computational perspectives can play into both their education and career paths. At the culmination of course work, the Institute hosts public poster sessions that give students the opportunity to showcase their work amongst peers, faculty, and external partners; this past fall\u00a0the Institute was pleased to host poster sessions for four\u00a0computer science courses, <\/span>Data Mechanics<\/em> (CS 591), <\/span>Machine Learning<\/em>\u00a0(CS 542),\u00a0<\/span>Tools for Data Science<\/em> (CS 506),\u00a0Spark Ventures (CS 491)<\/em>, and Mobile App Development <\/em>(CS591).<\/p>\n

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Data Mechanics students Steven Brzozowski (farthest left), Chris Joe (left of poster) and Keith Lovett (right of poster).<\/figcaption><\/figure>\n

Andrei Lapets<\/a>, CS Associate\u00a0Professor of the Practice and Director for Research Development and SAIL taught his\u00a0popular <\/span>Data Mechanics course again this past\u00a0fall. S<\/span>tudents\u00a0Steven Brzozowski, Chris Joe, Benjamin Kincaid, and Keith Lovett used the project as an opportunity to improve how winter storms are handled in Boston.\u00a0The model they developed uses optimization methods to set\u00a0large institutes, such as hospitals,\u00a0as a focal point to\u00a0be prioritized when plowing snow. Their model can be used by the City of Boston’s Public Works Department to understand how different\u00a0parts of the city can\u00a0be prioritized in snow storm preparations.<\/p>\n

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Data Mechanics students Yuchen Yuan (left) and Maulik Shah (right).<\/figcaption><\/figure>\n

Another pair of students, Yuchen Yuan and Maulik Shah, developed a\u00a0model that can be used to estimate\u00a0CO2 emissions by country as well as\u00a0predict the next year\u2019s emission. Using\u00a0regression and multilayer perceptrom methods, they analyzed\u00a0income per capita, energy use, carbon intensity and population intensity\u00a0to\u00a0calculate the CO2 emission.\u00a0Their model can be used by governments to set or change CO2 emission regulations.<\/p>\n

Kate Saenko<\/a>,\u00a0<\/b>director of the\u00a0<\/span>Computer Vision and Learning Group<\/a>\u00a0and leader of\u00a0AI Research (AIR) Initiative<\/a>, which is housed at the Hariri Institute,\u00a0taught the Machine Learning course this fall. Sudents\u00a0<\/span>Duaa Tashkandi, Wdjan Alharthi, Ben Gaudiosi and Shreya Ramesh focused their project on\u00a0identifying Brazilian names\u00a0out of a large pool of names.\u00a0 The company Digaai\u00a0is planning on using this project to be able to track the spread of Brazilian culture and influence in America, and specifically in Massachusetts.\u00a0 They also plan on using this to track Brazilian voting statistics in the state (e.g. ratio of registered to actual Brazilian voters).<\/p>\n

Jueru Jin, Haoyuan Liu, Yehui Huang and Han Xiao chose to research multi-class photo tagging.\u00a0Their project aims to make it easier for people to tag photos on Windows on Earth.\u00a0 The program currently tags photos into 11 predefined categories and cannot make new tags.\u00a0 However, this is a major improvement since there are about 11,000 photos that can be tagged into these groups and this program saves several hours of human effort that would have gone into manually tagging these 11,000 photos.<\/p>\n

Natalya Shelchkova, Colin Stuart, Jordan Love and\u00a0Varsha Achar used their project as an opportunity to optimize a dating app.\u00a0Their project added features to an app called Hater which matches people based on mutual dislikes rather than likes.\u00a0 The group added features to track users returning to the app, filter spam out more effectively, and match people more accurately.\u00a0 The group believes that these features could be used to track changes in societal trends and perceptions\u00a0if the information from these features is monitored over time.<\/p>\n

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Tools for Data Science students Alex O\u2019Connor (left), Sarah Ferry (right of poster) and Neela Kaushik (farthest right).<\/figcaption><\/figure>\n

In the <\/span>Tools for Data Science<\/em> course, taught by\u00a0Mark Crovella<\/a>, professor and chair of the Department of Computer Science, students pursued\u00a0a wide range of data-driven investigations, many of which were developed in collaboration with BU Spark! partners such as NECIR, Thomson Reuters, Citizens for Juvenile Justice, and many others. Sarah Ferry, Neela Kaushik and Alex O\u2019Connor worked with\u00a0<\/span>Emerge Massachusetts\u00a0(nonprofit organization with mission to get more female democrats elected into public office)\u00a0to find\u00a0factors that make female democratic political candidates more likely to win. They developed a model that identifies districts with features that contribute to a female democratic candidate\u2019s success by using election, voting behavior, demographics of candidates, and political contribution data from previous years.\u00a0Analysis showed which parts of states are likely to hold female candidates\u00a0with\u00a0reasonable accuracy (compared to the finalized analysis of the data with the current females siting in House of Representatives).<\/p>\n

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Tools for Data Science students Xinyu Li (farthest left), Di Wu (left of poster) and Yuxuan Mao (right).<\/figcaption><\/figure>\n

Di Wu, Yuxuan Mao and Xinyu Li worked with\u00a0Mercedes-Benz in testing the time prediction for greener manufacturing. They received a dataset (4209 trainning and testing samples with anonymous features names) from Mercedes-Benz and used various techniques such as forward step-wise feature selection, regression and Xgboost to determine\u00a0which features help increase the speed of the robust testing system and decide which techniques perform best. Mercedes-Benz can use the data analysis to explore\u00a0important features of their testing process and work to reduce overall\u00a0testing times.<\/p>\n

[Return to Nexus Newsletter]<\/a><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

[Return to Nexus Newsletter] By\u00a0Sabrina Charania As part of its commitment to enriching the student research experience at BU, the Hariri Institute for Computing supports several computer science (CS)\u00a0courses that provide experiential learning opportunities to students. These opportunities give students a chance to exchange ideas with industry leaders and allow them to explore how computational […]<\/p>\n","protected":false},"author":8550,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[11748,11747,11716],"tags":[],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/posts\/13275"}],"collection":[{"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/users\/8550"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/comments?post=13275"}],"version-history":[{"count":10,"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/posts\/13275\/revisions"}],"predecessor-version":[{"id":13428,"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/posts\/13275\/revisions\/13428"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/media?parent=13275"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/categories?post=13275"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bu.edu\/hic\/wp-json\/wp\/v2\/tags?post=13275"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}