{"599787":{"#nid":"599787","#data":{"type":"news","title":"Georgia Tech Team To Use Microsoft Grant to Study Human Migration Dynamics","body":[{"value":"\u003Cp\u003EGeorgia Tech School of Computational Science and Engineering (CSE) Assistant Professor \u003Cstrong\u003EBistra Dilkina\u003C\/strong\u003E has been awarded a grant from Microsoft as part of its \u003Ca href=\u0022https:\/\/www.microsoft.com\/en-us\/aiforearth\u0022\u003EAI for Earth\u003C\/a\u003E program.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAI for Earth works to empower people and organizations to solve global environmental challenges by increasing access to artificial intelligence (AI) tools and educational opportunities,\u0026nbsp;all while accelerating innovation.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026quot;My research group focuses on developing \u003Ca href=\u0022https:\/\/www.cc.gatech.edu\/news\/591425\/gears-begin-turning-new-center-dedicated-machine-learning\u0022 target=\u0022_blank\u0022\u003Emachine learning\u003C\/a\u003E and optimization techniques and applying them to challenging problems related to sustainable development goals, which closely aligns with Microsoft\u0026#39;s AI for Earth program,\u0026rdquo; said Dilkina.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EVia the \u003Ca href=\u0022https:\/\/www.microsoft.com\/en-us\/research\/academic-program\/microsoft-azure-for-research\/\u0022\u003EAzure for Research\u003C\/a\u003E AI for Earth award program, Microsoft provides selected researchers and organizations access to its cloud and AI computing resources to accelerate, improve, and expand work on climate change, agriculture, biodiversity, and water challenges.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe Georgia Tech project \u0026ndash; known as \u003Ca href=\u0022https:\/\/deeppop.github.io\/\u0022\u003EDeep Population\u003C\/a\u003E \u0026ndash; is a collaborative effort with CSE Ph.D. students \u003Cstrong\u003ECaleb Robinson\u003C\/strong\u003E and \u003Cstrong\u003EFred Hohman\u003C\/strong\u003E, as well as CSE Assistant Professor \u003Cstrong\u003EPolo Chau\u003C\/strong\u003E. Using Microsoft Azure infrastructure with Microsoft\u0026#39;s CNTK deep-learning toolkit, the team will work to train and analyze deep convolutional neural networks that can predict the population of an area using only satellite imagery from the same area.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe team will use what they learn to develop a methodology that is applicable across the globe, including in areas that have no resources for expensive census surveys.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026ldquo;We are particularly interested in human migration dynamics and would like to further use these models to study rural to urban migration trends and to document the effects of climate change,\u0026rdquo; said Dilkina.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026ldquo;Microsoft\u0026rsquo;s generous grant will support our work on creating predictive models of human populations and migrations, enabling much faster progress and spatial extent for our analysis.\u0026quot;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGeorgia Tech is among the first grant recipients of AI for Earth, first launched in July 2017. The grant process was a competitive and selective process and was awarded in recognition of the potential of the work and power of AI to accelerate progress.\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETo date, Microsoft has distributed more than 35 grants to qualifying researchers and organizations around the world. It also \u003Ca href=\u0022https:\/\/blogs.microsoft.com\/on-the-issues\/?p=56086\u0022 target=\u0022_blank\u0022\u003Erecently announced its intent to put $50 million over five years into the program\u003C\/a\u003E, enabling grant-making and educational training possible at a much larger scale.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Researchers will use new grant money to further machine learning and artificial intelligence techniques to  solve environmental challenges. "}],"uid":"32045","created_gmt":"2017-12-11 20:42:04","changed_gmt":"2017-12-11 20:42:04","author":"Ben Snedeker","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2017-12-11T00:00:00-05:00","iso_date":"2017-12-11T00:00:00-05:00","tz":"America\/New_York"},"extras":[],"hg_media":{"599785":{"id":"599785","type":"image","title":"Satellite photo tiles for Deep Population project ","body":null,"created":"1513024403","gmt_created":"2017-12-11 20:33:23","changed":"1513024403","gmt_changed":"2017-12-11 20:33:23","alt":"Satellite image in photo tiles for Deep Population project ","file":{"fid":"228661","name":"Deep Population_3.png","image_path":"\/sites\/default\/files\/images\/Deep%20Population_3.png","image_full_path":"http:\/\/www.tlwarc.hg.gatech.edu\/\/sites\/default\/files\/images\/Deep%20Population_3.png","mime":"image\/png","size":743949,"path_740":"http:\/\/www.tlwarc.hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/Deep%20Population_3.png?itok=ATu4geTR"}}},"media_ids":["599785"],"groups":[{"id":"47223","name":"College of Computing"},{"id":"576481","name":"ML@GT"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}