{"614427":{"#nid":"614427","#data":{"type":"news","title":"Georgia Tech Will Show Off Latest Research at AI\u2019s \u2018Hottest\u2019 Conference","body":[{"value":"\u003Cp\u003EIt is uncommon to hear about a machine learning and artificial intelligence (AI) conference selling out like Taylor Swift concert, but the \u003Ca href=\u0022https:\/\/nips.cc\/Conferences\/2018\u0022\u003ENeural Information Processing Systems (NeurIPS)\u003C\/a\u003E conference did just that.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe conference sold out in \u003Ca href=\u0022https:\/\/medium.com\/syncedreview\/nips-tickets-sell-out-in-less-than-12-minutes-e3aab37ab36a\u0022\u003Eless than 12 minutes\u003C\/a\u003E for its Dec. 2 - 8 gathering in Montreal, Quebec. As one of the biggest AI conferences in the world, tech companies like Google, Microsoft, and Facebook come to find new talent, while renowned researchers present their latest work.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EA large number of Georgia Tech faculty and students will be among the throngs of attendees. With 26 papers by more than 23 Georgia Tech authors and several workshops to participate in, the Yellow Jackets are one of the leading contributors to the conference program.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EByron Boots\u003C\/strong\u003E and \u003Cstrong\u003EDhruv Batra\u003C\/strong\u003E, assistant professors in the Machine Learning Center at Georgia Tech (ML@GT) and the \u003Ca href=\u0022https:\/\/www.ic.gatech.edu\/\u0022\u003ESchool of Interactive Computing,\u003C\/a\u003E are serving as area chairs.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026ldquo;We are thrilled to be a top performing university at a conference of NeurIPS\u0026rsquo; caliber. Our faculty and students continue to push boundaries and revolutionize our field, and it shows at events like this,\u0026rdquo; said \u003Cstrong\u003EIrfan Essa,\u003C\/strong\u003E \u003Ca href=\u0022http:\/\/ml.gatech.edu\/\u0022\u003EML@GT\u003C\/a\u003E director.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAs NeurIPS has increased in popularity since its first meeting in 1987, the conference receives thousands of submissions each year with a record high of 3,240 submissions in 2017. Over the years the content has shifted from examining biological and artificial neural networks and to focus more on AI, statistics, and machine learning.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EBelow is a list of Georgia Tech\u0026rsquo;s spotlight presentations, posters, and workshops being featured at NeurIPS next month.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ESpotlights\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1801.03423.pdf\u0022\u003EA Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ESampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, and Steven Wu\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1811.05016.pdf\u0022\u003ELearning Temporal Point Processes via Reinforcement Learning\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EShuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1805.10611.pdf\u0022\u003ERobust Hypothesis Testing Using Wasserstein Uncertainty Sets\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ERUI GAO, Liyan Xie, Yao Xie, Huan Xu\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1807.07531.pdf\u0022\u003ELimited Memory Kelley\u0026rsquo;s Method Converges for Composite Convex and Submodular Objectives\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ESong Zhou, Swati Gupta, and Madeleine Udell\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/www.seas.upenn.edu\/~xsi\/data\/nips18.pdf\u0022\u003ELearning Loop Invariants for Program Verification\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EXujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, and Le Song\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1807.10455.pdf\u0022\u003EAcceleration through Optimistic No-Regret Dynamics\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EJun-Kun Wang and Jacob Abernethy\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EPosters\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1805.10755.pdf\u0022\u003EDual Policy Iteration\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EWen Sun, Geoff Gordon, Byron Boots, and Drew Bagnell\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/abs\/1810.13400\u0022\u003EDifferentiable MPC for End-to-End Planning and Control\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EBrandon Amos, Jake Sacks, Ivan Dario Jimenez, Byron Boots, and Zico Kolter\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1809.08820.pdf\u0022\u003EOrthogonally Decoupled Variational Gaussian Processes\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EHugh Samilbeni, Ching-An Cheng, Byron Boots, and Marc Deisenroth\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1810.12369.pdf\u0022\u003ELearning and Inference in Hilbert Space with Quantum Graphical Models\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ESid Srinivasan, Carlton Downey, and Byron Boots\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/abs\/1811.00103\u0022\u003EThe Price of Fair PCA: One Extra Dimension\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ESamira Samadi, Uthaipon Tantipongpipat, Mohit Singh, Jamie Morgenstern, and Santosh Vempala\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1801.03423.pdf\u0022\u003EA Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ESampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, and Steven Wu\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1811.05016.pdf\u0022\u003ELearning Temporal Point Processes via Reinforcement Learning\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EShuang Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1805.10611.pdf\u0022\u003ERobust Hypothesis Testing Using Wasserstein Uncertainty Sets\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ERUI GAO, Liyan Xie, Yao Xie, Huan Xu\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1807.07531.pdf\u0022\u003ELimited Memory Kelley\u0026rsquo;s Method Converges for Composite Convex and Submodular Objectives\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ESong Zhou, Swati Gupta, and Madeleine Udell\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1810.11896.pdf\u0022\u003ESmoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ENima Anari, Amin Saberi, Wolfgang Maass, Robert Legenstein, Christos Papadimitriou, and Santosh Vempala\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/abs\/1803.06416\u0022\u003EDifferential Privacy for Growing Databases\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ERachel Cummings, Sara Krehbiel, Kevin Lai, and Uthaipon (Tao) Tantipongpipat.\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1808.10056.pdf\u0022\u003EDifferentially Private Change-Point Detection\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ERachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, and Wanrong Zhang\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/www.cs.rice.edu\/~as143\/Papers\/topkapi.pdf\u0022\u003ETopkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EAnkush Mandal, He Jiang, Anshumali Shrivastava, and Vivek Sarkar\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1810.03649.pdf\u0022\u003EOvercoming Language Priors in Visual Question Answering with Adversarial Regularization\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ESainandan Ramakrishnan, Aishwarya Agrawal, and Stefan Lee\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/abs\/1806.06004\u0022\u003EPartially Supervised Image Captioning\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EPeter Anderson, Stephen Gould, and Mark Johnson\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1805.09298.pdf\u0022\u003ELearning towards Minimum Hyperspherical Energy\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EWeiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, and Le Song\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/nips.cc\/Conferences\/2018\/Schedule?showEvent=11921\u0022\u003ECoupled Variational Bayes via Optimization Embedding\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EBo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, and Le Song\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/www.seas.upenn.edu\/~xsi\/data\/nips18.pdf\u0022\u003ELearning Loop Invariants for Program Verification\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EXujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, and Le Song\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/papers.nips.cc\/paper\/7667-cooperative-neural-networks-conn-exploiting-prior-independence-structure-for-improved-classification.pdf\u0022\u003ECooperative Neural Networks (CoNN): Exploiting Prior Independence Structure for Improved Classification\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EHarsh Shrivastava, Eugene Bart, Bob Price, Hanjun Dai, Bo Dai, Srinivas Aluru\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1803.02312.pdf\u0022\u003EDimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EMinshuo Chen, Lin Yang, Mengdi Wang, and Tuo Zhao\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1806.01660.pdf\u0022\u003ETowards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Distributed Nonconvex Stochastic Optimization\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ETianyi Liu, Shiyang Li, Jianping Shi, Enlu Zhou, and Tuo Zhao\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1612.02803.pdf\u0022\u003EThe Physical Systems behind Optimization Algorithms\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ELin Yang, Raman Arora, Vladimir Braverman, and Tuo Zhao\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1810.11098.pdf\u0022\u003EProvable Gaussian Embedding with One Observation\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EMing Yu, Zhuoran Yang, Tuo Zhao, Mladen Kolar, and Zhaoran Wang\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1805.09298.pdf\u0022\u003ELearning Towards Minimum Hyperspherical Energy\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EWeiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, and Le Song\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/pdf\/1807.10455.pdf\u0022\u003EAcceleration through Optimistic No-Regret Dynamics\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EJun-Kun Wang and Jacob Abernethy\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/arxiv.org\/abs\/1810.09593\u0022\u003EMiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EEdward Choi, Cao Xiao, Walter F. Stewart, and Jimeng Sun\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EWorkshops\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003EWorkshop on AI in Finance\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003ETucker Balch, School of Interactive Computing Professor and Associate Chair, is an invited speaker.\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/nips2018vigil.github.io\/\u0022\u003EVisually-Grounded Interaction and Language (ViGIL)\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EGeorgia Tech organizers include Erik Wijmans, Samyak Datta, Stefan Lee, Peter Anderson, Dhruv Batra, and Devi Parikh.\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/sites.google.com\/view\/nips18-ilr\u0022\u003EImitation Learning and its Challenges in Robotics\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EInteractive Computing Ph.D. student Mustafa Mukadam is organizing the workshop.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/blackinai.github.io\/\u0022\u003E2nd Black in AI Workshop\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EApplication of The Hilbert Schmit Independence Criterion to Lexical Geographic Variation in Lyon, France\u0026nbsp;by Taha Merghani\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cul\u003E\r\n\t\u003Cli\u003E\u003Ca href=\u0022https:\/\/www.wordplay2018.com\/\u0022\u003EWordplay: Reinforcement and Language Learning in Text-based Games\u003C\/a\u003E\u003C\/li\u003E\r\n\u003C\/ul\u003E\r\n\r\n\u003Cp\u003EPlaying Text-Adventure Games with Graph-Based Deep Reinforcement Learning\u0026nbsp;\u003Cbr \/\u003E\r\nPrithviraj Ammanabrolu and Mark O. Riedl\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Georgia Tech will present 26 papers at NeurIPS, a premier AI conference happening December 2-8 in Montreal, Quebec."}],"uid":"34773","created_gmt":"2018-11-19 21:10:44","changed_gmt":"2018-11-30 21:02:02","author":"ablinder6","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2018-11-19T00:00:00-05:00","iso_date":"2018-11-19T00:00:00-05:00","tz":"America\/New_York"},"extras":[],"hg_media":{"614435":{"id":"614435","type":"image","title":"NeurIPS 2018 will be held in Montreal, Quebec and is one of the premier AI conferences around the world. Photo Credit: Tourism Quebec","body":null,"created":"1542663792","gmt_created":"2018-11-19 21:43:12","changed":"1542810166","gmt_changed":"2018-11-21 14:22:46","alt":"","file":{"fid":"233926","name":"tourism-montreal-greater-montreal-convention-and-tourism-bureau-gmctb-photo.jpg","image_path":"\/sites\/default\/files\/images\/tourism-montreal-greater-montreal-convention-and-tourism-bureau-gmctb-photo.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/images\/tourism-montreal-greater-montreal-convention-and-tourism-bureau-gmctb-photo.jpg","mime":"image\/jpeg","size":86232,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/images\/tourism-montreal-greater-montreal-convention-and-tourism-bureau-gmctb-photo.jpg?itok=u8WLLEZR"}}},"media_ids":["614435"],"groups":[{"id":"576481","name":"ML@GT"},{"id":"47223","name":"College of Computing"},{"id":"1299","name":"GVU Center"},{"id":"50877","name":"School of Computational Science and Engineering"},{"id":"50875","name":"School of Computer Science"},{"id":"50876","name":"School of Interactive Computing"}],"categories":[],"keywords":[],"core_research_areas":[{"id":"39501","name":"People and Technology"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EAllie McFadden\u003C\/p\u003E\r\n\r\n\u003Cp\u003ECommunications Officer\u003C\/p\u003E\r\n\r\n\u003Cp\u003Eallie.mcfadden@cc.gatech.edu\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}