@article {DOI261,
	title = {Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy Models},
	journal = {ACM Transactions on Knowledge Discovery from Data},
	volume = {12},
	number = {7},
	year = {2018},
	month = {02/2018},
	doi = {10.1145/3047017},
	author = {Hao Wu and Sun, Maoyuan and Peng Mi and Nikolaj Ta and North, Chris and Ramakrishnan, Naren}
}
@article {DOI252,
	title = {AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets},
	journal = {Informatics},
	volume = {3},
	year = {2016},
	month = {10/2016},
	pages = {18},
	publisher = {MDPI},
	edition = {Special Issue on Information Visualization for Massive Data},
	doi = {10.3390/informatics3040018},
	url = {http://www.mdpi.com/2227-9709/3/4/18},
	author = {Peng Mi and Sun, Maoyuan and Moeti Masiane and Yong Cao and North, Chris}
}
@article {DOI10.3390/informatics3040023 ,
	title = {Interactive Graph Layout of a Million Nodes},
	journal = {Informatics},
	volume = {3},
	year = {2016},
	month = {12/2016},
	pages = {23},
	edition = {Special Issue on Information Visualization for Massive Data},
	doi = {10.3390/informatics3040023 },
	url = {http://www.mdpi.com/2227-9709/3/4/23},
	author = {Peng Mi and Sun, Maoyuan and Moeti Masiane and Yong Cao and North, Chris}
}
@conference {DOI246,
	title = {Usability Challenges underlying Bicluster Interaction for Sensemaking},
	booktitle = {CHI 2016 Workshop on Human Centred Machine Learning},
	year = {2016},
	month = {05/2016},
	pages = {6 pages},
	author = {Sun, Maoyuan and Peng Mi and Hao Wu and North, Chris and Ramakrishnan, Naren}
}
@conference {Wang2014b,
	title = {Making Sense of Daily Life Data: From Commonalities To Anomalies},
	booktitle = {VAST Challenge 2014},
	year = {2014},
	abstract = {We report the approach and results to solve the VAST 2014 Mini-Challenge 2 (MC2). Based on the commercial interactive visualization software Tableau, we follow the notional model of sensemaking loop for analysis of the massive multi-dimensional, multi-source and time-varying data sets in MC2. Our findings show that we can effectively identify the commonalities and anomalies to understand the GAStech employees{\textquoteright} daily life.},
	keywords = {Geo-visualization, human information interaction, Intelligence Analysis, sense-makingloop, visualanalysis},
	author = {Wang, Ji and Peng Mi and North, Chris}
}
@conference {DOI140,
	title = {Dynamic Analysis of Large Datasets with Animated and Correlated Views},
	booktitle = {IEEE VAST 2012 (Extended Abstract) (Honorable Mention for Good Use of Coordinated Displays)},
	year = {2012},
	abstract = {In this paper, we introduce a GPU-accelerated visual analytics tool, AVIST. By adopting the in-situ visualization architecture on the GPUs, AVIST supports real-time data analysis and visualization of massive scale datasets, such as VAST 2012 Challenge dataset. The design objective of the tool is to identify temporal patterns from large and complex data. To achieve this goal, we introduce three unique features: automatic animation, disjunctive data filters, and time-synced visualization of multiple datasets.},
	author = {Yong Cao and Reese Moore and Peng Mi and Endert, Alex and North, Chris and Randy Marchany}
}
