@conference {9833128,
	title = {Exploring Organization of Computational Notebook Cells in 2D Space},
	booktitle = {2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)},
	year = {2022},
	month = {09/2022},
	pages = {1-6},
	doi = {10.1109/VL/HCC53370.2022.9833128},
	author = {Jesse Harden and Christman, Elizabeth and Nurit Kirshenbaum and Wenskovitch, John and Jason Leigh and North, Chris}
}
@article {DOI329,
	title = {Bridging cognitive gaps between user and model in interactive dimension reduction},
	journal = {Visual Informatics},
	volume = {53},
	year = {2021},
	pages = {13-25},
	author = {Ming Wang and Wenskovitch, John and House, Leanna and Nicholas Polys and North, Chris}
}
@article {DOI10.1109/TVCG.2020.3028890,
	title = {An Examination of Grouping and Spatial Organization Tasks for High-Dimensional Data Exploration},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {27},
	year = {2021},
	month = {1/2021},
	pages = {1742-1752},
	issn = {1077-2626},
	doi = {10.1109/TVCG.2020.3028890},
	author = {Wenskovitch, John and North, Chris}
}
@article {9153297,
	title = {Interactive Artificial Intelligence: Designing for the "Two Black Boxes" Problem},
	journal = {IEEE Computer},
	volume = {53},
	year = {2020},
	month = {07/2020},
	pages = {29-39},
	doi = {10.1109/MC.2020.2996416},
	author = {Wenskovitch, John and North, Chris}
}
@conference {DOI324,
	title = {NetReAct: Interactive Learning for Network Summarization},
	booktitle = {NeurIPS 2020 Workshop on Human and Model in the Loop Evaluation and Training Strategies (HAMLETS)},
	year = {2020},
	month = {12/2020},
	author = {Amiri, Sorour and Adhikari, Bijaya and Wenskovitch, John and Rodriguez, Alexander and Michelle Dowling and North, Chris and Prakash, Aditya}
}
@conference {10.1145/3377325.3377516,
	title = {With Respect to What? Simultaneous Interaction with Dimension Reduction and Clustering Projections},
	booktitle = {Proceedings of the 25th International Conference on Intelligent User Interfaces},
	series = {IUI {\textquoteright}20},
	year = {2020},
	month = {03/2020},
	pages = {177{\textendash}188},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	keywords = {clustering, dimension reduction, interaction, Visual Analytics},
	isbn = {9781450371186},
	doi = {10.1145/3377325.3377516},
	url = {https://doi.org/10.1145/3377325.3377516},
	author = {Wenskovitch, John and Michelle Dowling and North, Chris}
}
@conference {DOI294,
	title = {Albireo: An Interactive Tool for Visually Summarizing Computational Notebook Structure},
	booktitle = {2019 Symposium on Visualization in Data Science (VDS{\textquoteright}19)},
	year = {2019},
	month = {10/2019},
	author = {Wenskovitch, John and Zhao, Jian and Carter, Scott and Cooper, Matthew and North, Chris}
}
@conference {DOI287,
	title = {DeepVA: Bridging Cognition and Computation through Semantic Interaction and Deep Learning},
	booktitle = {Proceedings of the IEEE VIS Workshop MLUI 2019: Machine Learning from User Interactions for Visualization and Analytics. VIS{\textquoteright}19. },
	year = {2019},
	month = {10/2019},
	author = {Yali Bian and Wenskovitch, John and North, Chris}
}
@article {DOWLING201949,
	title = {Interactive Visual Analytics for Sensemaking with Big Text},
	journal = {Big Data Research},
	volume = {16},
	year = {2019},
	month = {July/2019},
	pages = {49 - 58},
	abstract = {Analysts face many steep challenges when performing sensemaking tasks on collections of textual information larger than can be reasonably analyzed without computational assistance. To scale up such sensemaking tasks, new methods are needed to interactively integrate human cognitive sensemaking activity with machine learning. Towards that goal, we offer a human-in-the-loop computational model that mirrors the human sensemaking process, and consists of foraging and synthesis sub-processes. We model the synthesis loop as an interactive spatial projection and the foraging loop as an interactive relevance ranking combined with topic modeling. We combine these two components of the sensemaking process using semantic interaction such that the human{\textquoteright}s spatial synthesis actions are transformed into automated foraging and synthesis of new relevant information. Ultimately, the model{\textquoteright}s ability to forage as a result of the analyst{\textquoteright}s synthesis activities makes interacting with big text data easier and more efficient, thereby facilitating analysts{\textquoteright} sensemaking ability. We discuss the interaction design and theory behind our interactive sensemaking model. The model is embodied in a novel visual analytics prototype called Cosmos in which analysts synthesize structure within the larger corpus by directly interacting with a reduced-dimensionality space to express relationships on a subset of data. We then demonstrate how Cosmos supports sensemaking tasks with a realistic scenario that investigates the affect of natural disasters in Adelaide, Australia in September 2016 using a database of over 30,000 news articles.},
	keywords = {Big data, interactive visual analytics, Semantic interaction, text analytics, Topic modeling, visualization},
	issn = {2214-5796},
	doi = {https://doi.org/10.1016/j.bdr.2019.04.003},
	url = {http://www.sciencedirect.com/science/article/pii/S2214579618302995},
	author = {Michelle Dowling and Nathan Wycoff and Brian Mayer and Wenskovitch, John and Leman, Scotland and House, Leanna and Nicholas Polys and North, Chris and Peter Hauck}
}
@conference {DOI298,
	title = {Machine Learning from Interaction in Multi-Model Visual Analytics},
	booktitle = {Proceedings of the ACM CHI Conference Workshop on Human-Centered Machine Learning Perspectives at CHI{\textquoteright}19.},
	year = {2019},
	month = {04/2019},
	author = {Wenskovitch, John and North, Chris}
}
@conference {DOI296,
	title = {Machine Learning from User Interaction for Visualization and Analytics: A Workshop-Generated Research Agenda},
	booktitle = {Proceedings of the IEEE VIS Workshop MLUI 2019: Machine Learning from User Interactions for Visualization and Analytics. VIS{\textquoteright}19.},
	year = {2019},
	month = {10/2019},
	author = {Wenskovitch, John and Michelle Dowling and Grose, Laura and North, Chris and Chang, Remco and Endert, Alex and Rogers, David}
}
@conference {DOI293,
	title = {Pollux: Interactive Cluster-First Projections of High-Dimensional Data},
	booktitle = {2019 Symposium on Visualization in Data Science (VDS{\textquoteright}19)},
	year = {2019},
	month = {10/2019},
	author = {Wenskovitch, John and North, Chris}
}
@conference {Wenskovitch:2019:SID:3308557.3308718,
	title = {Simultaneous Interaction with Dimension Reduction and Clustering Projections},
	booktitle = {Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion},
	series = {IUI {\textquoteright}19},
	year = {2019},
	month = {03/2019},
	pages = {89{\textendash}90},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	keywords = {clustering, dimension reduction, interaction, Visual Analytics},
	isbn = {978-1-4503-6673-1},
	doi = {10.1145/3308557.3308718},
	url = {http://doi.acm.org/10.1145/3308557.3308718},
	author = {Wenskovitch, John and Michelle Dowling and North, Chris}
}
@article {doi:10.1080/0144929X.2019.1616223,
	title = {Towards insight-driven sampling for big data visualisation},
	journal = {Behaviour \& Information Technology},
	year = {2019},
	month = {05/2019},
	pages = {1-20},
	publisher = {Taylor \& Francis},
	doi = {10.1080/0144929X.2019.1616223},
	url = {https://doi.org/10.1080/0144929X.2019.1616223},
	author = {Moeti M. Masiane and Anne Driscoll and Wuchun Feng and Wenskovitch, John and North, Chris}
}
@proceedings {kodali2019uncertainty,
	title = {Uncertainty in Interactive WMDS Visualizations},
	journal = {2019 Symposium on Visualization in Data Science Posters},
	year = {2019},
	address = {Vancouver, BC, Canada},
	abstract = {Visualizations are useful when learning from high-dimensional data. However, visualizations can be misleading when they do not incorporate measures of uncertainty; e.g., uncertainty from the data or the dimension reduction algorithm used to create the visual display. In our work, we extend a framework called Bayesian Visual Analytics (BaVA) on a dimension reduction algorithm, Weighted Multidimensional Scaling (WMDS), to incorporate uncertainty as analysts explore data visually. BaVA-WMDS visualizations are interactive, and possible interactions include manipulating variable weights and/or the coordinates of the two-dimensional projection. Uncertainty exists in these visualizations on the variable weights, the user interactions, and the fit of WMDS. We quantify these uncertainties using Bayesian models exploring randomness in both coordinates and weights in a method we call Interactive Probabilistic WMDS (IP-WMDS). Specifically, we use posterior estimates to assess fit of WMDS, the range of motion of coordinates, as well as variability in weights. Visually, we display such uncertainty in the form of color and ellipses, and practically, these uncertainties reflect trust in fitting a dimension reduction algorithm. Our results show that these displays of uncertainty highlight different aspects of the visualization, which can help inform analysts.},
	keywords = {poster},
	author = {Lata Kodali and Wenskovitch, John and Nathan Wycoff and House, Leanna and North, Chris}
}
@article {DOI269,
	title = {Be the Data: Embodied Visual Analytics},
	journal = {IEEE Transactions on Learning Technologies},
	volume = {11},
	year = {2018},
	pages = {81-95},
	doi = {10.1109/TLT.2017.2757481},
	author = {Xin Chen and Self, Jessica Zeitz and House, Leanna and Wenskovitch, John and Sun, Maoyuan and Nathan Wycoff and Jane Robertson Evia and Leman, Scotland and North, Chris}
}
@conference {DOI282,
	title = {A Bidirectional Pipeline for Semantic Interaction},
	booktitle = {VIS Workshop on Machine Learning from User Interaction for Visualization and Analytics},
	year = {2018},
	month = {10/2018},
	author = {Michelle Dowling and Wenskovitch, John and Peter Hauck and Adam Binford and Nicholas Polys and North, Chris}
}
@conference {wenskovitch2018computational,
	title = {The Cognitive and Computational Benefits and Limitations of Clustering for Sensemaking},
	booktitle = {CHI {\textquoteright}18 Workshop on Sensemaking in a Senseless World},
	year = {2018},
	month = {04/2018},
	address = {Montreal, QC, Canada},
	abstract = {The cognitive process of sensemaking refers to acquiring, representing, and organizing information in order to understand that information.  The organization component naturally supports the introduction of clusters, an important enabler for grouping objects such that similar objects are placed in the same cluster.  This paper explores the benefits and limitations of introducing clusters into systems for exploratory data analysis.  We consider these issues for tasks that the system may support, methods for visualizing and interacting with data in the system, and algorithms that are encoded into the system.  We discuss the use of clusters in these systems with respect to cognition and computation, and we call out future areas of research in this area.},
	keywords = {clustering, exploratory data analysis, interaction, sensemaking, tasks, visualization},
	author = {Wenskovitch, John and Michelle Dowling and North, Chris}
}
@unpublished {dowling2018construction,
	title = {Construction and Usage of the Semantic Interaction Pipeline},
	year = {2018},
	pages = {1-29},
	publisher = {InfoVis Lab, Virginia Tech},
	type = {Technical Report},
	address = {Blacksburg, VA},
	abstract = {Semantic interaction techniques in visual data analytics allow users to indirectly adjust model parameters by directly manipulating the visual output of the models.  Many existing tools that support semantic interaction do so with a number of similar features, including using an underlying bidirectional pipeline, using a series of statistical models, and performing inverse computations to transform user interactions into model updates. We propose a visual analytics pipeline that captures these necessary features of semantic interactions.  Our flexible, multi-model, bidirectional pipeline has modular functionality to enable rapid prototyping. This enables quick alterations to the type of data being visualized, models for transforming the data, semantic interaction methods, and visual encodings. To demonstrate how this pipeline can be used, we developed a series of applications that employ semantic interactions.  We also discuss how the pipeline can be used or extended for future research on semantic interactions in visual analytics.},
	author = {Michelle Dowling and Wenskovitch, John and Peter Hauck and Adam Binford and Theo Long and Nicholas Polys and North, Chris}
}
@conference {wenskovitch2018effect,
	title = {The Effect of Semantic Interaction on Foraging in Text Analysis},
	booktitle = {2018 IEEE Conference on Visual Analytics Science and Technology (VAST)},
	year = {2018},
	abstract = {Completing text analysis tasks is a continuous sensemaking loop of foraging for information and incrementally synthesizing it into hypotheses. Past research has shown the advantages of using spatial workspaces as a means for synthesizing information through externalizing hypotheses and creating spatial schemas. However, spatializing the entirety of datasets becomes prohibitive as the number of documents available to the analysts grows, particularly when only a small subset are relevant to the task at hand. StarSPIRE is a visual analytics tool designed to explore collections of documents, leveraging users{\textquoteright} semantic interactions to steer (1) a synthesis model that aids in document layout, and (2) a foraging model to automatically retrieve new relevant information. In contrast to traditional keyword search foraging (KSF), "semantic interaction foraging" (SIF) occurs as a result of the user{\textquoteright}s synthesis actions. To quantify the value of semantic interaction foraging, we use StarSPIRE to evaluate its utility for an intelligence analysis sensemaking task. Semantic interaction foraging accounted for 26\% of useful documents found, and it also resulted in increased synthesis interactions and improved sensemaking task performance by users in comparison to only using keyword search.},
	author = {Wenskovitch, John and Lauren Bradel and Michelle Dowling and House, Leanna and North, Chris}
}
@article {self2018observation,
	title = {Observation-Level and Parametric Interaction for High-Dimensional Data Analysis},
	journal = {ACM Transactions on Interactive Intelligent Systems},
	volume = {8},
	year = {2018},
	month = {07/2018},
	doi = {10.1145/3158230},
	author = {Self, Jessica Zeitz and Michelle Dowling and Wenskovitch, John and Ian Crandell and Ming Wang and House, Leanna and Leman, Scotland and North, Chris}
}
@conference {dowling2018sirius,
	title = {SIRIUS: Dual, Symmetric, Interactive Dimension Reductions},
	booktitle = {2018 IEEE Conference on Visual Analytics Science and Technology (VAST)},
	year = {2018},
	month = {Oct},
	abstract = {Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst{\textquoteright}s perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights.},
	keywords = {attribute projection, dimension reduction, exploratory data analysis, observation projection, Semantic interaction},
	author = {Michelle Dowling and Wenskovitch, John and J.T. Fry and Leman, Scotland and House, Leanna and North, Chris}
}
@article {wenskovitch2018towards,
	title = {Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {24},
	number = {01},
	year = {2018},
	month = {01/2018},
	pages = {131-141},
	keywords = {Algorithm design and analysis, clustering, Clustering algorithms, Data visualization, Dimension reduction;algorithms, Manifolds, Partitioning algorithms, Visual Analytics, visualization},
	issn = {1077-2626},
	doi = {10.1109/TVCG.2017.2745258},
	author = {Wenskovitch, John and Ian Crandell and Ramakrishnan, Naren and House, Leanna and Leman, Scotland and North, Chris}
}
@conference {wenskovitch2017oli,
	title = {Observation-Level Interaction with Clustering and Dimension Reduction Algorithms},
	booktitle = {Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics},
	series = {HILDA{\textquoteright}17},
	year = {2017},
	pages = {14:1{\textendash}14:6},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	keywords = {data clustering, Observation-Level Interaction (OLI), Semantic interaction, sensemaking, Visual Analytics},
	isbn = {978-1-4503-5029-7},
	doi = {10.1145/3077257.3077259},
	url = {http://doi.acm.org/10.1145/3077257.3077259},
	author = {Wenskovitch, John and North, Chris}
}
