Ab Mosca
Ab Mosca
Northeastern University
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Cited by
Cited by
Rnnbow: Visualizing learning via backpropagation gradients in rnns
D Cashman, G Patterson, A Mosca, N Watts, S Robinson, R Chang
IEEE Computer Graphics and Applications 38 (6), 39-50, 2018
A User‐based Visual Analytics Workflow for Exploratory Model Analysis
D Cashman, SR Humayoun, F Heimerl, K Park, S Das, J Thompson, ...
Computer Graphics Forum 38 (3), 185-199, 2019
At a glance: Pixel approximate entropy as a measure of line chart complexity
G Ryan, A Mosca, R Chang, E Wu
IEEE transactions on visualization and computer graphics 25 (1), 872-881, 2018
Impact of cognitive biases on progressive visualization
M Procopio, A Mosca, C Scheidegger, E Wu, R Chang
IEEE Transactions on Visualization and Computer Graphics 28 (9), 3093-3112, 2021
Visual analytics for automated model discovery
D Cashman, SR Humayoun, F Heimerl, K Park, S Das, J Thompson, ...
arXiv preprint arXiv:1809.10782, 2018
Does interaction improve bayesian reasoning with visualization?
A Mosca, A Ottley, R Chang
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021
Tipping the balance: The Balancing Incentive Program and state progress on rebalancing their long-term services and supports
RS Lester, CV Irvin, A Mosca, C Bradnan
National Evaluation of the Money Follows the Person (MFP) Demonstration …, 2015
Defining an Analysis: A Study of Client-Facing Data Scientists.
A Mosca, S Robinson, M Clarke, R Redelmeier, S Coates, D Cashman, ...
EuroVis (Short Papers), 73-77, 2019
Predicting phenotype from multi-scale genomic and environment data using neural networks and knowledge graphs
AE Thessen, R Bartelme, M Behrisch, EJ Cain, R Chang, I Debnath, ...
2020 ESA Annual Meeting (August 3-6), 2020
A Grammar for Hypothesis-Driven Visual Analysis
A Suh, Y Jiang, A Mosca, E Wu, R Chang
arXiv preprint arXiv:2204.14267, 2022
Inferential Tasks as an Evaluation Technique for Visualization
A Suh, A Mosca, S Robinson, Q Pham, D Cashman, A Ottley, R Chang
arXiv preprint arXiv:2205.05712, 2022
Evaluating Countable Texture Elements to Represent Bathymetric Uncertainty
C Ware, C Kastrisios
EuroVis 2022-Short Papers, 97-1015, 2022
Predicting Phenotype from Multi-Scale Genomic and Environment Data using Neural Networks and Knowledge Graphs: An Introduction to the NSF GenoPhenoEnvo Project
A Thessen, M Behrisch, E Cain, R Chang, B Heidorn, P Jaiswal, ...
Plant and Animal Genome XXVIII Conference (January 11-15, 2020), 2020
How Good is Your Machine Translation? Quality Estimation for Direct User Feedback
A Brennen, A Mosca, R Chang, N Lopatina
Towards Data Science for the Masses: A Study of Data Scientists and their Interactions with Clients
A Mosca, S Robinson, M Clarke, R Redelmeier, S Coates, D Cashman, ...
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