about
I am a CS researcher who works in a variety of areas within visual analytics, but focuses on the use of machine learning for human interpretable dimensionality reduction. I also have a strong interest in software engineering and computer science education. My CV is available here.
I completed my PhD in Computer Science at Tufts University where I worked with Professor Remco Chang at the Visual Analytics Lab. I went to undergrad at Oberlin College, where I graduated with a degree in Computer Science and Economics. I was awarded honors in both departments for my two theses: Modeling and High Performance Computing; an MPI Expansion of Nova, and The Effect of District Size on Student Performance, a Story from Illinois.
publications
Kriging Convolutional Networks
Appleby, G., Liu, L., & Liu, L.-P. (2020). Kriging Convolutional Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3187-3194.
Active Search
Mukadum, F., Nguyen, Q., Adrion, D. M., Appleby, G., Chen, R., Dang, H., Chang, R., Garnett, R., Lopez, S. A. (2021). Efficient Discovery of Visible Light-Activated Azoarene Photoswitches with Long Half-Lives Using Active Search. Journal of Chemical Information and Modeling, 61(11), 5524–5534.
Unprojection
Espadoto, M., Appleby, G., Suh, A., Cashman, D., Li, M., Scheidegger, C. E., Anderson, E. W., Chang, R., Telea, A. C. (2021). UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data. IEEE Transactions on Visualization and Computer Graphics, 29(2), 1559-1572.
HyperNP
Appleby, G., Espadoto, M., Chen, R., Goree, S., Telea, A.C., Anderson, E.W. and Chang, R. (2022), HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters. Computer Graphics Forum, 41, 169-181.
Communication Guidelines
Suh, A., Appleby, G., Anderson, E.W., Finelli, L., Chang, R., and Cashman, D. (2023), Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts. IEEE Transactions on Visualization and Computer Graphics, 1-16.
Knowledge Graphs in Practice
Li, H., Appleby, G., Brumar C.D., Chang, R., and Suh, A. (2024), Knowledge Graphs in Practice: Characterizing their Users, Challenges, and Visualization Opportunities. IEEE Transactions on Visualization and Computer Graphics, 30, 584-594.
Attention Is All They Need
Goree, S., Appleby, G., Crandall D., and Su, N.M. (2024), Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper. Proceedings of the ACM on Human-Computer Interaction, Volume 8 (CSCW2), 1-25.
LinkQ
Li, H., Appleby, G., and Suh, A. (2024), LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answering. IEEE Visualization and Visual Analytics (VIS Short).
Dimbridge
Montambault, B., Appleby, G., Rogers, J., Brumar, Camelia, C.D., Li, M., and Chang, R. (2025), DimBridge: Interactive Explanation of Visual Patterns in Dimensionality Reductions with Predicate Logic. IEEE Transactions on Visualization and Computer Graphics, 31, 207-217.
Publications that are accepted but have not been presented, are under review, were presented at workshops, or are in progress can be found in my
CV.