Current Projects
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Columnar Data Augmentation Using Knowledge Graphs
Knowledge graphs are a type of information repository that is useful for recording and querying relationships between entities. They are often used to find answers to questions about individual entities, like What is the population of Germany?, and their structure and schema make it easy to connect them with artificial intelligence agents. In my research, I have developed tools for using knowledge graphs to instead add entire columns onto tabular datasets to enrich the space of possible analysis. This can be useful for both visual analysis as well as predictive analytics, including machine learning.
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The Human Role in Classical Model Selection
It can be difficult to pin down exactly what the role of the user is in human-in-the-loop systems for machine learning. We know that users gain trust and understanding by being involved in the process. And we know that users can be valuable in labeling unlabeled data and in identifying data cleaning issues. But there are theoretical underpinnings of machine learning that can help us classify the types of errors that machine learning can have when generalizing to their deployed settings. These include model mismatches, changes in data distribution, or dependences within the data sampling. Looking into learning theory may provide theoretical foundations for why visualization is such a useful aspect of model selection.
Read my thesis, Bridging the Human-Machine Gap in Applied Machine Learning with Visual Analytics.
Selected Publications
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A. Suh, G. Appleby, E.W. Anderson, L. Finelli, R. Chang, D. Cashman. Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts. IEEE Transactions on Visualization and Computer Graphics, 2023.
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A. Suh, A. Mosca, S. Robinson, Q. Pham, D. Cashman, A. Ottley R. Chang. Inferential tasks as an evaluation technique for visualization. EuroVis Short Papers, 2022.
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M. Espadoto, G. Appleby, A. Suh, D. Cashman, M. Li, C. Scheidegger, E. W. Anderson, R. Chang, A. C. Telea. Unprojection: Leveraging inverse-projections for visual analytics of high-dimensional data. Transactions on Visualization and Computer Graphics (TVCG), 2021.
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D. Cashman, S. Xu, S. Das, F. Heimerl, C. Liu, S. Humayoun, M. Gleicher, A. Endert, R. Chang. CAVA: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge Graphs. Transactions on Visualization and Computer Graphics (TVCG), 2020.
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D. Cashman, A. Perer, R. Chang, H. Strobelt. Ablate, variate, and contemplate: Visual analytics for discovering neural architectures. Transactions on Visualization and Computer Graphics (TVCG), 2019.
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D. Cashman, S. Humayoun, F. Heimerl, K. Park, S. Das, J. Thompson, B. Saket, A. Mosca, J. Stasko, A. Endert, M. Gleicher, R. Chang. A User-based Visual Analytics Workflow for Exploratory Model Analysis. Computer Graphics Forum (CGF), 2019.
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S. Das, D. Cashman, R. Chang, A. Endert, "BEAMES: Interactive Multi-Model Steering, Selection, and Inspection for Regression Tasks " Symposium on Visualization in Data Science (at IEEE VIS), 2018.
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D. Cashman, G. Patterson, A. Mosca, N. Watts, S. Robinson, R. Chang, "RNNbow: Visualizing Learning via Backpropagation Gradients in RNNs" IEEE Computer Graphics and Applications, 2018.
D. Cashman, G. Patterson, A. Mosca, R. Chang, "RNNbow: Visualizing Learning via Backpropagation Gradients in Recurrent Neural Networks" Workshop on Visual Analytics for Deep Learning (at IEEE VIS), 2017.
Best Paper Award
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B. Price, L. Price, D. Cashman, M. Nabi, "Efficient Bayesian Detection of Disease Onset in Truncated Medical Data" IEEE International Conference on Healthcare Informatics, 2017.
Posters and Workshop Papers
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A. Suh, G. Appleby, E. W. Anderson, L. Finelli, D. Cashman, "Communicating performance of regression modelsusing visualization in pharmacovigilance." Workshop on Visual Analytics in Healthcare at IEEE VIS, 2021.
D. Cashman, Y. Wu, R. Chang, A. Ottley, "Inferential Tasks as a Data-Rich Evaluation Method for Visualization" Workshop on Evaluation of Interactive Visual Machine Learning Systems at IEEE VIS, 2019.
B. Kang, D. Cashman, R. Chang, J. Lijffijt, T. De Bie, "CLIPPR: Maximally Informative CLIPped PRojections with Bounding Regions" Posters for IEEE Conference on Visual Analytics for Science and Technology, 2018.
D. Cashman, S. Kelley, D. Staheli, C. Fulcher, M. Procopio, R. Chang, "Big Data, Bigger Audience: A Meta-algorithm for Making Machine Learning Actionable for Analysts" Posters for VizSec, 2016. Also presented at MIT Lincoln Labs Cyber and Netcentric Workshop CNW 2017
Awards
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Best Paper, EuroVis Short Papers, Rome, Italy, June 2022.
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Best Paper, Symposium on Visualization for Data Science, IEEE Conference on Visualization, Berlin, Germany, October 2018.
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3rd Place, Tufts Graduate Research Symposium, Tufts University, 2018.
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Best Paper, Workshop on Visual Analytics for Deep Learning, IEEE Conference on Visualization, Phoenix, AZ, October 2017.
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Provost's Fellowship, Tufts University, 2016-present.
Talks
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D. Cashman, "Machine Learning for Visualization" Washington University in St. Louis , 2021
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D. Cashman, "Bridging the Human-Machine Gap in Applied AI with Visual Analytics" Pacific Northwest National Lab , 2021
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D. Cashman, "Value of Visual Analytics for Insights, Strategy, and Design" Novartis Institute for Biological Research , 2020
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D. Cashman, "Model Selection for Data Scientists" MATLab Deep Learning Tea, 2019
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D. Cashman, G. Patterson, A. Mosca, N. Watts, S. Robinson, R. Chang, "RNNbow: Visualizing Learning via Backpropagation Gradients in Recurrent Neural Networks" Tufts Graduate Research Symposium , 2018
3rd Place
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D. Cashman, F. Yang, J. Chandler, A. Mosca, M. Iori, T. August, R. Chang, "Chasing Waldo: Implicit Recovery of User Behavior and Intent from User Interaction Logs" Tufts Graduate Research Symposium , 2017
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D. Cashman, "Color Spaces and Color Places" Tufts REU Lecture Series , Summer 2017
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D. Cashman, "Big Data, Bigger Audience: A Method for Adapting Statistical Methods for a Wider Audience of Users" Tufts IGNITE , 2015
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D. Cashman, "Introduction to Ruby" and "Models, Scaffolding, and Migrations", Railsbridge Boston , 2013
Teaching
Lecturer
- DS 4200: Information Presentation and Visualization. Northeastern University. Spring 2020
Guest Lecturer
- COMP 150VA: Visual Analytics Tufts University. Fall 2019
Teaching Assistant
- COMP 40: Machine Structure and Assembly Language Programming. Tufts University. Fall 2016, Spring 2017
- COMP 61: Discrete Math. Tufts University. Fall 2015
- Math 0520: Linear Algebra. Brown University. Spring 2008
- Math 0200: Multivariable Calculus. Brown University. Fall 2008, Fall 2009
- Math 0190: Calculus II. Brown University. Fall 2007
Hobbies
I used to play classical upright bass; I still play guitar sometimes. I'm a big music guy and I try to constantly expand what I'm listening to, both in genre and in time period. I like reading and I try to alternate between something fun and something important. My brothers and cousins and I all have a scheduled night every two weeks to play some dumb online videogames together. I was really into pickup basketball, but I'm afraid I'll hurt my knees if I play too frequently.
Oh, and watching TV series over and over again. Way too many times.