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.
Visual Analytics for Neural Architecture Search
Model selection is a traditional task in machine learning in which chooses a learning algorithm and a hyperparameter setting that optimizes a cost function over a given training (and optionally, validation) dataset. In order to use neural networks, there is an additional complication in the need to choose an architecture for the network. By visualizing the search and providing affordances for controlling the search, we can empower the user to steer the discovery of architectures for their usage scenario.
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
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.
Z. Wang, D. Cashman, M. Li, J. Li, M. Berger, J.A. Levine, R. Chang, C. Scheidegger, "NNCubes: Learned Structures for Visual Data Exploration" arXiv preprint arXiv:1808.08983, 2018.
Posters and Workshop Papers
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.
Best Paper, Symposium on Visualization for Data Science, IEEE Conference on Visualization, Berlin, Germany, October 2018.
3rd Place, Tufts Graduate Research Symposium, Tufts University, 2018.
Best Paper, Workshop on Visual Analytics for Deep Learning, IEEE Conference on Visualization, Phoenix, AZ, October 2017.
Provost's Fellowship, Tufts University, 2016-2018.
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
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
D. Cashman, "Color Spaces and Color Places" Tufts REU Lecture Series , Summer 2017
D. Cashman, "Big Data, Bigger Audience: A Method for Adapting Statistical Methods for a Wider Audience of Users" Tufts IGNITE , 2015
D. Cashman, "Introduction to Ruby" and "Models, Scaffolding, and Migrations", Railsbridge Boston , 2013
- DS 4200: Information Presentation and Visualization. Northeastern University. Spring 2020
- COMP 150VA: Visual Analytics Tufts University. Fall 2019
- 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
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.