Dylan Cashman

dcashm01 _at_ cs _dot_ tufts _dot_ edu

PhD Student

Tufts University

Computer Science

I am a third year PhD candidate in Computer Science at Tufts University. I work under Remco Chang in the Visual Analytics Lab at Tufts (VALT).

I received my Masters of Science in Computer Science at Tufts University in 2016, and my Bachelor of Science in Mathematics at Brown University in 2010.

In the winter of 2017, I passed my Qualification Exams, taking tests in Visualization, Human Computer Interaction, and Statistical Pattern Recognition.

Before grad school, I built Ruby on Rails systems for healthcare nonprofits in the Boston area at Annkissam. During my graduate study, I have interned at MIT Lincoln Laboratory, Palo Alto Research Center, and the MIT IBM Watson AI Lab.



Visual Analytics Lab at Tufts

Current Projects

  • Visual Analytics for Human-centered Model Selection

    Human-centered model selection

    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. However, the actual deployment of a machine learning model is rarely taken into account. For some cases, we might not care so much if the model is interpretable as long as its accuracy or true-positive rate is maximized, as in a spam detector or image detection in a self-driving car. But in many use cases, such as medical outcome predictions or economic models, the user of the model would be willing to trade off some performance in exchange for interpretability or actionability. My research uses visual analytics to empower the user to select models that match their usage scenario.

  • Visualization of Deep Learning Models

    Vis For Deep Learning

    While deep learning has been adapted to enumerable domains with dramatically impressive results, the tools we have to understand the construction, training, and performance of a model are woefully obscure to non-experts. I build visual tools that aid in the interpretability of deep learning models, including Convolutional Neural Networks and Recurrent Neural Networks across images and text.


  • Vanishing GradientVanishing Gradient

    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



  • BEAMES screenshot

    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.

  • Bayesian Detection

    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.


  • Interface of NNCubes

    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.



  • Clipped Projections

    B. Kang, D. Cashman, R. Chang, J. Lijffijt, T. De Bie, "CLIPPR: Maximally Informative CLIPped PRojections with Bounding Regions" IEEE Conference on Visual Analytics for Science and Technology, 2018.

  • Visual Analytics for ML diagram

    S. Humayoun, D. Cashman, F. Heimerl, R. Chang, "A Visual Analytics Framework for Automated Machine Learning" IEEE Conference on Visual Analytics for Science and Technology, 2018.

  • Data Scientist Study

    A. Mosca, S. Robinson, M. Clarke, R. Redelmeier, S. Coates, D. Cashman, R. Chang, "Towards Data Science for the Masses: A Study of Data Scientists and their Interactions with Clients " IEEE Conference on Information Visualization, 2018.

  • Big Data, Bigger Audience

    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" VizSec, 2016. Also presented at MIT Lincoln Labs Cyber and Netcentric Workshop CNW 2017

    Extended Abstract

    Poster (PPT)


  • 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-present.


  • 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


  • 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

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


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.