Dylan Cashman

Google Scholar


Assistant Professor of Computer Science

Brandeis University

Waltham, MA

I am an assistant professor of computer science at Brandeis University. Previously, I was a senior expert in data science and advanced visual analytics in the Data and AI division at Novartis in Cambridge, MA. I also volunteer as a web chair of the organizing committee for IEEE VIS, the premier conference for visualization research.

I completed my PhD in Computer Science under Remco Chang in the Visual Analytics Lab at Tufts (VALT) in 2020.

My dissertation was entitled Bridging the Human-Machine Gap in Applied Machine Learning with Visual Analytics.

In the fall semester of 2023, I am teaching COSI 21A (Data Structures) and COSI 116A (Information Visualization). I live in beautiful Lynn, Massachusetts with my wife and two sons.


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.

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



Visual Analytics Lab at Tufts

Current Projects

  • Columnar Data Augmentation Using Knowledge Graphs

    Illustration of queries over a knowledge graph

    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.

    Read our 2020 IEEE VAST paper or see the source code.

  • The Human Role in Classical Model Selection

    PAC Learning for 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.

    Watch the thesis defense.

Selected Publications

  • A visual comparison of the performance of a new model against a baseline model

    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.


  • Inferential Tasks result in more interactions during evaluation of a visualization

    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.

    Best Paper Award


  • Unprojection of unit sphere for LLE and UMAP

    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.


  • Interface of NNCubes

    Z. Wang, D. Cashman, M. Li, J. Li, M. Berger, J.A. Levine, R. Chang, C. Scheidegger, "NeuralCubes: Deep Representations for Visual Data Exploration" IEEE Big Data, 2021.



  • CAVA screenshot

    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.




    Talk (Youtube)

    Demo Video (Youtube)

  • REMAP screenshot

    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.




    Talk (Vimeo)

  • Snowcat screenshot

    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.



    Video Fast Forward

  • 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.

    Best Paper Award


  • 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




    Talk (Vimeo)

  • 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.

Posters and Workshop Papers

  • Inferential Tasks

    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.


  • Inferential Tasks

    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.



  • Clipped Projections

    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.

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

    Extended Abstract

    Poster (PPT)


  • Best Paper, EuroVis Short Papers, Rome, Italy, June 2022.

  • 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-present.


  • D. Cashman, "Machine Learning for Visualization" Washington University in St. Louis , 2021
  • D. Cashman, "Bridging the Human-Machine Gap in Applied AI with Visual Analytics" Pacific Northwest National Lab , 2021
  • D. Cashman, "Value of Visual Analytics for Insights, Strategy, and Design" Novartis Institute for Biological Research , 2020
  • D. Cashman, "Model Selection for Data Scientists" MATLab Deep Learning Tea, 2019
  • 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



  • DS 4200: Information Presentation and Visualization. Northeastern University. Spring 2020

Guest Lecturer

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.