Author: Caitlin Iverson

How Can Machine Learning Improve Educational Technologies?

by Blake Mason, John Vito Binzak and Fangyun (Olivia) Zhao  As computer-based technologies continue to establish a significant presence in modern education, it becomes increasingly important to understand how to improve the ways that these technologies present educational content and

Posted in LUCID, Machine Learning

Interactive Science Communication

 by Scott Sievert and Purav “Jay” Patel Scientific research is still communicated with static text and images despite innovations in learning theories and technology. We are envisioning a future in which interactive simulations and visualizations are used to enhance how

Posted in LUCID, Machine Learning, Resources, Uncategorized

Singular Value Decomposition (SVD)

By Lowell Thompson and Ashley Hou This is a collaborative tutorial aimed at simplifying a common machine learning method known as singular value decomposition. Learn how these techniques impact computational neuroscience research as well! Singular value decomposition is a method

Posted in LUCID, Machine Learning, Resources

What is a Computational Cognitive Model?

By Rui Meng and Ayon Sen A computational cognitive model explores the essence of cognition and various cognitive functionalities through developing detailed, process-based understanding by specifying corresponding computation models. Sylvain Baillet discusses various aspects of cognitive computation models Computational model

Posted in LUCID Library, Resources

Martina Rau on Learning with Visuals

Lucid Faculty, Educational Psychology Professor, Director of Learning, Representations, & Technology Lab as well as Computer Sciences Affiliate, Martina Rau is interested in educational technologies to support more effective learning with visuals. While we generally think of visuals as helpful

Posted in LUCID Library, Resources

Poolmate: Pool-Based Machine Teaching

Poolmate provides a command-line interface to algorithms for searching teaching sets among a candidate pool. Poolmate is designed to work with any learner which can be communicated with through a file-based API. Developed by Ara Vartanian (, Scott Alfeld (,

Posted in Resources

LUCIDtalks: Machine Learning with Ayon Sen

As part of our LUCID program, the graduate students meet weekly and discuss topics that are interesting to both the computation and behavioral graduate students. In this video Ayon provides an overview of Machine Learning in 20 minutes. Ayon explains linear

Posted in LUCID, LUCID Library, Machine Learning

Apply to LUCID!

We are currently accepting applications for Fall 2018! Please read at our LUCID overview and diversity statement Details for how to apply to the LUCID program at UW–Madison: 1. Apply to UW-Madison at Graduate School 2. Apply to a Ph.D. program

Posted in LUCID

Watch Our LUCID Video

Our new LUCID video demonstrates our collaborative learning approach to tackling the real-world projects. This video highlights the LUCID program and focusses on four pedagogical approaches: interdisciplinary groups, practical problems, prof-and-peer mentoring, and science communication.

Posted in LUCID


HAMLET  (Human, Animal, and Machine Learning: Experiment and Theory) HAMLET is an interdisciplinary proseminar series that started in 2008. The goal is to provide behavioral and computational graduate students with a common grounding in the learning sciences. Guest speakers give a

Posted in Events