Tandy Warnow - "Theoretical and Empirical Advances in Large-Scale Species Tree Estimation" | DSI

Skip to main content

More on this topic

BlogMar 13, 2025

Data Ecology Faculty Co-Director Bridget Fahey Writes About Congressional Authority Over Government Data

Figures 1a-b show the general strategy: during a training period (fig. 1a), a time-dependent state is imposed on the system. This is pictured by an origami bird whose wings are periodically moved by an external agent. After training (fig. 1b), a retrieval period during which the physical system evolves under the learned dynamics should lead to a dynamic state that matches the training as best as possible. (fig. 1c and 1d) Demonstration of the training and retrieval procedure using a programmable LEGO toy. The angular positions of two motors are imposed by hand during a training phase (fig. 1c), during which couplings between the motors are learned. Dynamics during retrieval, with learned couplings, can produce fixed points as well as dynamic states where the angles constantly change (fig. 1d). See this video for a detailed description.

BlogSep 03, 2024

Developing a smart material that can learn from example

Fig 1. Percentage of broadband-serviceable locations (BSLs) with at least one submitted challenge per US state or territory. Nebraska had strong participation in the challenge process, followed by Virginia, New Mexico, and Maine. Most states and territories have low participation rates.

BlogAug 29, 2024

The Challenging Path to Internet Broadband Access Equity

BlogJul 29, 2024

Does Political Polarization Really Undermine Democracy? Maybe Not