Talks

"Set" is a pattern-matching card game. In this project we describe a Mathematica program to play Set over a USB video camera. This requires some interesting image processing, including developing a shape classifier using a probabilistic machine learning algorithm called multinomial logistic regression.

A tour of the Controls Lab at the University of California at Santa Barbara, for incoming first-year undergraduate students in Electrical and Computer Engineering.

Can a Beaglebone Black balance a pendulum? The answer is a resounding yes… thanks to our friend, Control Theory. In this talk, we present an overview of the Quanser pendulum hardware, the Beaglebone Black, and state-feedback controllers.

What do an airplane, a pancreas, and a warehouse have in common? It’s no joke: the answer is that they are all systems whose behavior we want to control. In Control Theory, we use math to describe how systems behave, then design algorithms to control them.

This talk introduces the Kalman Filter: an algorithm for efficiently estimating the state of a system from noisy measurements. Presented at the Dynamical Neuroscience Symposium, University of California, Santa Barbara.

"Why should I learn Mathematica when I already have to learn Matlab??"

This talk, geared toward college freshman engineering students, answers this question by highlighting Mathematica’s strengths in comparison to Matlab. It’s meant as a short introduction to whet the appetites of students eager to expand their engineering toolboxes.

An electrical and computer engineering graduate student researcher shares his insights from academics and industry about how he uses Mathematica and the Wolfram Language, and how it compares to Matlab.

The Kalman Filter allows control engineers to employ celebrated state-­feedback controllers even when the system’s state was not directly measurable, in a plug­-and-­play sort of way. This talk explains the basics of control theory and linear systems, describes the Kalman Filter, and shows some cool examples. Presented at SIAM Seminar on Top Algorithms in Computational Science.

UCSB electrical and computer engineering graduate student Justin Pearson shows how engineering equations can be both simplified and physically insightful using the Wolfram Language as compared to procedures in MATLAB.