The McCormick School of Engineering Department of Electrical Engineering and Computer Science invites you to the

 

CS+X Colloquium Series

 

Computer science penetrates all areas of thinking—even the social sciences, humanities, and arts. A new way of thinking—computational thinking—is emerging as a fundamental way of understanding and reimagining the entire world. It envisions all processes as computations and attempts to develop an understanding and technological mastery from this perspective. This colloquium series will help define the challenges and opportunities for both computer science and other disciplines as the scope and nature of computational thinking continue to evolve.

 

 

First in the Series

Wednesday, April 2, 2 p.m., ITW Classroom

 

McAfee Preston McAfee

Director, Google Strategic Technologies

 

Machine Learning + Economics

“Machine Learning in an Exchange Environment”

 

Machine learning involves very large regressions, with as many as a billion explanatory variables. Due to the scale, point estimates are typically used, so that common economic concepts like standard errors are ignored. The talk focuses on applications of ML in an exchange environment, as arises in internet advertising. The use of ML in an auction suggests several ways of improving ML: dealing with the winner’s curse, accommodating the inevitable prediction errors into pricing, and choosing a loss function appropriate to the application. In addition, it is shown that active learning strategies have modest payoffs.

 

Next in the Series

 

Please save the date for the upcoming lecture in this series.

 

Tuesday, April 29, 2 p.m., ITW Classroom

 

Cardelli Luca Cardelli

Principal Researcher, Microsoft Research

 

Programming Languages + Biology

“The Cell Cycle Switch Computes Approximate Majority”

 

This talk focuses on the insights that can be gained by combining the ‘dynamical systems’ and ‘reactive systems’ perspectives on biological systems, specifically focusing on how the cell cycle switch computes the well-known distributed systems algorithm: approximate majority.