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The Data Science Machine

Location: 
Stata Center, MIT CSAIL
Series: 
CAP General
Speaker: 
Kalyan Veeramachaneni
Speaker Affiliation: 
MIT CSAIL
Biography: 

Kalyan is a Research Scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL). He co-leads a group called Any Scale learning for all. The group is interested in Big data science and Machine learning. His primary research interests are in building statistical models that enable extraction of information from large amounts of data.

Vote for Kalyan to present at the annual RSA Cybersecurity conference on Cybersecurity and Artificial Intelligence. The judges will choose the top 25 proposals and then place the selected participants up for voting by the crowd. Based on the voting, the top 17 proposals will present at the conference. Voting is currently open until February 9, 2016: https://www.rsaconference.com/events/us16/crowdsourced-voting

Abstract: 
The Data Science Machine is an end-to-end software system that is able to automatically develop predictive models from relational data. The Machine was created by Max Kanter and Kalyan Verramachaneni at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. The system automates two of the most human-intensive components of a data science endeavor: feature engineering, and selection and tuning of the machine learning methods that build predictive models from those features. First, an algorithm called Deep Feature Synthesis automatically engineers features. Next, through an approach called Deep Mining, the Machine composes a generalized machine learning pipeline that includes dimensionality reduction methods, feature selection methods, clustering, and classifier design. Finally, it tunes the parameters through a Gaussian Copula Process.

Relevant links:

DSM website:
http://bit.ly/mitdsm

Kaylan Verramachaneni's website :
http://www.kalyanv.org/

FeatureLab wesbite:
http://www.featurelab.co/

Max Kanter's website:
http://www.jmaxkanter.com/

Download the podcast in iTunes:
http://apple.co/1ZNQtZ2