BEGIN:VCALENDAR
METHOD:PUBLISH
PRODID:-//Apple Computer\, Inc//iCal 1.0//EN
X-WR-CALNAME;VALUE=TEXT:USC
VERSION:2.0
BEGIN:VEVENT
DESCRIPTION:Speaker: Professor Nick Sahinidis, Chemical Engineering, Carnegie Mellon University
Talk Title: ALAMO: Machine learning from data and first principles
Abstract: We have developed the ALAMO methodology with the aim of producing a tool capable of using data to learn algebraic models that are accurate and as simple as possible. ALAMO relies on (a) integer nonlinear optimization to build low-complexity models from input-output data, (b) derivative-free optimization to collect additional data points that can be used to improve tentative models, and (c) global optimization to enforce physical constraints on the mathematical structure of the model. We present computational results and comparisons between ALAMO and a variety of learning techniques, including Latin hypercube sampling, simple least-squares regression, and the lasso. We also describe results from applications in CO2 capture that motivated the development of ALAMO.
Host: Dr. Joe Qin
SEQUENCE:5
DTSTART:20190409T160000
LOCATION:SLH 200
DTSTAMP:20190409T160000
SUMMARY:Mork Family Department of Chemical Engineering and Materials Science Seminar - Lyman L. Handy Colloquia
UID:EC9439B1-FF65-11D6-9973-003065F99D04
DTEND:20190409T172000
END:VEVENT
END:VCALENDAR