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Computing That Serves

Deep Learning: Theory, Algorithms, and Applications to the Natural Sciences

Date: 

Thursday, March 26, 2015 - 11:00am

Speaker: 

Pierre Baldi

Host: 

Christophe Giraud-Carrier

Colloquium presented by Dr. Pierre Baldi, Chancellor's Professor of Computer Science at the University of California Irvine
Thursday, March 26, 2015 at 11:00 AM
Location: 1170 TMCB

Abstract:

Machine learning has been one of the main success stories of computer science over
the last few decades. Today, the cutting edge of machine learning is deep learning, 
and deep learning has been key to designing intelligent systems that can leverage big
data to address a host of engineering applications ranging from computer vision, to
robotics, to natural language understanding, and to speech recognition.

We will present recent developments in the theory of deep learning and the
application of deep learning methods to several problems in the natural sciences including:
(1) the detection of exotic particles in high-energy physics;
(2) the prediction of the physical, chemical, and biological
properties of small molecules;
(3) the prediction of chemical reactions; and
(4) the prediction of the structural features and 3D structures of proteins.

Biography: 

Pierre Baldi earned MS degrees in Mathematics and Psychology from the University of Paris and a PhD in Mathematics from the California Institute of Technology. He is currently Chancellor's Professor in the Department of Computer Science, Director of the Institute for Genomics and Bioinformatics, and Associate Director of the Center for Machine Learning and Intelligent Systems at the University of California Irvine.
The long term focus of his research is understanding intelligence
in brains and machines. This includes working at the interface between
the computational and the natural sciences, in particular the application of
artificial intelligence and statistical machine learning methods to big data problems
in physics, chemoinformatics, genomics, systems biology, and computational neuroscience.
He is credited with pioneering the use of Hidden Markov Models (HMMs),
graphical models, and recursive neural networks (deep learning) in bioinformatics and chemoinformatics.
He has a Google Scholar H-index of 73 and is the the recipient of the 1993 Lew Allen Award at JPL,
the 1999 Laurel Wilkening Faculty Innovation Award at UCI,
a 2006 Microsoft Research Award, the 2010 E. R. Caianiello Prize for research in
machine learning, and a 2004 Google Faculty Research Award.
He is and Elected Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB.




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