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August 24, 2017

We review and discuss the structure and implementation of basic neural networks using PyTorch. Polynomial fitting, classification, and mixture density networks will be discussed along with coding details for replications of results found in the literature.



August 18, 2017

We review the motivation for and implementation of sparse Gaussian processes.  Special attention is given to the variational method of Titsias (2009), which addresses many of the shortcomings of the previous state of the art and serves as a foundation for many current...

Introduction to Expectation Propagation [Slides]

Souvik Chakraborty

Expectation propagation (EP) is an approximate Bayesian inference algorithm which constructs tractable approximations to complex probability distributions. EP is an extension of the assumed density filte...

May 23, 2017

We welcome our new group member, Nick Geneva, graduated from University of Delaware with a BS, Honors Mechanical Engineering (with Mathematics Minor). Nick earned two prestigious fellowships, one from NSF Graduate Research Fellowship Program (GRFP), the other from Nati...

May 23, 2017

We have recently received a funding from Rolls Royce on ...

How certain are we of the accuracy of DFT simulations? In this project we try to provide an answer through a framework to quantify the uncertainty in DFT XC functional.

Figure 1 (Top) Magnitude of the coefficients obtained using a RVM for the determination of the hyperp...

What confidence can we have in alloy property calculations when we use surrogate models? In this project, we develop a Bayesian UQ framework to quantify and propagate the uncertainty when using the Cluster Expansion model.


New technological advances require...

March 14, 2017

Stayed tuned as we will update more about our current research in the coming weeks.

March 14, 2017

Intro to DGP and everything...

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Our new website is launched today.

March 14, 2017

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