
PennyLane  Automatic Differentiation and Machine Learning of Quantum Computations
29 Apr 2020   Contributor(s):: Nathan Killoran
PennyLane is a Pythonbased software framework for optimization and machine learning of quantum and hybrid quantumclassical computations.

Advances in Computational and Quantum Imaging Workshop
28 Jan 2020 
The purpose of the workshop is to bring different communities together, review recent theoretical and experimental advances and explore synergetic collaborations. The workshop aligns well with the significant investments in quantum technologies through the National Quantum Initiative in the...

ECE 595ML Lecture 1: Linear Regression  Introduction
21 Jan 2020   Contributor(s):: Stanley H. Chan

ECE 595ML Lecture 2: Regularized Linear Regression
21 Jan 2020   Contributor(s):: Stanley H. Chan

ECE 595ML: Machine Learning I
17 Jan 2020   Contributor(s):: Stanley H. Chan
Spring 2020  This course is in productionCourse Website: https://engineering.purdue.edu/ChanGroup/ECE595/index.htmlCourse Outline:Part 1: Mathematical BackgroundLinear Regression and OptimizationPart 2: ClassificationMethods to train linear classifiersFeature analysis, Geometry, Bayesian...

Universal Variational Quantum Computation
28 Oct 2019   Contributor(s):: Jacob Biamonte
We show that the variational approach to quantum enhanced algorithms admits a universal model of quantum computation.

Quantum Algorithmic Breakeven: on Scaling Up with Noisy Qubits
21 Aug 2019   Contributor(s):: Daniel Lidar
In this talk I will argue in favor of a different criterion I call "quantum algorithmic breakeven," which focuses on demonstrating an algorithmic scaling improvement in an errorcorrected setting over the uncorrected setting. I will present evidence that current experiments with...

Overview of Computational Methods and Machine Learning: Panel Discussion
14 Jun 2019   Contributor(s):: Brett Matthew Savoie, Pradeep Kumar Gurunathan, Peilin Liao, Xiulin Ruan, Guang Lin
The individual Panel Talks which accompanies this discussion can be found here.Why do we need experiments?Are your methods “descriptive” or “predictive”?Do you work with any other theory/simulation groups?On the 5 year timescale: is machinelearning hype or a real...

Overview of Computational Methods and Machine Learning: Panel Talks
14 Jun 2019   Contributor(s):: Brett Matthew Savoie, Pradeep Kumar Gurunathan, Peilin Liao, Xiulin Ruan, Guang Lin
The Panel Discussion which follows these individual presentations can be found here.Individucal Presentations:Theory and Machine Learning in the Chemical Sciences, Brett Matthew Savoie;Divide and Conquer with QM/MM Methods, Pradeep Kumar Gurunathan;Computational Chemistry/Materials, Peilin...

Big Data in Reliability and Security: Some Basics
30 May 2019   Contributor(s):: Saurabh Bagchi

Big Data in Reliability and Security: Applications
30 May 2019   Contributor(s):: Saurabh Bagchi

Peter Shor
Peter Shor is Morss Professor of Applied Mathematics since 2003, and Chair of the Applied Mathematics Committee since 2015. He received the B.A. in mathematics from Caltech in 1981, and the Ph.D....
https://nanohub.org/members/230531

HumanInterpretable Concept Learning via Information Lattices
23 May 2019   Contributor(s):: Lav R. Varshney
The basic idea is an iterative discovery algorithm that has a studentteacher architecture and that operates on a generalization of Shannon’s information lattice, which itself encodes a hierarchy of abstractions and is algorithmically constructed from grouptheoretic foundations.

Janet Daetton
Janet Daetton is a teacher and private tutor working at school for 8 years. She has an educational blog with tips for students and lifelong learners. Her first business experience is being an...
https://nanohub.org/members/228343

Feb 25 2019
Software Productivity and Sustainability for CSE and Data Science
The SIAM CSE conference seeks to enable indepth technical discussions on a wide variety of major computational efforts on largescale problems in science and engineering, foster the...
https://nanohub.org/events/details/1738

Networked Dynamical Systems for Function and Learning: Paradigms for DataDriven Control and Learning in Neurosensory Systems
16 Jan 2019   Contributor(s):: J. Nathan Kutz
Our objective is to use emerging datadriven methods to extract the underlying engineering principles of cognitive capability, namely those that allow complex networks to learn and enact control and functionality in the robust manner observed in neurosensory systems. Mathematically, the...

DataDriven Discovery of Governing Equations of Physical Systems
16 Jan 2019   Contributor(s):: J. Nathan Kutz
We introduce a number of datadriven strategies for discovering nonlinear multiscale dynamical systems and their embeddings from data. We consider two canonical cases: (i) systems for which we have full measurements of the governing variables, and (ii) systems for which we have incomplete...

Myron DSilva
https://nanohub.org/members/182103

Quantifying Uncertainties in Physical Models
28 Aug 2017   Contributor(s):: Ilias Bilionis
Increasing modeling detail is not necessarily correlated with increasing predictive ability. Setting modeling and numerical discretization errors aside, the more detailed a model gets, the larger the number of parameters required to accurately specify its initial/boundary conditions, constitutive...

A Distributed Algorithm for Computing a Common Fixed Point of a Family of Paracontractions
21 Jun 2017   Contributor(s):: A. Stephen Morse
In this talk a distributed algorithm is described for finding a common fixed point of a family of m paracontractions assuming that such a common fixed point exists. The common fixed point is simultaneously computed by m agents assuming each agent knows only paracontraction, the current estimates...