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Pittsburgh, PA, United States

Carnegie Mellon University is a private research university in Pittsburgh, Pennsylvania.The university began as the Carnegie Technical Schools founded by Andrew Carnegie in 1900. In 1912, the school became the Carnegie Institute of Technology and began granting four-year degrees. In 1967, the Carnegie Institute of Technology merged with the Mellon Institute of Industrial Research to form Carnegie Mellon University. The university's 140-acre main campus is 3 miles from Downtown Pittsburgh and abuts the Carnegie Museums of Pittsburgh, the main branch of the Carnegie Library of Pittsburgh, the Carnegie Music Hall, Schenley Park, Phipps Conservatory and Botanical Gardens, the Pittsburgh Golf Club, and the campus of the University of Pittsburgh in the city's Oakland and Squirrel Hill neighborhoods, partially extending into Shadyside.Carnegie Mellon has seven colleges and independent schools: the Carnegie Institute of Technology , College of Fine Arts, Dietrich College of Humanities and Social science, Mellon College of Science, Tepper School of Business, H. John Heinz III College and the School of Computer Science. Carnegie Mellon fields 17 varsity athletic teams as part of the University Athletic Association conference of the NCAA Division III. Wikipedia.

Jin R.,Carnegie Mellon University
Angewandte Chemie - International Edition | Year: 2010

(Figure Presented) Seeing double: Nanoparticle clusters (dimers, trimers, etc.) have long been pursued as enhancers in surfaceenhanced Raman spectroscopy research. A recent report presents an elegant approach for the high-yielding fabrication of dimers of silver nanospheres from nanocubes by controlled chemical etching. These nanoparticle dimers are capable of strongly enhancing Raman signals of surface adsorbates (see picture). © 2010 Wiley-VCH Verlag GmbH & Co. KCaA,.

De La Torre F.,Carnegie Mellon University
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2012

Over the last century, Component Analysis (CA) methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA), Locality Preserving Projections (LPP), and Spectral Clustering (SC) have been extensively used as a feature extraction step for modeling, classification, visualization, and clustering. CA techniques are appealing because many can be formulated as eigen-problems, offering great potential for learning linear and nonlinear representations of data in closed-form. However, the eigen-formulation often conceals important analytic and computational drawbacks of CA techniques, such as solving generalized eigen-problems with rank deficient matrices (e.g., small sample size problem), lacking intuitive interpretation of normalization factors, and understanding commonalities and differences between CA methods. This paper proposes a unified least-squares framework to formulate many CA methods. We show how PCA, LDA, CCA, LPP, SC, and its kernel and regularized extensions correspond to a particular instance of least-squares weighted kernel reduced rank regression (LS-WKRRR). The LS-WKRRR formulation of CA methods has several benefits: 1) provides a clean connection between many CA techniques and an intuitive framework to understand normalization factors; 2) yields efficient numerical schemes to solve CA techniques; 3) overcomes the small sample size problem; 4) provides a framework to easily extend CA methods. We derive weighted generalizations of PCA, LDA, SC, and CCA, and several new CA techniques. © 2012 IEEE.

Jin R.,Carnegie Mellon University
Nanoscale | Year: 2010

The scientific study of gold nanoparticles (typically 1-100 nm) has spanned more than 150 years since Faraday's time and will apparently last longer. This review will focus on a special type of ultrasmall (<2 nm) yet robust gold nanoparticles that are protected by thiolates, so-called gold thiolate nanoclusters, denoted as Au n(SR) m (where, n and m represent the number of gold atoms and thiolate ligands, respectively). Despite the past fifteen years' intense work on Au n(SR) m nanoclusters, there is still a tremendous amount of science that is not yet understood, which is mainly hampered by the unavailability of atomically precise Au n(SR) m clusters and by their unknown structures. Nonetheless, recent research advances have opened an avenue to achieving the precise control of Au n(SR) m nanoclusters at the ultimate atomic level. The successful structural determination of Au 102(SPhCOOH) 44 and [Au 25(SCH 2CH 2Ph) 18] q (q = -1, 0) by X-ray crystallography has shed some light on the unique atomic packing structure adopted in these gold thiolate nanoclusters, and has also permitted a precise correlation of their structure with properties, including electronic, optical and magnetic properties. Some exciting research is anticipated to take place in the next few years and may stimulate a long-lasting and wider scientific and technological interest in this special type of Au nanoparticles. © 2010 The Royal Society of Chemistry.

Jordan M.I.,University of California at Berkeley | Mitchell T.M.,Carnegie Mellon University
Science | Year: 2015

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.

Matyjaszewski K.,Carnegie Mellon University | Tsarevsky N.V.,Southern Methodist University
Journal of the American Chemical Society | Year: 2014

This Perspective presents recent advances in macromolecular engineering enabled by ATRP. They include the fundamental mechanistic and synthetic features of ATRP with emphasis on various catalytic/initiation systems that use parts-per-million concentrations of Cu catalysts and can be run in environmentally friendly media, e.g., water. The roles of the major components of ATRP - monomers, initiators, catalysts, and various additives - are explained, and their reactivity and structure are correlated. The effects of media and external stimuli on polymerization rates and control are presented. Some examples of precisely controlled elements of macromolecular architecture, such as chain uniformity, composition, topology, and functionality, are discussed. Syntheses of polymers with complex architecture, various hybrids, and bioconjugates are illustrated. Examples of current and forthcoming applications of ATRP are covered. Future challenges and perspectives for macromolecular engineering by ATRP are discussed. © 2014 American Chemical Society.

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