Introducing a new lecture series from the Office of the Provost and the Vice President for Research
|April 25: J.C. Séamus Davis, PhD|
Abstract: Everything around us, everything each of us has ever experienced, and virtually everything underpinning our technological society and economy is governed by quantum mechanics. Yet this most fundamental physical theory of nature often feels as if it is a set of somewhat eerie and counterintuitive ideas of no direct relevance to our lives. Why is this? One reason is that we cannot perceive the strangeness (and astonishing beauty) of the quantum mechanical phenomena all around us by using our own senses. Davis will describe the very recent development of techniques that allow us to visualize electronic quantum matter directly at the atomic scale, and will discuss how they are used to explore the complex and mysterious forms of electronic matter sustaining high temperature superconductivity. One of the key motivations for development of these techniques has been to explore the complex and mysterious forms of electronic matter sustaining high temperature superconductivity–the ability of some new materials to transport electrical energy and information in a perfectly efficient and lossless manner. Davis will discuss the potential benefits of discovering a fundamental understanding of this phenomenon, and then explain the progress toward that goal achieved by direct atomic scale visualization of this form of quantum matter.
Thursday, April 25, 4:00 pm, Wang Center Theater
|February 1: Leslie Valiant, PhD|
Biological Evolution as a Form of Learning
Abstract: Living organisms function according to protein circuits. Darwin's theory of evolution suggests that these circuits have evolved through variation guided by natural selection. However, the question of which circuits can so evolve in realistic population sizes and within realistic numbers of generations has remained essentially unaddressed. Computational learning theory offers the framework for investigating this question, of how circuits can come into being adaptively from experience, without a designer. We formulate evolution as a form of learning from examples. The targets of the learning process are the functions of highest fitness. The examples are the experiences. The learning process is constrained so that the feedback from the experiences is Darwinian. We formulate a notion of evolvability that distinguishes function classes that are evolvable with polynomially bounded resources from those that are not. The dilemma is that if the function class, say for the expression levels of proteins in terms of each other, is too restrictive, then it will not support biology, while if it is too expressive then no evolution algorithm will exist to navigate it. This lecture will review current work in this area.
Friday, February 1, 2:30 pm, Simons Center
|February 15: Nobel Prize-Winner Robert H. Grubbs, PhD|
Green Chemicals and Materials
Abstract: Plastics, pharmaceuticals and fuels—essentials of modern life—are all produced through specific chemical transformations. In most of these cases, catalysts provide the key component in their production. As pressures for cleaner processes grow, new types of catalysts are required that open new ways to transform renewable carbon sources to fuels and products, and provide more sustainable products. Examples from developments in olefin metathesis catalysts will be used to demonstrate some of these principles. New catalysts have resulted from basic research that are currently being used for the clean production of insect pheromones to replace pesticides, for the construction of lighter, tougher wind turbines and for the production of fuels and chemicals from bio-sources.
Friday, February 15, 4:00 pm, Charles B. Wang Center Theater
|March 8: Jitendra Malik, PhD|
The Three R's of Computer Vision: Recognition, Reconstruction and Reorganization
Abstract: Over the last two decades, we have seen remarkable progress in computer vision with demonstration of capabilities such as face detection, handwritten digit recognition, reconstructing three-dimensional models of cities, automated monitoring of activities, segmenting out organs or tissues in biological images, and sensing for control of robots and cars. Yet there are many problems where computers still perform significantly below human perception. For example, in the recent PASCAL benchmark challenge on visual object detection, the average precision for most 3D object categories was under 50%. This lecture will argue that further progress on the classic problems of computational vision: Recognition, Reconstruction and Re-organization requires the study of interaction among these processes.
Friday, March 8, 2:30 pm, Wang Center Theater
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