Introducing a new lecture series from the Office of the Provost and the Vice President for Research
Free and open to the public

Visualizing the Quantum World
Séamus Davis is J.G. White Distinguished Professor of Physical Sciences at Cornell University; SUPA Distinguished Research Professor of Physics at St. Andrews University, Scotland; and Scientific Director of the Center for Emergent Superconductivity at Brookhaven National Lab. He has been the recipient of many honors, including the 2005 London Prize, the premier award in low temperature physics; and in 2009 the prestigious Kamerlingh-Onnes Prize for superconductivity research. Davis is a fellow of the British Institute of Physics and the American Physical Society. In 2010 he was elected to the US National Academy of Sciences.

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

Biological Evolution as a Form of Learning
Leslie Valiant is T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in the School of Engineering and Applied Sciences at Harvard University, where he has taught since 1982. He was educated at King's College, Cambridge; Imperial College, London; and at Warwick University where he received his PhD in computer science in 1974. Before coming to Harvard, he taught at Carnegie Mellon University, Leeds University, and the University of Edinburgh. Leslie Valiant's work has ranged over several areas of theoretical computer science, particularly complexity theory, computational learning, and parallel computation. He also has interests in computational neuroscience, evolution and artificial intelligence. He received the Nevanlinna Prize at the International Congress of Mathematicians in 1986, the Knuth Award in 1997, the European Association for Theoretical Computer Science EATCS Award in 2008, and the 2010 A. M. Turing Award. He is a Fellow of the Royal Society and a member of the National Academy of Sciences.

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

Catalysis: Green Chemicals and Materials
Robert Howard Grubbs was awarded the 2005 Nobel Prize in Chemistry for his pioneering work in metathesis. A member of the National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences and the Honorary Fellowship of the Royal Society of Chemistry, he has more than 500 publications and 115 patents based on his research.
Grubbs is the Atkins Professor of Chemistry at the California Institute of Technology, where he has been a faculty member since 1978.

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

The Three R's of Computer Vision: Recognition, Reconstruction and Reorganization
Jitendra Malik is Arthur J. Chick Professor of Electrical Engineering & Computer Science at the University of California at Berkeley. His research group has worked on many different topics in computer vision, computational modeling of human vision, computer graphics and the analysis of biological images, resulting in more than 150 research papers and 30 PhD dissertations. Several well-known concepts and algorithms arose in this research, such as anisotropic diffusion, normalized cuts, high dynamic range imaging, and shape contexts. According to Google Scholar, seven of his papers have received more than a thousand citations each, and he is one of ISI's Highly Cited Researchers in Engineering.

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