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Events for November 23, 2010
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Seminar by Jongseung Yoon
Tue, Nov 23, 2010 @ 12:30 PM - 01:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Jongseung Yoon, USC, Assistant Professor in Chemical Engineering and Materials Science
Talk Title: Inorganic Semiconductor Micro/Nanomaterials and Deterministic Assembly by Transfer Printing for Unusual Format Photovoltaics
Abstract: Solar modules that involve large collections of small, ultrathin photovoltaic cells integrated on a thin sheet of plastic offer attractive features that can not be achieved with conventional approaches. In the first part of my talk, I will describe the use of ultrathin, monocrystalline silicon solar microcells generated from the bulk wafer through wet chemical etching and top-down lithographic processes as building blocks for creating unconventional photovoltaic modules enabled with massively parallel printing techniques. The resulting devices can provide many useful characteristics, including high degrees of mechanical flexibility, user-definable levels of transparency, ultra-thin form factor micro-optic concentrator designs, together with the potential for low cost and high efficiency. In the second part, I will discuss releasable epitaxial multilayer assemblies of gallium arsenide (GaAs) based compound semiconductors for their use in high performance photovoltaics. While compound semiconductors such as GaAs provide unmatched performance in photovoltaic and optoelectronic devices, current methods for growing and fabricating these materials are incompatible with the most important modes of use, particularly in photovoltaics, where large quantities of material must be distributed over large areas on low cost, amorphous foreign substrates. We developed new methods that address many of these challenges, through cost effective production of high quality functional films of GaAs from thick, epitaxial assemblies formed in a single deposition sequence on a growth wafer. Specialized designs enabled separation, release and assembly of individual active layers in these stacks to create devices on various substrates, in quantities and over areas that exceed possibilities with conventional approaches.
Biography: Prof. Yoon received his B.S. degree from Seoul National University in South Korea, and Ph.D. degree in Materials Science and Engineering from the Massachusetts Institute of Technology in 2006. Prof. Yoon has been a Beckman Institute Postdoctoral Fellow at the University of Illinois at Urbana-Champaign since 2007. At UIUC, Prof. Yoon has worked on developing new approaches for high performance, unusual format photovoltaic and optoelectronic systems based on arrays of monocrystalline Si and GaAs and micro-transfer-printing techniques. Prof. Yoonâs research interests at USC lie in exploiting various classes of micro/nanomaterials and heterogeneously integrating them into functional devices in the manner that their electrical, optical, mechanical, and thermal properties are optimally combined together for advanced applications in energy-harvesting, photonics, electronics, as well as sensor technologies.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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CS Colloquium
Tue, Nov 23, 2010 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Chun-Nan Hsu, Information Sciences Institute (ISI)
Talk Title: Accelerating Machine Learning by Aggressive Extrapolation
Abstract: This talk presents how to accelerate statistical machine learning algorithms for large scale applications by aggressive extrapolation. Extrapolation methods, such as Aitken's acceleration, have the advantage that they can achieve quadratic convergence with an overhead linear to the dimension of the training data. However, they can be numerically unstable and their convergence is only locally guaranteed. We show that this can be fixed by a double extrapolation method. There are two options for the extrapolation, global or component-wise. Previously, it was not clear which option is more effective. We show a general condition to determine which option will be more effective and show how to apply the condition to the training of Bayesian networks and conditional random fields (CRF). Then we show that extrapolation can accelerate on-line learning with a method called Periodic Step-size Adaptation (PSA). We show that PSA is an approximation of a theoretic "single-pass" on-line learning method, which can converge to an empirical optimum in a single pass through the training examples. With a single-pass on-line learning method, disk I/O can be minimized when a training set is too large to fit in memory. Experimental results for a wide variety of models, including CRF, linear SVM, and convolutional neural networks, show that single-pass performance of PSA is always very close to empirical optimum. Finally, an application to gene mention tagging for biological text mining will be presented, which achieved the top score in BioCreative 2 challenge in 2007 and again in BioCreative 3 challenge in 2010.
Biography: Dr. Chun-Nan Hsu is a computer scientist at Information Sciences Institute (ISI). Prior to joining ISI, he is Research Fellow and Leader of the Adaptive Internet Intelligent Agents (AIIA) Lab at the Institute of Information Science, Academia Sinica, Taipei, Taiwan. His research interests include machine learning, data mining, databases and bioinformatics. He earned his M.S. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles, CA, in 1992 and 1996, respectively. In 1996, before he passed his doctoral oral exam, he had been offered a position as Assistant Professor at the Department of Computer Science and Engineering, Arizona State University, Tempe, AZ. He taught there for two years before he returned to Taiwan in 1998. Since 2005, he has been the principal investigator of the Advanced Bioinformatics Core, National Research Program in Genomic Medicine, Taiwan, and leading one of the largest research efforts in computerized drug design and discovery in Taiwan. In 2006, the first drug candidate due to the use of the software his team developed was commercialized. In 2007, his teams achieved the best scores in the BioCreative 2 text mining challenge. Dr. Hsu has published about 90 scientific articles since 1993. Currently, Dr. Hsu has been working on applying artificial intelligence to computational biology and bioinformatics.
Host: Dr. Dennis McLeod
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Kanak Agrawal
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CS Colloquium
Tue, Nov 23, 2010 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Chun-Nan Hsu, ISI, USC, Machine Learning, Information Integration and Bioinformatics
Talk Title: Accelerating Machine Learning by Aggressive Extrapolation
Abstract: This talk presents how to accelerate statistical machine learning algorithms for large scale applications by aggressive extrapolation. Extrapolation methods, such as Aitken's acceleration, have the advantage that they can achieve quadratic convergence with an overhead linear to the dimension of the training data. However, they can be numerically unstable and their convergence is only locally guaranteed. We show that this can be fixed by a double extrapolation method. There are two options for the extrapolation, global or component-wise. Previously, it was not clear which option is more effective. We show a general condition to determine which option will be more effective and show how to apply the condition to the training of Bayesian networks and conditional random fields (CRF). Then we show that extrapolation can accelerate on-line learning with a method called Periodic Step-size Adaptation (PSA). We show that PSA is an approximation of a theoretic "single-pass" on-line learning method, which can converge to an empirical optimum in a single pass through the training examples. With a single-pass on-line learning method, disk I/O can be minimized when a training set is too large to fit in memory. Experimental results for a wide variety of models, including CRF, linear SVM, and convolutional neural networks, show that single-pass performance of PSA is always very close to empirical optimum. Finally, an application to gene mention tagging for biological text mining will be presented, which achieved the top score in BioCreative 2 challenge in 2007 and again in BioCreative 3 challenge in 2010.
Biography: Dr. Chun-Nan Hsu is a computer scientist at Information Sciences Institute (ISI). Prior to joining ISI, he is Research Fellow and Leader of the Adaptive Internet Intelligent Agents (AIIA) Lab at the Institute of Information Science, Academia Sinica, Taipei, Taiwan. His research interests include machine learning, data mining, databases and bioinformatics. He earned his M.S. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles, CA, in 1992 and 1996, respectively. In 1996, before he passed his doctoral oral exam, he had been offered a position as Assistant Professor at the Department of Computer Science and Engineering, Arizona State University, Tempe, AZ. He taught there for two years before he returned to Taiwan in 1998. Since 2005, he has been the principal investigator of the Advanced Bioinformatics Core, National Research Program in Genomic Medicine, Taiwan, and leading one of the largest research efforts in computerized drug design and discovery in Taiwan. In 2006, the first drug candidate due to the use of the software his team developed was commercialized. In 2007, his teams achieved the best scores in the BioCreative 2 text mining challenge. Dr. Hsu has published about 90 scientific articles since 1993. Currently, Dr. Hsu has been working on applying artificial intelligence to computational biology and bioinformatics.
Host: Prof. Dennis McLeod
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Kanak Agrawal
