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Events for December 09, 2010
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Six Sigma Black BeltThu, Dec 09, 2010
Executive Education
Conferences, Lectures, & Seminars
Speaker: Multiple Instructors,
Talk Title: Six Sigma Black Belt
Series:
Abstract: This course teaches you the advanced problem-solving skills you'll need in order to measure a process, analyze the results, develop process improvements and quantify the resulting savings. Project assignments between sessions require you to apply what youâve learned. This course is presented in the classroom in three five-day sessions over a three-month period.
Learn the advanced problem-solving skills you need to implement the principles, practices and techniques of Six Sigma to maximize performance and cost reductions in your organization. During this three-week practitioner course, you will learn how to measure a process, analyze the results, develop process improvements and quantify the resulting savings. You will be required to complete a project demonstrating mastery of appropriate analytical methods and pass an examination to earn IIEâs Six Sigma Black Belt Certificate.This practitioner course for Six Sigma implementation provides extensive coverage of the Six Sigma process as well as intensive exposure to the key analytical tools associated with Six Sigma, including project management, team skills, cost analysis, FMEA, basic statistics, inferential statistics, sampling, goodness of fit testing, regression and correlation analysis, reliability, design of experiments, statistical process control, measurement systems analysis and simulation. Computer applications are emphasized.
Biography:
Host: Viterbi Professional Programs
More Info: http://mapp.usc.edu/professionalprograms/ShortCourses/SixSigmaBlackBelt.htmLocation: USC campus or Online
Audiences: Registered Attendees Only
Contact: Viterbi Professional Programs
Event Link: http://mapp.usc.edu/professionalprograms/ShortCourses/SixSigmaBlackBelt.htm
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Online Learning in Dynamic Spectrum Access: Restless Bandits, Equilibrium and Social Optimality
Thu, Dec 09, 2010 @ 03:00 AM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mingyan Liu , Electrical Engineering and Computer Science University of Michigan
Abstract:
Thursday December 9 3 â 4:30 pm EEB 248
Abstract: We consider a dynamic spectrum access problem where the time‐varying condition of a channel (e.g., as a result of random fading or certain primary users' activities) is modeled as an arbitrary finite‐state Markov chain. At each instance of time, a (secondary) user selects and uses a channel and receives a certain reward as a function of the state of the channel (e.g., good channel condition results in higher data rate for the user). Each channel has potentially different state space and statistics, both unknown to the user, who tries to learn which one is the best so it can maximize its usage of the best channel. The objective is to construct good online learning algorithms so as to minimize the difference between the user's performance in total reward and that of using the best channel (on average) had it known which one is the best from a priori knowledge of the channel statistics (also known as the regret). This is an instance of the multiarmed bandit problem, and is well studied when each reward process is iid over time. In our case the reward processes are Markovian, and furthermore, restless, in that the channel conditions will continue to evolve independent of the user's actions. This leads to a restless bandit problem, for which there exists relatively few results on either algorithms or performance bounds in this learning context. We introduce an algorithm that utilizes regenerative cycles of a Markov chain to compute a sample‐mean based index policy, and show that under mild conditions on the state transition probabilities of the Markov chains this algorithm achieves logarithmic regret uniformly over time, and that this regret bound is also optimal. We also show that this result can be easily extended to the case when the user is allowed to use multiple channels at a time. We numerically examine the performance of this algorithm along with a few other algorithms with Gilbert‐Elliot channel models, and discuss how this algorithm may be further improved (in terms of its constant) and how this result may lead to similar bounds for other algorithms.
We then consider this type of online learning in a multiuser setting where simultaneous access to the same channel by multiple users may lead to collision and reduced reward. We show how such multiuser learning converges to a Nash equilibrium of an equivalent game, and how appropriate modifications to the learning algorithms can induce socially optimal channel allocation.
Host: Bhaskar Krishnamachari
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Shane Goodoff
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Insights on Latent Perceptual Indexing with Applications in Audio and Speech Recognition
Thu, Dec 09, 2010 @ 10:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Shiva Sundaram, Senior Research Scientist/ Deutsche Telekom Laboratories (T-Labs), Berlin, Germany
Talk Title: Insights on Latent Perceptual Indexing with Applications in Audio and Speech Recognition
Abstract: One of the main ideas that originated from my thesis work is latent indexing applied to content-based audio retrieval. Coined as Latent Perceptual Indexing/Mapping, it fundamentally uses the information in weighted unit-document co-occurrence measures. The procedure is analogous to latent semantic indexing of text documents except the bag-of-features from the audio clips constitute the documents and the units are obtained by clustering those documents. In this talk, I will present improvements to the basic approach and also present recent results on its application to acoustic modelling for speech recognition. I will also take this opportunity to talk about my related research efforts in affect-based retrieval of audio, salient-event detection in video and natural speech interfaces.
Biography: Shiva Sundaram received his PhD and his MS, both in Electrical Engineering from the University of Southern California (USC) in 2008, and 2003 respectively. He received his Bachelor of Engineering (B.E) degree in Electronics Engineering from the University of Pune, India in 2001. Since November 2008 he has been a Senior Research Scientist with Deutsche Telekom Laboratories (T-Labs) in Berlin, Germany. Before joining T-Labs, he was a research intern in the Speech and Language Technologies Group at Apple. From summer 2002 to fall 2008 he was a research assistant with Prof. Shrikanth Narayanan in the Signal Analysis and Interpretation Lab (SAIL) at the University of Southern California (USC), Los Angeles. His research interests in the area of speech and audio processing includes recognition and synthesis of speech, signal processing for multimedia retrieval, audio perception, and pattern recognition. He has published over 25 scientific articles in international conferences and journals. In 2006, he received the best student paper award in IEEE MMSP workshop for his work in music information retrieval.
Host: Professor Shrikanth Narayanan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mary Francis
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Lyman L. Handy Colloquium Series
Thu, Dec 09, 2010 @ 12:50 PM - 01:50 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Professor Jinsang Kim,
Talk Title: Functional Conjugated Polymers for Biosensors and Optoelectronic Applications
Abstract: Conjugated polymers (CPs) have become emerging materials for many useful applications due to the tunability of their properties by variation of chemical structure. Particularly the biosensor application of CPs has gain much interest recently because CP-based sensors can provide large signal amplification. The concept, design principles, and applications of conjugated polymers for self-signal amplifying biosensors and sensor arrays will be discussed. We have developed conjugated polymer-based biosensors to detect clinically important biological materials such as DNA and proteins. Our signal amplifying sensors are designed to achieve high sensitivity by means of the energy harvesting property and highly emissive property of conjugated polymers. Receptors are rationally designed to provide specificity toward a target analyte to realize high selectivity. Signal amplifying DNA microarrays, PDA liposome arrays for selective potassium detection and mercury detection, prostate specific antigen sensors, bioconjugated emissive organic nanoparticles for immunofluorescence labeling, and warfare agent detection sensors will be discussed. Optoelectronic application is another promising direction of our conjugated polymer research. Flexible conjugated polymer photovoltaic cells having controlled nanostructures, pure organic phosphorescence emitters, and negative index materials will be also discussed in the second part of the talk.
Biography: Jinsang Kim is an associate professor having a joint appointment in Department of Materials Science and Engineering, Chemical Engineering, Biomedical Engineering, and Macromolecular Science and Engineering at the University of Michigan, Ann Arbor. He holds a M.S (1993) and a B.S. (1991) from Seoul National University, Korea, both in Fiber and Polymer Science. He earned his Ph.D. in 2001 in Materials Science and Engineering from MIT, where he studied the design, synthesis, and assembly of conjugated sensory polymers and energy transport properties in the controlled structures. He is also an expert in genetically engineered protein research. His postdoctoral work in this area at Caltech involved the expression of artificial genes to determine the extent to which artificial genetic information can be used to encode supramolecular assembly in macromolecular systems.
He has won several prestigious awards including 2007 NSF CAREER Award, 2006 Holt Award for excellent teaching, 2002 IUPAC Prize for Young Chemist, 2002 ACS ICI Award, and 2000 MRS Graduate Student Gold Award. He was also named one of emerging investigators by the journal of materials chemistry in 2007. His current research interests at the UM are self-signal amplifying molecular biosensors, flexible solar cells, highly emissive organic emitters, and negative index materials. His research has been sponsored by NSF BES, NSF ECS, NSF DMR, AFOSR, ARO, DoE, ACS, KIMM, KRF, NVRQS, and Center for Chemical Genomics.
Host: Professor Wang
More Info: http://chems.usc.edu/academics/10-11/l-12-09-10.htmLocation: Hedco Pertroleum and Chemical Engineering Building (HED) - 116
Audiences: Everyone Is Invited
Contact: Petra Pearce
Event Link: http://chems.usc.edu/academics/10-11/l-12-09-10.htm
