November 2018 - talks - lists.cs.princeton.edu

TODAY November 12, 4:30pm, E-Quad B205 - Young-Han Kim, University of California, San Diego
by Emily Lawrence 12 Nov '18

12 Nov '18

EE SEMINAR SERIES Speaker: Young-Han Kim, University of California, San Diego Title: Data Science with Universal Probability Day: Monday, November 12, 2018 Time: 4:30 pm Room: B205 Engineering Quadrangle Host: Prof. Yuxin Chen Abstract: As Laplace famously asked "What is the probability that the sun will rise tomorrow?," inferring the probabilities underlying a given data is at the heart of many data science problems. In this talk, I will explore how to assign probabilities to data such that its unknown distribution is uniformly approximated without overfitting the data. I will develop the mathematical foundation of this theory of universal probability which traces back to Rissanen, to Ziv and Lempel, and even to Laplace, and present a general framework for addressing many data science problems in a unified manner. To illustrate this framework, I will present two practical applications in nucleotide sequence classification and grayscale image denoising. Bio: Young-Han Kim received his B.S. degree in Electrical Engineering from Seoul National University in 1996 and his Ph.D. degree in Electrical Engineering (M.S. degrees in Statistics and in Electrical Engineering) from Stanford University in 2006. Since then he has been on the faculty of the Department of Electrical and Computer Engineering at the University of California, San Diego, where he is currently a Professor. His research contributions have been in information theory, communication engineering, and data science. He has coauthored a highly cited textbook ``Network Information Theory'' (Cambridge University Press, 2011) and a recent monograph ``Fundamentals of Index Coding'' (Now Publishers, 2018). He has received several awards and honors for his contributions, including the NSF CAREER Award (2008), the US-Israel BSF Bergmann Memorial Award (2009), the IEEE Information Theory Paper Award (2012), and the first IEEE James L. Massey Research and Teaching Award (2015). He is a fellow of the IEEE. This seminar is supported with funds from the Korhammer Lecture Series

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Colloquium Speaker-Andy Pavlo, Friday, Nov 16
by Emily C. Lawrence 12 Nov '18

12 Nov '18

Colloquium Speaker Andy Pavlo from Carnegie Mellon University Friday, November 16 - 12:30pm Computer Science - Room 105 “Make Your Database Dream of Electric Sheep: Designing for Autonomous Operation” In the last 20 years, researchers and vendors have built advisory tools to assist DBAs in tuning and physical design. Most of this previous work is incomplete because they require humans to make the final decisions about any database changes and are reactionary measures that fix problems after they occur. What is needed for a "self-driving" DBMS are components that are designed for autonomous operation. This will enable new optimizations that are not possible today because the complexity of managing these systems has surpassed the abilities of humans. In this talk, I present the core design principles of an autonomous DBMS. These are necessary to support ample data collection, fast state changes, and accurate reward observations. I will discuss techniques on how to build a new autonomous DBMS or the steps needed to retrofit an existing one to enable automated management. Our work is based on our experiences at CMU from developing an automatic tuning service ( OtterTune ) and our self-driving DBMS (Peloton). Bio: Andy Pavlo is an Assistant Professor of Databaseology in the Computer Science Department at Carnegie Mellon University. He also used to raise clams.

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Wednesday, November 14, 12:30pm, E-Quad B205 - Gongguo Tang, Colorado School of Mines
by Emily Lawrence 09 Nov '18

09 Nov '18

EE SEMINAR SERIES Speaker: Gongguo Tang, Colorado School of Mines Title: Optimal Spectral Estimation via Atomic Norm Minimization Day: Wednesday, November 14, 2018 Time: 12:30 pm Room: B205 Engineering Quadrangle Host: Prof. Yuxin Chen Abstract: Atomic norm minimization is a convex relaxation framework that greatly generalizes l1 norm for compressed sensing and nuclear norm for matrix completion. In particular, it allows one to construct convex regularizers for signals that have sparse representations with respect to continuously parameterized dictionaries. In this talk, the speaker will focus on the application of this framework to line spectral estimation, which can be viewed as a sparse recovery problem whose atoms are indexed by the continuous frequency variable. In particular, the method's accuracy in inferring the frequencies and complex magnitudes from noisy observations of a mixture of complex sinusoids will be analyzed. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are well-separated, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of one of the true frequencies, whose size matches the CramérRao lower bound up to a logarithmic factor. The analysis is based on a primaldual witness construction procedure. The analysis also reveals that the atomic norm minimization can be viewed as a convex way to solve a l1-norm regularized, nonlinear and nonconvex least-squares problem to global optimality. Bio: Dr. Gongguo Tang is an Assistant Professor in the Department of Electrical Engineering at Colorado School of Mines since 2014. He received his Ph.D. degree in Electrical Engineering from Washington University in St. Louis in 2011. He was a Postdoctoral Research Associate at the Department of Electrical and Computer Engineering, University of Wisconsin-Madison from 2011 to 2013, and a visiting scholar at the University of California, Berkeley in 2013. Dr. Tang's research interests are in the area of optimization, signal processing, and machine learning, and their applications in big data analytics, optics, imaging, and networks. This seminar is supported with funds from the Korhammer Lecture Series

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CEE Seminar-Dr. Lawrence Conyers, Nov. 28
by Scott Karlin 05 Nov '18

05 Nov '18

Wednesday November 28 at 4:30 in Bowen Hall 222 (Auditorium). “Ground-penetrating radar (GPR) methods to map and evaluate buried and architectural remains: examples from around the world” Dr. Lawrence B. Conyers Professor of Anthropology, University of Denver Abstract: The GPR method, which uses reflected radar waves to map the extent and composition of architectural features, can produce three-dimensional images of a variety of cultural remains where there is no surface expression of what lies below. Imaging software can take those digital readings produced from hundreds of thousands of individual reflections in high resolution grids to produce profiles, map-view slices and three-dimensional renderings of buried structures and associated cultural features. Collection, processing and interpretation of those datasets must be calibrated and modified based on the architecture of interest, but also complex ground conditions. A few of variables that must be accounted for are the frequency of radar waves transmitted, the composition and geological complexity of the ground, and the resolution and processing of the digital information collected. Examples of processing steps and analyses will be shown using a variety of visualization software at a variety of sites including a Roman villa (later modified into an early Christian cathedral) in France, Nabatean temples from Jordan, monumental architecture in Costa Rica, Aboriginal earthen constructions from northern Australia, and early colonial farmsteads from New England. Dr. Conyers is a professor of anthropology at the University of Denver, Colorado. He has recently published a number of books on the subject of GPR and other near-surface geophysical methods including Ground Landscape Analysis, Ground-penetrating Radar for Geoarchaeology, Ground-Penetrating Radar for Archaeology, 3 rd Editions, and Interpreting Ground-penetrating Radar for Archaeology.. His background and degrees are in the fields of geology, geophysics, and archaeology and the integration of those disciplines. He has applied this synthesis to sites around the world.

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