- talks - lists.cs.princeton.edu

Talk by Byron Cook, Friday, Nov 16
by Emily Lawrence 13 Nov '18

13 Nov '18

PL Seminar Speaker Byron Cook, from Amazon Web Services and UCL Friday, November 16 - 2:00 pm Computer Science - Room 302 Reasoning about Security of Amazon Web Services This talk will discuss the development and use of formal verification tools within Amazon Web Services (AWS) to increase the security assurance of its cloud infrastructure and to help customers secure themselves. I’ll also discuss some remaining challenges that could inspire future research in the community. Bio: Byron Cook is Professor of Computer Science at University College London (UCL) and Director of Automated Reasoning at Amazon Web Services. Byron’s interests include computer/network security, program analysis/verification, programming languages, theorem proving, logic, hardware design, operating systems, and biological systems. Byron is the founder and leader of Amazon’s Automated Reasoning Group (ARG).

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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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Yushan Su will present her General Exam on Thursday, October 25, 2018 at 12:30pm in CS 401.
by Nicki Gotsis 19 Oct '18

19 Oct '18

Yushan Su will present her General Exam on Thursday, October 25, 2018 at 12:30pm in CS 401. The members of her committee are Kai Li (advisor), Kyle Jamieson, and David August. Everyone is invited to attend her talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Her abstract and reading list follow below. Title: FPGA Accelerator for 2D Iterative Compressed Sensing Reconstruction Abstract: During the past decade, Compressed Sensing (CS) has emerged as an important method in signal acquisition and processing, allowing the reconstruction of original data from sparse signals with a significantly lower sampling rate than traditional sampling techniques. Among the CS reconstruction algorithms, iterative algorithms can achieve high accuracy, but require intensive computation. As quite a few applications of CS such as biomedical imaging and radar imaging have real-time constraints, how to accelerate the performance of iterative CS reconstruction algorithms is a challenging problem. In this research, we explore how to build an accelerator of 2D iterative CS reconstruction algorithms for FPGAs, since they can potentially achieve higher performance to power ratio than CPUs and GPUs, and they are more programmable than ASICs. Our approach is based on the methodology to mapping to spatial computing fabrics. We have implemented 2D Iterative Soft-shrinkage Threshold Algorithm (ISTA) on Intel/Altera Stratix 10 FPGA. Comparing with the performance of a CPU and a GPU with similar technologies, the projected performance to power ratio is 24.2x than CPU, 2.4x than GPU running the open-source BART MRI toolkit. Reading List: Candès, Emmanuel J., and Michael B. Wakin. "An introduction to compressive sampling." IEEE signal processing magazine 25.2 (2008): 21-30. Lustig, Michael, David Donoho, and John M. Pauly. "Sparse MRI: The application of compressed sensing for rapid MR imaging." Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 58.6 (2007): 1182-1195. Compton, Katherine, and Scott Hauck. "Reconfigurable computing: a survey of systems and software." ACM Computing Surveys (csuR) 34.2 (2002): 171-210. Parashar, Angshuman, et al. "Triggered instructions: a control paradigm for spatially-programmed architectures." ACM SIGARCH Computer Architecture News. Vol. 41. No. 3. ACM, 2013. Lee, Edward A., and David G. Messerschmitt. "Synchronous data flow." Proceedings of the IEEE 75.9 (1987): 1235-1245. Chen, Ren, Hoang Le, and Viktor K. Prasanna. "Energy efficient parameterized FFT architecture." Field Programmable Logic and Applications (FPL), 2013 23rd International Conference on. IEEE, 2013. Zeng, Hanqing, et al. "A framework for generating high throughput CNN implementations on FPGAs." Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. ACM, 2018. Bai, Lin, et al. "High-speed compressed sensing reconstruction on FPGA using OMP and AMP." Electronics, Circuits and Systems (ICECS), 19th IEEE International Conference on. 2012. Stanislaus, Jerome LVM, and Tinoosh Mohsenin. "Low-complexity FPGA implementation of compressive sensing reconstruction." Computing, Networking and Communications (ICNC), 2013 International Conference on. IEEE, 2013. Quan, Yinghui, et al. "FPGA implementation of real-time compressive sensing with partial Fourier dictionary." International Journal of Antennas and Propagation 2016 (2016). Book List: Hennessy, John L., and David A. Patterson. Computer architecture: a quantitative approach. Elsevier, 2011. Cormen, Thomas H., et al. Introduction to algorithms. MIT press, 2009.

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Yinda Zhang will present his FPO "From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments" on Thursday, 10/25/2018 at 3:00 pm in CS 302.
by Nicki Gotsis 18 Oct '18

18 Oct '18

Yinda Zhang will present his FPO "From Pixels to Scenes: Recovering 3D Geometry and Semantics for Indoor Environments" on Thursday, 10/25/2018 at 3:00 pm in CS 302. The members of his committee are as follows: Thomas Funkhouser (adviser); Examiners: Ryan Adams, Olga Russakovsky, and Thomas Funkhouser; Readers: Szymon Rusinkiewicz and James Hays (Georgia Institute of Technology) A copy of his thesis is available upon request. Everyone is invited to attend her talk. The talk title and abstract follow below: Understanding the 3D geometry and semantics of real environments is in critically high demand for many applications, such as autonomous driving, robotics, and augmented reality. However, it is extremely challenging due to imperfect and noisy measurements from real sensors, limited access to ground truth data, and cluttered scenes exhibiting heavy occlusions and intervening objects. To address these issues, this thesis introduces a series of works that produce a geometric and semantic understanding of the scene in both pixel-wise and holistic 3D representations. Starting from estimating a depth map, which is a fundamental task in many approaches for reconstructing the 3D geometry of the scene, we introduce a learning-based active stereo system that is trained in a self-supervised fashion and reduces the disparity error to 1/10th of other canonical stereo systems. To handle a more common case where only one input image is available for scene understanding, we create a high-quality synthetic dataset facilitating pre-training of data-driven approaches, and demonstrating that we can improve the surface normal estimation and improve raw depth measurements from commodity RGBD sensors. Lastly, we pursue holistic 3D scene understanding by estimating a 3D representation of the scene, in which objects and room layout are represented using 3D bounding box and planar surfaces respectively. We propose methods to produce such a representation from either a single color panorama or a depth image, leveraging scene context. On the whole, these proposed methods produce understanding of both 3D geometry and semantics from the most ne-grained pixel level to the holistic scene scale, building foundations that support future work in 3D scene understanding.

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TODAY 10/17 at 4:30pm in B205 - Negar Kiyavash, Georgia Institute of Technology
by Emily Lawrence 17 Oct '18

17 Oct '18

EE SEMINAR SERIES Speaker: Negar Kiyavash, Georgia Institute of Technology Title: Network Inference: from Passive to Active Learning Day: Wednesday, October 17, 2018 Time: 4:30 pm Room: B205 Engineering Quadrangle Host: Prof. H. Vincent Poor Abstract: One of the paramount challenges of this century is that of understanding complex, dynamic, large-scale networks. Such high-dimensional networks, including social, financial, and biological networks, cover the planet and dominate modern life. In this talk, we propose novel approaches to inference in such networks, for both active (interventional) and passive (observational) learning scenarios. We highlight how timing could be utilized as a degree of freedom that provides rich information about the dynamics. This information allows resolving direction of causation even when only a subset of the nodes is observed (latent setting). In the presence of large data, we propose algorithms that identify optimal or near-optimal approximations to the topology of the network often by harnessing side information about the model class. Bio: Negar Kiyavash is a joint Associate Professor in the H. Milton Stewart School of Industrial & Systems Engineering (ISyE) and the School of Electrical and Computer Engineering (ECE) at Georgia Institute of Technology (Gatech). Prior to joining Gatech, when was a Willett Faculty Scholar and a joint Associate Professor of Industrial and Enterprise Engineering (IE) and Electrical and Computer Engineering (ECE) at the University of Illinois . Her research interests are in design and analysis of algorithms for network inference and security. She is a recipient of NSF CAREER and AFOSR YIP awards and the Illinois College of Engineering Dean's Award for Excellence in Research.

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Tomorrow, October 18, 4:30pm, E-Quad B205, Ayfer Ozgur Aydin, Stanford
by Emily Lawrence 17 Oct '18

17 Oct '18

TOMORROW Department of Electrical Engineering Seminar Series Speaker: Prof. Ayfer Ozgur Aydin Stanford University Title: A Geometric Approach to two Problems in Networks: Learning High-Dimensional Distributions and Communication with Relays Date: Thursday, October 18, 2018 Time: 4:30 pm Room: Engineering Quadrangle B205 Host: Prof. Yuxin Chen Abstract: We investigate the pivotal role geometry can play in characterizing the fundamental limits of information flow in two different network problems. We first consider a classical problem in network information theory: characterizing the capacity of a so-called relay channel. Even though the relay channel has been one of the central problems in network information theory, its capacity remains unknown for almost four decades. We solve an open problem posed by Cover in 1987. This problem, which Cover calls the "Capacity of the Relay Channel", corresponds to characterizing the capacity of this channel at one special operating point. Our approach is geometric and builds on an extension we develop for the isoperimetric inequality on a high-dimensional sphere. We then turn to another network problem, where the end goal is not to communicate a message but learn a high-dimensional distribution or parameter from its distributed samples under communication constraints. We first provide a geometric characterization of Fisher information from quantized samples. We then use this characterization to prove tight minimax bounds for distributed estimation of common statistical models (such as the product Bernoulli model, multinomial model, dense/sparse Gaussian location models). Our results show that the impact of the communication constraint can be drastically different depending on the tail behavior of the score function of the model. Some of our results recover or strengthen existing results in this area with simpler and more transparent proofs. We conclude with a discussion on future directions in the intersection of geometry and information theory. First part of the talk is joint work with Xiugang Wu and Leighton Barnes. Second part is joint work with Leighton Barnes, Yanjun Han and Tsachy Weissman. Bio: Ayfer Ozgur received her Ph.D. degree in 2009 from the Information Processing Group at EPFL, Switzerland. In 2010 and 2011, she was a post-doctoral scholar at the same institution. She is an Assistant Professor in the Electrical Engineering Department at Stanford University since 2012. Her research interests include distributed communication and learning, wireless systems, and information theory. Dr. Ozgur received the EPFL Best Ph.D. Thesis Award in 2010, an NSF CAREER award in 2013, the Okawa Foundation Research Grant and the IEEE 2018 Communication Theory Technical Committee (CTTC) Early Achievement Award in 2018. This seminar is supported with funds from the Korhammer Lecture Series.

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Fwd: NEXT PACM Colloquium: Oct 22 - Matthew Salganik, Princeton University
by Jennifer Rexford 16 Oct '18

16 Oct '18

> From: "Gina M. Holland" <gholland(a)Princeton.EDU> > Subject: NEXT PACM Colloquium: Oct 22 - Matthew Salganik, Princeton University > Date: October 16, 2018 at 3:21:01 PM EDT > To: "pacmdistribution(a)math.princeton.edu" <pacmdistribution(a)math.princeton.edu> > > The next PACM Colloquium will be given by Matthew Salganik, Princeton University > > Title and abstract are below. > > ================= > DATE/LOCATION: Monday, October 22, 2018, 4:00 – 5:00 PM / 214 Fine Hall > > SPEAKER: Matthew Salganik, Princeton University > > TITLE: The Fragile Families Challenge: A Scientific Mass Collaboration > > ABSTRACT: > > Sociologists have long theorized about the processes through which childhood experiences shape life outcomes. However, statistical models that use data on family background and childhood experiences to predict life outcomes often have poor predictive performance. These empirical results have lead pioneers of the field muse that random chance must play an important role. In this talk, we present results from the Fragile Families Challenge, a scientific mass collaboration based on the Fragile Families and Child Wellbeing Study, a birth cohort study of about 5,000 families from large US cities. The study began with a probability sample of newborns, and for more than 15 years, researchers have followed these families to collect information related to child and family development as reported by the child as well as the child’s mother, father, primary caregiver, and teachers. During the Fragile Families Challenge, 159 research teams from 68 institutions in 7 countries used this high-dimensional survey data to produce statistical and machine learning models to predict six life outcomes. Results suggest that (a) modern machine learning methods enabled predictive performance that outpaced approaches more common in social science, but (b) overall predictive performance was poor. The talk will include a discussion of the potential reasons for poor predictive performance in this setting, open methodological questions raised by our results, and the potential for mass collaboration to address other scientific and policy questions. > > > Matthew Salganik is Professor of Sociology at Princeton University, and he is affiliated with several of Princeton's interdisciplinary research centers: the Office for Population Research, the Center for Information Technology Policy, the Center for Health and Wellbeing, and the Center for Statistics and Machine Learning. His research interests include social networks and computational social science. He is the author of Bit by Bit: Social Research in the Digital Age <http://www.bitbybitbook.com/>. Salganik's research has been published in journals such as Science, PNAS, Sociological Methodology, and Journal of the American Statistical Association. His papers have won the Outstanding Article Award from the Mathematical Sociology Section of the American Sociological Association and the Outstanding Statistical Application Award from the American Statistical Association. Popular accounts of his work have appeared in the New York Times, Wall Street Journal, Economist, and New Yorker. Salganik's research is funded by the National Science Foundation, National Institutes of Health, Joint United Nations Program for HIV/AIDS (UNAIDS), Russell Sage Foundation, Sloan Foundation, Facebook, and Google. During sabbaticals from Princeton, he has been a Visiting Professor at Cornell Tech and a Senior Research at Microsoft Research. > >

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