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PhD Defense - Franziska Meier
Tue, May 03, 2016 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Towards a Probabilistic Motor Skill Learning Framework
Location: RTH 406
Time: 10am - noon, May 3rd, 2016
PhD Candidate: Franziska Meier
Committee members:
Prof. Stefan Schaal (Chair)
Prof. Gaurav Sukhatme
Prof. Yan Liu
Prof. James Finley
Abstract:
While learning in robotics has always been seen as one of the
hallmarks to accomplish autonomous behaviors, so far, there is no coherent and robust approach to robot learning. For instance, realizing complex behaviors, such as manipulation skills, often involves a mixing and matching of planning, control, and learning modules, dominated by the insights of the robotics researcher, but not by a coherent design and/or algorithmic principle. Thus, most robot learning approaches have largely remained a proof-of-concept rather then a general research approach towards robot learning.
In this thesis we aim to move towards a motor skill learning framework coherently routed in probabilistic representations. The use of probabilistc graphical models for different learning modules can foster a principled combination of these modules to form an integrated approach to skill representations. Towards this goal I will present
contributions from two directions: Creating computationally efficient approximations of probabilistic graphical models and developing probabilistic solutions to problems in motor skill learning.
In the first part of my talk I will present our work on scaling up learning in graphical models such that the use of a complex graphical model -- as would be required for complex motor skill representations with perceptual coupling -- is feasible.
In the second part of the talk, I will then present probabilistic approaches to two subproblems of motor skill learning. First I will introduce a probabilistic version of dynamic movement primitives. With the help of this formulation we can implement online movement recognition and perform segmentation of complex skill sequences into movement primitives.
Finally, I will tackle the problem of learning internal models, exemplified by inverse dynamics learning. Having a good inverse dynamics model ensures that we can execute trajectories in an accurate yet compliant manner. I will present a real-time capable drifting Gaussian process approach to learning a local approximation of the
inverse dynamics model on the fly.
Location: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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USC Stem Cell Seminar: Stacey Finley, USC
Tue, May 03, 2016 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Stacey Finley, USC
Talk Title: Applying computational systems biology to predict tumor angiogenesis signaling
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Abstract: Angiogenesis, the formation of new blood vessels, enables tumors to obtain oxygen and nutrients from the surrounding microenvironment, promoting tumor growth. Both promoters and inhibitors of angiogenesis contribute to the response to anti-angiogenic cancer therapies; however, there is no quantitative framework that enables investigation of angiogenic factors together in tumor tissue. This presentation will discuss our ongoing work to construct predictive models of key factors involved in regulating tumor angiogenesis. The models provide a tool with which to study the response to anti-angiogenic therapies for a range of tumor types.
Host: Megan McCain
More Info: https://calendar.usc.edu/event/speaker_stacey_finley_usc?utm_campaign=widget&utm_medium=widget&utm_source=USC+Event+Calendar%3A+Beta#.VvGX33DFl04
Webcast: http://keckmedia.usc.edu/stem-cell-seminarWebCast Link: http://keckmedia.usc.edu/stem-cell-seminar
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
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Study Day Spring 2016
Tue, May 03, 2016 @ 11:00 AM - 04:00 PM
Viterbi School of Engineering Student Affairs
Student Activity
Get a jump-start on studying for finals with review sessions for select engineering classes and open study time. Come with your books, your friends, and your questions!
Stop by the sign-in table in the RTH to get your breakfast burrito.
To register, click here https://myviterbi.usc.edu/vasa/?PostingID=1234567992.Location: 105, 109, 115
Audiences: Undergrad
Contact: Christine Franks
Event Link: https://myviterbi.usc.edu/vasa/?PostingID=1234567992
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Inaugural USC Day of SCupport
Wed, May 04, 2016
Aerospace and Mechanical Engineering, Astronautical Engineering, Aviation Safety and Security Program, Alfred E. Mann Department of Biomedical Engineering, Center for Engineering Diversity, Thomas Lord Department of Computer Science, Daniel J. Epstein Department of Industrial and Systems Engineering, DEN@Viterbi, Information Sciences Institute, Technology & Applied Computing Program (TAC), Ming Hsieh Department of Electrical and Computer Engineering, Mork Family Department of Chemical Engineering and Materials Science, Sonny Astani Department of Civil and Environmental Engineering, USC Viterbi School of Engineering, Viterbi School of Engineering Alumni
Receptions & Special Events
On May 4, 2016, the USC community will unite to demonstrate the loyalty and SCupport of the Trojan Family. The goal: Rally as many people as possible to create the single largest day of giving participants in USC history.
Let us show the world that the collective power of the Trojan Family is unmatched anywhere. Every donor counts and all gifts, no matter the size, make a difference.
Connect with the USC community by using #DayofSCupport on Facebook, Twitter, LinkedIn, Instagram, and other social media. Your pride in USC can best be shown by our joining together to spread the word about USC Day of SCupport.
Thank you for your SCupport, and Fight On!
Visit DayofSCupport.usc.edu to plant your sword and show your support!
#DayofSCupportWebCast Link: dayofscupport.usc.edu
Audiences: Everyone Is Invited
Contact: James Morse
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Communications, Networks & Systems (CommNetS) Seminar
Wed, May 04, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Wotao Yin, UCLA
Talk Title: Theory and Applications of Asynchronous Parallel Computing
Series: CommNetS
Abstract: Single-core performance stopped improving around 2005. However, 64 CPU-cores workstations, thousand-core GPUs, and even eight-core cellphones are sold at affordable prices. To take advantages of multiple cores, we must parallelize our algorithms. In order for iterative algorithms to have strong parallel performance, it is important to reduce or even remove synchronizations so that the cores can keep running with the information they have, even when the latest one has not arrived. This talk explains why such async-parallel computing is both theoretically sound and practically attractive. In particular, we study fixed-point iterations of a nonexpansive operator and show that randomized async-parallel iterations will almost surely converge to a fixed point, as long as the operator has a fixed point and the step size is properly chosen. Roughly speaking, the convergence speed scales linearly with the number of cores when the number of cores is no more than the square root of the number of variables. As special cases, novel algorithms for linear equation systems, machine learning, distributed and decentralized optimization are introduced. On sparse logistic regression and others, new async-parallel algorithms run order-of-magnitude faster than the traditional sync-parallel algorithms. This is joint work with Zhimin Peng (UCLA), Yangyang Xu (IMA), and Ming Yan (Michigan State).
Biography: Wotao Yin is a professor in the Department of Mathematics of UCLA. His research interests lie in computational optimization and its applications in image processing, machine learning, and other inverse problems. He received his B.S. in mathematics from Nanjing University in 2001, and then M.S. and Ph.D. in operations research from Columbia University in 2003 and 2006, respectively. During 2006 - 2013, he was with Rice University. He won NSF CAREER award in 2008 and Alfred P. Sloan Research Fellowship in 2009. His recent work has been in optimization algorithms for large-scale and distributed signal processing and machine learning problems.
Host: Dr. Mahdi Soltanolkotabi
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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AI Seminar-Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
Thu, May 05, 2016 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Marc Smith, Social Media Research Foundation
Talk Title: Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
Series: Artificial Intelligence Seminar
Abstract: Networks are a data structure commonly found in any social media service that allows populations to author collections of connections. The Social Media Research Foundation's NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. With NodeXL, network charts become as easy to create as pie charts. Recent research created by applying the tool to a range of social media networks has already revealed the variations in network structures present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, Facebook and email networks will be presented.
Description: We now live in a sea of tweets, posts, blogs, and updates coming from a significant fraction of the people in the connected world. Our personal and professional relationships are now made up as much of texts, emails, phone calls, photos, videos, documents, slides, and game play as by face-to-face interactions. Social media can be a bewildering stream of comments, a daunting fire hose of content. With better tools and a few key concepts from the social sciences, the social media swarm of favorites, comments, tags, likes, ratings, updates and links can be brought into clearer focus to reveal key people, topics and sub-communities. As more social interactions move through machine-readable data sets new insights and illustrations of human relationships and organizations become possible. But new forms of data require new tools to collect, analyze, and communicate insights.
The Social Media Research Foundation (http://www.smrfoundation.org), formed in 2010 to develop open tools and open data sets, and to foster open scholarship related to social media. The Foundation's current focus is on creating and publishing tools that enable social media network analysis and visualization from widely used services like email, Twitter, Facebook, flickr, YouTube and the WWW. The Foundation has released the NodeXL project (http://nodexl.codeplex.com/), a spreadsheet add-in that supports "network overview discovery and exploration". The tool fits inside your existing copy of Excel in Office 2007, 2010 and 2013 and makes creating a social network map similar to the process of making a pie chart.
Using NodeXL, users can easily make a map of public social media conversations around topics that matter to them. Maps of the connections among the people who recently said the name of a product, brand or event can reveal key positions and clusters in the crowd. Some people who talk about a topic are more in the "center" of the graph, they may be key influential members in the population. NodeXL makes it a simple task to sort people in a population by their network location to find key people in core or bridge positions. NodeXL supports the exploration of social media with import features that pull data from personal email indexes on the desktop, Twitter, Flickr, YouTube, Facebook, Wikis, blogs and WWW hyper-links. The tool allows non-programmers to quickly generate useful network statistics and metrics and create visualizations of network graphs.
A book Analyzing Social Media Networks with NodeXL: Insights from a connected world is available from Morgan-Kaufmann. The book provides an introduction to the history and core concepts of social network analysis along with a series of step-by-step instructions that illustrate the use of the key features of NodeXL. The second half of the book is dedicated to chapters by a number of leading social media researchers that each focus on a single social media service and the networks it contains. Chapters on Twitter, email, YouTube, flickr, Facebook, Wikis, and the World Wide Web illustrate the network data structures that are common to all social media services.
A recent report co-authored with the Pew Research Center's Internet Project documents the discovery of the six basic forms of social media network structures present in social media platforms like Twitter. The report, "Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters" provides a step by step guide to analyzing social media networks.
Biography: Marc Smith is a sociologist specializing in the social organization of online communities and computer mediated interaction. Smith leads the Connected Action consulting group and lives and works in Silicon Valley, California. Smith co-founded and directs the Social Media Research Foundation (http://www.smrfoundation.org/), a non-profit devoted to open tools, data, and scholarship related to social media research.
Smith is the co-editor with Peter Kollock of Communities in Cyberspace (Routledge), a collection of essays exploring the ways identity; interaction and social order develop in online groups. Along with Derek Hansen and Ben Shneiderman, he is the co-author and editor of Analyzing Social Media Networks with NodeXL: Insights from a connected world, from Morgan-Kaufmann which is a guide to mapping connections created through computer-mediated interactions.
Smith's research focuses on computer-mediated collective action: the ways group dynamics change when they take place in and through social cyberspaces. Many "groups" in cyberspace produce public goods and organize themselves in the form of a commons (for related papers see: http://www.connectedaction.net/marc-smith/). Smith's goal is to visualize these social cyberspaces, mapping and measuring their structure, dynamics and life cycles. While at Microsoft Research, he founded the Community Technologies Group and led the development of the "Netscan" web application and data mining engine that allowed researchers studying Usenet newsgroups and related repositories of threaded conversations to get reports on the rates of posting, posters, crossposting, thread length and frequency distributions of activity. He contributes to the NodeXL project (http://nodexl.codeplex.com/) that adds social network analysis features to the familiar Excel spreadsheet. NodeXL enables social network analysis of email, Twitter, Flickr, WWW, Facebook and other network data sets.
The Connected Action consulting group (http://www.connectedaction.net) applies social science methods in general and social network analysis techniques in particular to enterprise and internet social media usage. SNA analysis of data from message boards, blogs, wikis, friend networks, and shared file systems can reveal insights into organizations and processes. Community managers can gain actionable insights into the volumes of community content created in their social media repositories. Mobile social software applications can visualize patterns of association that are otherwise invisible.
Smith received a B.S. in International Area Studies from Drexel University in Philadelphia in 1988, an M.Phil. in social theory from Cambridge University in 1990, and a Ph.D. in Sociology from UCLA in 2001. He is an adjunct lecturer at the College of Information Studies at the University of Maryland. Smith is also a Distinguished Visiting Scholar at the Media-X Program at Stanford University.
Host: Emilio Ferrara
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=2abc878bdcdd43c1bbba80cdc09562c41dLocation: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=2abc878bdcdd43c1bbba80cdc09562c41d
Audiences: Everyone Is Invited
Contact: Peter Zamar
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Fog: A new architecture for network distributed computation, communication, control and storage
Thu, May 05, 2016 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mung Chiang, Princeton University
Talk Title: Fog: A new architecture for network distributed computation, communication, control and storage
Abstract: Fog architecture distributes computation, communication, control and storage closer to end users along the cloud-to-things continuum, promising the potential benefits in cognition, efficiency, agility and latency, and possibly enabling applications in 5G, IoT and big data. This talk overviews the opportunities and challenges in this research area and discusses the emergent industry momentum in fog.
Biography: Mung Chiang is the Arthur LeGrand Doty Professor of Electrical Engineering at Princeton University. His research on networking received the 2013 Alan T. Waterman Award, the highest honor to US young scientists and engineers. His textbook "Networks: Friends, Money and Bytes" and online course reached 250,000 students since 2012. He founded the Princeton EDGE Lab in 2009, which bridges the theory-practice gap in edge networking research by spanning from proofs to prototypes. He co-founded a few startups in mobile, IoT and big data areas and co-founded the Open Fog Consortium. Chiang is the Director of Keller Center for Innovations in Engineering Education at Princeton University and the inaugural Chairman of Princeton Entrepreneurship Council.
Host: Urbashi Mitra, ubli@usc.edu, EEB 536, x04667
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 539
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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PhD Defense - Guan Pang
Thu, May 05, 2016 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: 3D Object Detection in Industrial Site Point Clouds
Location: SAL 322
Time: 12:00pm - 2:00pm, May 5th, 2016
PhD Candidate: Guan Pang
Committee members:
Prof. Ulrich Neumann (Chair)
Prof. Aiichiro Nakano
Prof. C.-C. Jay Kuo (Outside Member)
Abstract:
Detection of three dimensional (3D) objects in point clouds is a challenging problem. Existing methods either focus on a specific type of object or scene, or require prior segmentation, both of which are usually inapplicable on real-world industrial applications.
This thesis describe three methods to tackle the problem, with gradually improving performance and efficiency. The first is a general purpose 3D object detection method that combines Adaboost with 3D local features, without requirement for prior object segmentation. Experiments demonstrated competitive accuracy and robustness to occlusion, but this method suffers from limited rotation invariance. As an improvement, another method is presented with a multi-view detection approach that projects the 3D point clouds into several 2D depth images from multiple viewpoints, transforming the 3D problem into a series of 2D problems, which reduces complexity, stabilizes performance, and achieves rotation invariance. The problem is the huge amount of projected views and rotations that need to be individually detected, limiting the complexity and performance of 2D algorithm choice. Thus the third method is proposed to solve this with the introduction of convolutional neural network, because it can handle all viewpoints and rotations for the same class of object together, as well as predicting multiple classes of objects with the same network, without the need for individual detector for each object class. The detection efficiency is further improved by concatenating two extra levels of early rejection networks with binary outputs before the multi-class detection network.
3D object detection in point clouds is crucial for 3D industrial point cloud modeling. Prior efforts focus on primitive geometry, street structures or indoor objects, but industrial data has rarely been pursued. We integrate several algorithm components into an automatic 3D modeling system for industrial site point clouds, including modules for pipe modeling, plane classification and object detection, and solves the technology gaps revealed during the integration. The integrated system is able to produce classified models of large and complex industrial scenes with a quality that outperforms leading commercial software and comparable to professional hand-made models.
This thesis also describes an earlier work in multi-modal image matching which inspires later research in 3D object detection by 2D projections. Most existing 2D descriptors only work well on images of a single modality with similar texture. This proposal presents a novel basic descriptor unit called a Gixel, which uses an additive scoring method to sample surrounding edge information. Several Gixels in a circular array create the Gixel Array Descriptor, excelling in multi-modal image matching with dominant line features.
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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USC Viterbi V.I.P. Event in San Diego: Water Sustainability In Coastal Regions
Thu, May 05, 2016 @ 05:30 PM - 07:30 PM
Sonny Astani Department of Civil and Environmental Engineering, USC Viterbi School of Engineering, Viterbi School of Engineering Alumni
Receptions & Special Events
Join us on Thursday, May 5 for a presentation on Water Sustainability in Coastal Regions: Integrated Systems of Wastewater Re-use and Desalination by Amy Childress, PhD, Professor in the the Sonny Astani Department of Civil and Environmental Engineering Department.
As you may know, California is facing a historic drought and challenging issues related to water use. How does the research at USC Viterbi School of Engineering impact these issues and lead to breakthroughs and advances? How are we preparing to provide water to our growing communities?
Thursday, May 5, 2016 at 5:30 PM
Hosted by Debra Reed, BS '78, Chairman and CEO, Sempra Energy
Location: Sempra Energy, 488 8th Avenue, San Diego, CA 92101
Admission for USC alumni, USC parents, and guests is $10 per person and features a reception. All are welcome, but space is limited.
If you have any questions or would like to order over the phone please contact Maita Schuster at mrschust@usc.edu or 213.740.4880.
WHAT IS VITERBI INNOVATION PARTNERS (V.I.P.)?
V.I.P. is a yearly giving, engagement, and recognition program where alumni, parents and
friends come together in support of our engineering students and the advancement of
engineering here at USC Viterbi. For more information, please visit: http://viterbigiving.usc.edu/vip/Audiences: USC Alumni
Contact: James Morse
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CS Colloquium: John Lafferty (University of Chicago) - Statistical Learning Under Communication and Shape Constraints
Fri, May 06, 2016 @ 11:00 AM - 12:15 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: John Lafferty, University of Chicago
Talk Title: Statistical Learning Under Communication and Shape Constraints
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: Imagine that I estimate a statistical model from data, and then want to share my model with you. But we are communicating over a resource constrained channel. By sending lots of bits, I can communicate my model accurately, with little loss in statistical risk. Sending a small number of bits will incur some excess risk. What can we say about the tradeoff between statistical risk and the communication constraints? This is a type of rate distortion and constrained minimax problem, for which we provide a sharp analysis in certain nonparametric settings. We also consider the problem of estimating a high dimensional convex function, and develop a screening procedure to identify irrelevant variables. The approach adopts on a two-stage quadratic programming algorithm that estimates a sum of one-dimensional convex functions, beating the curse of dimensionality that holds under smoothness constraints. Joint work with Yuancheng Zhu and Min Xu.
Host: Yan Liu
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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PhD Defense - Shay Deutsch
Fri, May 06, 2016 @ 02:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Learning the Geometric Structure of High Dimensional Data using the Tensor Voting Graph
Location: SAL 322
Time: 2:30pm - 4:30pm, May 6th, 2016
PhD Candidate: Shay Deutsch
Committee members:
Prof. Gerard Medioni (Chair)
Prof. Aiichiro Nakano
Prof. Antonio Ortega (Outside Member)
Abstract:
This study addresses a range of fundamental problems in unsupervised manifold learning. Given a set of noisy points in a high dimensional space that lie near one or more possibly intersecting smooth manifolds, different challenges include learning the local geometric structure at each point, geodesic distance estimation, and clustering. These problems are ubiquitous in unsupervised manifold learning, and many applications in computer vision as well as other scientific applications would benefit from a principled approach to these problems.
In the first part of this thesis we present a hybrid local-global method that leverages the algorithmic capabilities of the Tensor Voting framework. However, unlike Tensor Voting, which can learn complex structures reliably only locally, our method is capable of reliably inferring the global structure of complex manifolds using a unique graph construction called the Tensor Voting Graph (TVG). This graph provides an efficient tool to perform the desired global manifold learning tasks such as geodesic distance estimation and clustering on complex manifolds, thus overcoming one of one of the main limitations of Tensor Voting as a strictly local approach. Moreover, we propose to explicitly and directly resolve the ambiguities near the intersections with a novel algorithm, which uses the TVG and the positions of the points near the manifold intersections.
In the second part of this thesis we propose a new framework for manifold denoising based processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian. The suggested approach, called MFD, uses the Spectral Graph Wavelet transform in order to perform non-iterative denoising directly in the graph frequency domain. To the best of our knowledge, MFD is the first attempt to use graph signal processing tools for manifold denoising on unstructured domains. We provide theoretical justification for our Manifold Frequency Denoising approach on unstructured graphs and demonstrate that for smooth manifolds the coordinate signals also exhibit smoothness. This is first demonstrated in the case of noiseless observations, by proving that manifolds with smoother characteristics creates more energy in the lower frequencies. Moreover, it is shown that higher frequency wavelet coefficients decay in a way that depends on the smoothness properties of the manifold, which is explicitly tied to the curvature properties. We then provide an analysis for the case of noisy points and a noisy graph, establishing results which tie the noisy graph Laplacian to the noiseless graph Laplacian characteristics, induced by the smoothness manifold properties and the graph construction properties.
Finally, the last part of this research merges the Manifold Frequency Denoising and the Tensor Voting Graph methods into a uniform framework, which allows us to denoise and analyze a general class of noisy manifolds with singularities also in the presence of outliers. We demonstrate that the limitation of the Spectral Graph Wavelets in its flexibility to analyze certain classes of graph signals can be overcome for manifolds with singularities using certain graph construction and regularization methods. This allows us to take into account global smoothness characteristics without over-smoothing in the manifold discontinuations (which correspond to high frequency bands of the Spectral Graph Wavelets), and moreover is robust to a large amount of outliers.
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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NL Seminar-THE TECHKNACQ PROJECT: BUILDING PEDAGOGICALLY TUNED READING LISTS FROM TECHNICAL CORPORA
Fri, May 06, 2016 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Gully Burns, USC/ISI
Talk Title: THE TECHKNACQ PROJECT: BUILDING PEDAGOGICALLY TUNED READING LISTS FROM TECHNICAL CORPORA
Series: Natural Language Seminar
Abstract: This work is geared towards developing pedagogically-tuned information retrieval systems to help learners select the most informative documents as a reading list for a given query over a given technical corpus. This work will enable learners to understand complex subjects more quickly. I will discuss our overall methodology, our efforts to study dependency between topics within a technical corpus and improvements to evaluating topic quality. I will describe ongoing efforts to study a document's pedagogical value to the end user and future directions for this enterprise.
Biography: Gully Burns' focus is to develop pragmatic knowledge engineering systems for scientists in collaboration with experts from the field of AI. He was originally trained as a physicist at Imperial College in London before switching to do a Ph.D. in neuroscience at Oxford. He came to work at USC in 1997, developing the 'NeuroScholar' project in Larry Swanson's lab before joining the Information Sciences Institute in 2006. He is as Research Lead at ISI.
Host: Xing Shi and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
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Sonny Astani Department Seminar
Fri, May 06, 2016 @ 03:00 PM - 03:30 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Meida Chan, Civil Engineering PhD student, USC
Talk Title: Development of a data acquisition-planning framework for hybrid data collection techniques to achieve blind-spots free 3D point cloud
Abstract: As-is 3D building models are valuable in many ways such as urban planning, historical building information storage, building renovation, facility management, building energy simulation and so on. Data acquisition for complete and accurate as-is 3D building reconstruction is a time consuming and labor intensive process. Establishing a data acquisition plan before or during the data acquisition process is necessary. As such, there has been extensive research on developing/advancing data acquisition planning algorithms with a single data acquisition technique. However, for buildings that have complex building structure and architectural elements, data collection process with a single data acquisition technique is not sufficient neither effective. The hypothesis behind this research study is that image-based technique (photogrammetry) and range-based technique (laser scanning) are complementary to each other and that the combination of the two techniques can improve the quality of the derived as-is 3D point cloud in terms of completeness and accuracy. As such, this research study will develop a framework that will provide an improved data acquisition process to support the creation of complete and accurate 3D models of existing buildings, while reducing the total cost of data acquisition by eliminating the need for site revisits and reworking of the data collection process.
Host: Lucio Soibelman
Location: Seeley G. Mudd Building (SGM) - 101
Audiences: Everyone Is Invited
Contact: Kaela Berry
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Sonny Astani Department Seminar
Fri, May 06, 2016 @ 03:30 PM - 04:00 PM
Sonny Astani Department of Civil and Environmental Engineering
Conferences, Lectures, & Seminars
Speaker: Prof. Xin Wang, Research Institute of Disaster Science, Tohoku University, Japan
Talk Title: 1D Wave Propagation Analysis and Shear-Wave Velocity Extraction of Super High-Rise Buildings Based on Ambient Vibrations Measurement
Abstract: Shaking modes of super high-rise buildings are very complex. In the first part of this study, three 2-D frame models of super high-rise buildings including bending and shear deflections in each member are used to simulate shear-wave propagation within the building. Different shaking modes at the lower stories of the three models are designed, each with a different mass-and-rigidity distributions, such as: (i) all stories shaking in a shear-bending mode, (ii) the lower eight stories shaking in pure bending mode, and (iii) the fourth to eighth stories shaking in pure bending mode. The wave reflections at the boundaries of stories with different shaking modes are examined from the response waves and the impulse responses with respect to the response of the top. Because of the wave interference, it is difficult to observe the travel path directly from the response waves. However, the travel path and the reflected waves can be observed clearly from impulse responses. For the stories shaking in pure bending mode, similar to the models (ii) and (iii), because there is no inter-story shear deformation, the apparent shear-rigidity of these stories seems infinite, which leads to zero shear-wave travel time and shear-wave velocities cannot be extracted successfully. In the second part of this study, 1D vertical shear-wave propagation in two super high-rise buildings are identified using ambient vibration response recorded by a portable array. The identified shear-wave propagation from the impulse response, including the boundary conditions, is compared with the simulated ones. Attempt is made to identify the shear-wave velocities for the individual stories.
Biography: Dr. Xin Wang is an Assistant Professor of the Research Institute of Disaster Science of Tohoku University in Japan. Her research combines knowledge of seismology and civil engineering and aims to disaster prevention from earthquakes. Her research topics include building damage detection and building damage causes examination due to ground shaking during big earthquake disasters, e.g. the 2008 Wenchuan Earthquake in China, the 2011 Great East Japan Earthquake, and the 2014 Ludian Earthquake in China. She is currently studying damage from the recent 2016 Kumamoto Earthquake in Japan. Her main recent research topics are Structural Health Monitoring of super high-rise buildings, and earthquake response recording systems using smart devices. Native of China, Dr. Wang received her B.S. and M.S. degrees in Civil Engineering from the Dalian Jiaotong University and Southeast University, respectively, after which she moved to Japan and earned her Ph.D. degree from the Aichi Institute of Technology.
Host: Maria Todorovska
Location: Seeley G. Mudd Building (SGM) - 101
Audiences: Everyone Is Invited
Contact: Kaela Berry
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Viterbi Pre-College Robotics Invitational
Sat, May 07, 2016 @ 07:30 AM - 02:00 PM
Viterbi School of Engineering K-12 STEM Center
Receptions & Special Events
A middle school and high school competition where student teams design, build and program robots that emulate a solution to a societal issue such as health care and medical technology, clean water, green energy solutions, etc.
More Information: Robotic Invitational Flyer.pdf
Location: Ronald Tutor Hall of Engineering (RTH) -
Audiences: Everyone Is Invited
Contact: Darin Gray/Viterbi Pre-College
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Communications, Networks & Systems (CommNetS) Seminar
Mon, May 09, 2016 @ 11:00 AM - 12:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Afonso Bandeira, MIT and NYU
Talk Title: On solving certain Semidefinite programs with low-rank solutions
Series: CommNetS
Abstract: Semidefinite programming has played an important role in mathematical signal processing, information theory, combinatorial optimization, etc. Although large Semidefinite programs are particularly challenging to solve, the solution one seeks is often low-rank. A now somehwat common approach is to constraint the search to low-rank solutions in the hope of reducing the computational cost. While such an approach can in general create new suboptimal local optima, it appears to work remarkably well in practice. We give the first (proof of concept) guarantee by showing that for a certain relevant semidefinite program this procedure indeed does not produce new local optima, and the global optima can be found.
Biography: Afonso is an Applied Mathematics Instructor at the MIT Math Department, with a half-time postdoctoral position sponsored by Philippe Rigollet. Starting in the Summer of 2016, he is going to join the Courant Institute of Mathematical Sciences as an Assistant Professor of Mathematics with a joint appointment in the Center for Data Science at NYU.
Host: Prof. Mahdi Soltanolkotabi
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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EE 598 Cyber-Physical Systems Seminar Series
Mon, May 09, 2016 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mohammad Al Faruque, Assistant Professor, University of California, Irvine
Talk Title: Cyber-Physical System Forensics for Cross-Domain Attack Analysis
Abstract: A Cyber-Physical System (CPS) is a cross-domain system integrating sub-systems from multiple domains connected through communication networks. Today, CPSs can be found in security-sensitive areas such as aerospace, automotive, energy, healthcare, manufacturing transportation, entertainment, and consumer appliances. Compared to the traditional information and embedded systems, due to the tight interactions between cyber and physical domains in CPSs, new vulnerabilities emerge from the boundary between cyber and physical domains. This enables new types of "cross-domain attacks" which include the following two concepts: First, observable energy flows from physical domain such as the analog emissions from acoustics, power flow, electromagnetic (EM), thermal, etc. provide the attackers new ways to access the critical information in the cyber domain. We call this types of attack as "Side Channel Attacks." Second, the classic cyber domain attacks on CPS may cause direct physical damage on them. The second types of attack in the scope of this talk will be called "Kinetic Cyber Attack." In the first part of this talk, I will be presenting our recent work on additive manufacturing systems (3D-printers), where we have demonstrated the vulnerability of a 3D-printer to confidentiality attacks. An additive manufacturing system is attacked through observable acoustic analog emissions in this work. See recent articles at Science (http://science.sciencemag.org/content/352/6282/132) and at ACM Communications (http://cacm.acm.org/news/199406-bad-vibrations-uci-researchers-find-security-breach-in-3d-printing-process/fulltext) about our work.
In the second half of the talk, I will discuss the bright side of the tight integration between cyber and physical domains, which may bring about the potential of physics-centric defense mechanisms to shield CPS against attackers. I will present how my group has achieved physical-layer security for V2X communication for an automotive cyber-physical system.
Biography: Mohammad Al Faruque is currently with the University of California Irvine (UCI), where he is a tenure track assistant professor and directing the Cyber-Physical Systems Lab. Prof. Al Faruque served as an Emulex Career Development Chair during October 2012 till July 2015. Before, he was with Siemens Corporate Research and Technology in Princeton, NJ. His current research is focused on system-level design of embedded systems and Cyber-Physical-Systems (CPS) with special interest on model-based design of software-integrated (multi)-physics systems, multi-core systems, CPS security, etc.
Prof. Al Faruque received the 2016 DATE Best Paper Award, the 2015 DAC Best Paper Award, the 2009 ICCAD Best Paper Award, the 2016 NDSS Distinguished Poster Award, the 2008 HiPEAC Paper Award, the 2012 DATE Best IP Award Nomination, the 2005 DAC Best Paper Award Nomination, the EECS Professor of the year 2015-16 Award, the 2015 UCI Chancellor's Award for Excellence in Fostering Undergraduate Research, and the 2015 Hellman Fellow Award. Besides 50+ IEEE/ACM publications in the premier journals and conferences, Prof. Al Faruque holds 4 US patents.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Estela Lopez
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USC Stem Cell Seminar: Eun Ji Chung, University of Chicago
Tue, May 10, 2016 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Eun Ji Chung, University of Chicago
Talk Title: Biomaterial design for tissue regeneration and theranostic applications
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Abstract: Nanomedicine and tissue engineering can harness biomaterial strategies to address limitations of clinical solutions. We will present how multivalent micelles are used as a tool for targeted detection and delivery of therapeutics to diseases, such as atherosclerosis and cancer. In addition, we will discuss how biodegradable, citric acid-based scaffolds can be combined with adult stem cells for complex regeneration of hierarchically-ordered tissues.
Host: Michael Bonaguidi
More Info: https://calendar.usc.edu/event/speaker_eun_ji_chung_university_of_chicago?utm_campaign=widget&utm_medium=widget&utm_source=USC+Event+Calendar%3A+Beta#.VvGYP3DFl04
Webcast: http://keckmedia.usc.edu/stem-cell-seminarWebCast Link: http://keckmedia.usc.edu/stem-cell-seminar
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
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Understanding and Improving Graph Algorithm Performance
Wed, May 11, 2016 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Mr. Scott Beamer, Computer Architecture PhD Candidate/UC Berkeley
Talk Title: Understanding and Improving Graph Algorithm Performance
Abstract: Graph processing is experiencing a renewed surge of interest as applications grow in importance in social networks analysis, recognition and the sciences. Unfortunately, graph algorithms often execute inefficiently on today's hardware platforms. In particular, graph algorithms are often able to simultaneously underutilize a platform's compute throughput and memory bandwidth.
In this talk, I will describe my work to identify these hardware bottlenecks and their causes. By understanding the main factors for graph algorithm performance, we can design hardware better suited for graph algorithms or improve graph algorithm software implementations. Through our characterization work, we find that contrary to the notion that graph algorithms have a random memory access pattern, we find well-tuned parallel graph codes exhibit substantial locality and thus experience a moderately high cache hit rate.
To understand these graph processing bottlenecks and the solutions to them requires a vertically integrated approach, ranging from algorithms with a novel breadth-first search algorithm to architecture with a detailed graph workload characterization. In between, this includes a graph domain-specific language, a performance model, and a benchmark suite. I will conclude the talk with our recent work on reducing memory communication via an algorithmic transformation.
Biography: Scott Beamer is a Computer Architecture PhD candidate at UC Berkeley advised by Krste Asanovic and David Patterson. He is currently investigating how to accelerate graph algorithms through software optimization and hardware specialization. In the past, he looked into how to best use monolithically integrated silicon photonics to create memory interconnects. He received a B.S. in Electrical Engineering and Computer Science and a M.S. in Computer Science, both from UC Berkeley.
Host: Professor Sandeep K. Gupta
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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PhD Defense - Andrew Jones - Rendering for Automultiscopic Displays
Wed, May 11, 2016 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Andrew Jones, PhD Candidate
Talk Title: Rendering for Automultiscopic Displays
Abstract: Title: Rendering for Automultiscopic Displays
Location: SAL 322
Time: 3:00pm - 5:00pm, May 11th, 2016
PhD Candidate: Andrew Jones
Committee Members:
Paul Debevec
Mark Bolas (outside member)
Jernej Barbiq
Abstract:
While a great deal of computer generated imagery is modelled and rendered in three dimensions, the vast majority of this 3D imagery is shown on two-dimensional displays. Various forms of 3D displays have been contemplated and constructed for at least one hundred years, but only recent advances in digital capture, computation, and display have made functional and practical 3D displays possible. In this thesis, I propose several designs that overcome some of the classic limitations of 3D displays. The displays are: autostereoscopic, requiring no special viewing glasses; omnidirectional, allowing viewers to be situated anywhere around it; and multiview, producing a correct rendition of the 3D objects with correct horizontal parallax and vertical perspective for any viewer around the display.
The first display prototype utilizes a spinning anisotropic mirror to distribute frames from a high-speed video projector to different viewers. Unfortunately, as the size and mass of the mirror increases, it becomes increasingly difficult to maintain a stable and rapid rotation speed. The second 3D display form has no moving mechanical parts, provides interactive content, and scales to large format displays. The key insight is that a large array of closely stacked projectors aimed at a stationary anisotropic screen is optically equivalent to a single high-speed projector aimed at a rotating anisotropic screen. Both types of display utilize new algorithms based on geometry and light filed based rendering. Applications for these displays include life-size interactive virtual characters, 3D teleconferencing, and time-offset conversations with 3D subjects.
Host: Andrew Jones
Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Ryan Rozan
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Viterbi School of Engineering PhD Hooding and Awards Ceremony
Thu, May 12, 2016 @ 08:30 AM - 11:00 AM
Viterbi School of Engineering Doctoral Programs
Receptions & Special Events
The Viterbi PhD Hooding and Awards Ceremony will take place on Thursday, May 12, 2016, from 8:30-11:00am in Bovard Auditorium. Tickets are required. The ceremony will be followed by a reception in Associates Park.
Location: George Finley Bovard Administration Building (ADM) -
Audiences: Everyone Is Invited
Contact: Jennifer Gerson
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NL Seminar-Towards Multi-Agent Communication-Based Language Learning
Fri, May 13, 2016 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Angeliki Lazaridou, University of Trento
Talk Title: Towards Multi-Agent Communication-Based Language Learning
Series: Natural Language Seminar
Abstract: One of the most ambitious goals of AI is to develop intelligent conversational agents able to communicate with humans and assist them in their tasks. Thus, communication and interaction should be at the core of the learning process of these agents; failure to integrate communication as their main building block raises concerns regarding their usability.
In this talk, I will propose an interactive multimodal framework for language learning. Instead of being passively exposed to large amounts of natural text, our learners (implemented as feed-forward neural networks) engage in cooperative referential games starting from a tabula rasa setup, and thus develop their own language from the need to communicate in order to succeed at the game. Preliminary experiments provide promising results, but also suggest that it is important to ensure that agents trained in this way do not develop an ad-hoc communication code only effective for the game they are playing.
Biography: Angeliki is a final year PhD student at the Center for Mind/Brain Sciences of the University of Trento. She received her MSc from the Saarland University, where she worked with Ivan Titov and Caroline Sporleder on Bayesian models for sentiment and discourse. She is currently working at the intersection between language and vision under the supervision of Marco Baroni.
Host: Xing Shi and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
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USC Stem Cell Seminar: Hiro Nakauchi, Stanford University
Tue, May 17, 2016 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Hiro Nakauchi, Stanford University
Talk Title: TBD
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Host: Justin Ichida
More Info: https://calendar.usc.edu/event/speaker_hiro_nakauchi_stanford_university?utm_campaign=widget&utm_medium=widget&utm_source=USC+Event+Calendar%3A+Beta#.VvGYk3DFl04
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
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AI SEMINAR
Thu, May 19, 2016 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Filippo Menczer, Professor, University of Indiana, Bloomington
Talk Title: The Spread of Misinformation in Social Media
Series: AI Seminar
Abstract: As social media become major channels for the diffusion of news and information, they are also increasingly attractive and targeted for abuse and manipulation. This talk overviews ongoing network analytics, data mining, and modeling efforts to understand the spread of misinformation online and offline. I present machine learning methods to detect astroturf and social bots, and outline initial steps toward computational fact-checking, as well as theoretical models to study how truthful and truthy facts compete for our collective attention. These efforts will be framed by a case study in which, ironically, our own research became the target of a coordinated disinformation campaign. Joint work with many members and collaborators of the Center for Complex Networks and Systems Research and the Indiana University Network Science Institute. This research is supported by the National Science Foundation, McDonnell Foundation, and DARPA. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of these funding agencies.
Biography: Filippo Menczer is a professor of informatics and computer science, adjunct professor of physics, and a member of the cognitive science program at Indiana University, Bloomington. He holds a Laurea in Physics from the University of Rome and a Ph.D. in Computer Science and Cognitive Science from the University of California, San Diego. Dr. Menczer has been the recipient of Fulbright, Rotary Foundation, and NATO fellowships, and a Career Award from the National Science
Foundation. He currently serves as director of the Center for Complex Networks and Systems Research, as a member of the Scientific Leadership Team of the IU Network Science Institute, and is a Fellow of the Institute for Scientific Interchange Foundation in Torino, Italy, a Senior Research Fellow of The Kinsey Institute, and an ACM Distinguished Scientist. He previously served as division chair in the IUB School of Informatics and Computing, and was Fellow-at-large of the Santa Fe Institute. His research focuses on Web science, social networks, social media, social computation, Web mining, distributed and intelligent Web applications, and modeling of complex information networks. His work has been covered in many US and international news
sources, including The New York Times, Wall Street Journal, Washington Post, NPR, CNN, BBC, Nature, and Science.
Host: Aram Galstyan
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=06801a2cef6c4a22bdb908bd2e186dbb1dLocation: Information Science Institute (ISI) - 1135 - 11th fl Large CR
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=06801a2cef6c4a22bdb908bd2e186dbb1d
Audiences: Everyone Is Invited
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AI SEMINAR
Fri, May 20, 2016 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: James Gleeson, Professor, University of Limerick
Talk Title: Competition and avalanches in social spreading phenomena
Series: AI Seminar
Abstract: We consider the spreading of memes (distinct pieces of information like ideas, hashtags, URLs, etc.) on a large directed social network, like Twitter. We use a branching-process model to describe how users choose among multiple sources of incoming information, similar to models used in other studies, which rely on intensive computational simulations to fit to data. In contrast, we here develop analytical insights into the respective roles of the network degree distribution, the memory-time distribution of users, and the competition between memes for the limited resource of user attention. The result is a form of self-organised criticality, which we dub competition-induced criticality. Using this analysis, we fit the model to data on Twitter hashtags, and predict features of the time-dependent data. This work is in collaboration with Yamir Moreno, Raquel A. Baños (both Universidad de Zaragoza), and Kevin O Sullivan (University of Limerick)
Biography: Professor James Gleeson holds the Chair in Industrial and Applied Mathematics at the University of Limerick. He is a graduate of University College Dublin in Mathematical Sciences and Mathematical Physics and received his PhD in Applied Mathematics from Caltech in 1999. Following his graduation from Caltech, he was a visiting assistant professor in Arizona State University, and then moved to University College Cork for 7 years, before taking up his current position at the University of Limerick. He is an Associate Editor of the Journal of Complex Networks; is a council member of the European Consortium for Mathematics for Industry, and was appointed to the Irish Research Council by Minister Sean Sherlock in 2013. As co-director of MACSI, the Mathematics Applications Consortium for Science and Industry, he leads research into applications of mathematics to real-world problems with significant economic and social impact. His research interests include stochastic dynamics and contagion on complex networks
Host: Kristina Lerman
Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=fff38167d27a42cc9a0ad981a082d7c71dLocation: Information Science Institute (ISI) - 1135 - 11th fl Large CR
WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=fff38167d27a42cc9a0ad981a082d7c71d
Audiences: Everyone Is Invited
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NL Seminar-Natural Language Communication with Computers
Fri, May 20, 2016 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Yonatan Bisk, USC/ISI
Talk Title: Natural Language Communication with Computers
Series: Natural Language Seminar
Abstract: We propose a framework for devising testable algorithms for bridging the communication gap between humans and robots. We begin with a setting in which humans give instructions to robots using unrestricted language commands, with instruction sequences aimed at building complex goal configurations in a blocks world. I will present details of our data-collection effort, and preliminary results on action understanding. Time permitting, I will present new baseline results for flipping the semantic parsing paradigm to address the problem of language generation, where a human performs commands produced by a machine to demonstrate basic two-way communication.
Biography: Yonatan Bisk received his PhD from UIUC in 2015 under Professor Julia Hockenmaier and is now a Postdoc with Daniel Marcu at ISI.
Host: Xing Shi and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 6th Floor -CR # 689; ISI-Marina del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
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PhD Defense - Yurong Jiang - Crowd-Sourced Collaborative Sensing in Highly Mobile Environments
Mon, May 23, 2016 @ 11:00 AM - 01:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yurong Jiang, PhD Candidate
Talk Title: Crowd-Sourced Collaborative Sensing in Highly Mobile Environments
Abstract: Title: Crowd-Sourced Collaborative Sensing in Highly Mobile Environments
Location: SAL 213
Time: 11am-1pm, May 23rd, 2016
PhD Candidate: Yurong Jiang
Committee Members:
Ramesh Govindan (chair)
Bhaskar Krishnamachari (outside member)
Gaurav Sukhatme
Abstract:
Networked sensing has revolutionized various aspects of our lives. In particular, it has allowed us to minutely quantify many aspects of our existence: what we eat, how we sleep, how we use our time, and so forth. We have seen such quantification from the smart devices we use daily, such as smartphones and wearable devices. Those smart devices usually have more than ten high precision sensors to sense both internal and external information. Another domain that will likely to see such quantification in near future is automobiles. Modern vehicles are equipped with several hundred sensors that govern the operation of internal vehicular subsystems. Those sensors from both smart devices and automobiles, coupled with online information (cloud computing, maps, traffic, etc.) and other databases as well as crowd-sourced information from other users, can enable various forms of context sensing, and can be used to design new features for both mobile devices and vehicles. We abstract those aspects for context sensing into three parts: mobile and vehicular sensing, cloud assistance and crowdsourcing. Though each part itself comes with different challenges, accurate context sensing usually requires a careful combination of one or more of the three aspects, which brings new challenges for designing and developing context sensing systems. In this dissertation, we focus on three challenges, Programmability, Accuracy and Timeliness, in designing efficient and accurate context sensing system for mobile devices and vehicles. We will leverage the mobile and vehicle sensors, cloud information and crowdsourcing, collectively to ease context sensing programming, improve context sensing accuracy and timeliness.
First, for Programmability, we focus on programming context descriptions using information from cloud and vehicle sensors. As more sensor-based apps are developed for vehicular platforms, we think many of these apps will be programmed using an event-based paradigm, where apps try to detect events and perform actions on detection. However, modern vehicles have several hundred sensors, these sensors can be combined in complex ways together with cloud information in order to detect some complicated context, e.g. dangerous driving. Moreover, these sensor processing algorithms may incur significant costs in acquiring sensor and cloud information. Thus, we propose a programming framework called CARLOG to simplify the task of programming these event detection algorithms. CARLOG uses Datalog to express sensor processing algorithms, but incorporates novel query optimization methods that can be used to minimize bandwidth usage, energy or latency, without sacrificing correctness of query execution. Experimental results on a prototype show that CARLOG can reduce latency by nearly two orders of magnitude relative to an unoptimized Datalog engine.
Second, for Accuracy, we focus on automotive positioning accuracy. Positioning accuracy is an important factor for all kinds of context sensing applications for automobiles. Lane-level precise positioning of an automobile can improve navigation experience and on-board application context awareness. However, GPS by itself cannot provide such precision in obstructed urban environments. We propose a system called CARLOC for lane-level positioning of automobiles which carefully incorporates the three aspects in context sensing. CARLOC uses three key ideas in concert to improve positioning accuracy: it uses digital maps to match the vehicle to known road segments; it uses vehicular sensors to obtain odometry and bearing information; and it uses crowd-sourced location estimates of roadway landmarks that can be detected by sensors available in modern vehicles. CARLOC unifies these ideas in a probabilistic position estimation framework, widely used in robotics, called the sequential Monte Carlo method. Through extensive experiments, we show our system achieves sub-meter positioning accuracy even in obstructed environment, which is an order of magnitude improvement over a high-end GPS device.
Finally, for context sensing applications, Timeliness is another important problem we need to take care of. We consider how to ensure the timeliness and availability of media content from mobile devices. Motivated by an availability gap for visual media, where images and videos are uploaded from mobile devices well after they are generated, we explore the selective, timely retrieval of media content from a collection of mobile devices. We envision this capability being driven by similarity-based queries posed to a cloud search front-end, which in turn dynamically retrieves media objects from mobile devices that best match the respective queries within a given time limit. We design and implement a general crowdsourcing framework called MediaScope that supports various geometric queries and contains a novel retrieval algorithm to maximize the retrieval of relevant information. In experiments on a prototype, our system achieves near optimal performance under different scenarios.
Host: Yurong Jiang
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Ryan Rozan
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USC Stem Cell Seminar: Brigid Hogan, Duke
Tue, May 24, 2016 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Brigid Hogan, Duke
Talk Title: The life of breath: Stem cells of the adult lung
Series: Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research at USC Distinguished Speakers Series
Abstract: The lung is composed of a tree-like system of branched tubes lined by a mucociliary epithelium. These carry air into the millions of thin-walled, highly vascularized peripheral gas-exchange sacs known as alveoli. The mesenchyme of the lung also contributes important stromal components, including airway and alveolar smooth muscle, fibroblasts/lipofibroblasts and an extensive vascular network. Cell turnover in both epithelial and mesodermal compartments of the mouse lung is normally very low, but in response to specific injuries and viral and bacterial infections there is extensive proliferation and differentiation of progenitor cells in different anatomical regions. A major goal has been to use lineage tracing to identify the specific subtypes of progenitor cells that help to maintain the lung at steady state and to promote repair. This has revealed unexpected plasticity in epithelial cell fate in response to changes in their local environment. 3D organoid assays have been established to screen for small molecules and pathways regulating progenitor cell behavior. Most recently Crispr/Cas9 technology has been used to identify the function of genes implicated in epithelial cell morphogenesis and differentiation.
Host: Francesca Mariani
More Info: https://calendar.usc.edu/event/speaker_brigid_hogan_duke?utm_campaign=widget&utm_medium=widget&utm_source=USC+Event+Calendar%3A+Beta#.VvGY6HDFl04
Webcast: http://keckmedia.usc.edu/stem-cell-seminarWebCast Link: http://keckmedia.usc.edu/stem-cell-seminar
Audiences: Everyone Is Invited
Contact: Cristy Lytal/USC Stem Cell
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The Multilevel Framework of System Safety: Foundational Research and Future Prospects
Wed, May 25, 2016 @ 04:30 PM - 06:00 PM
Astronautical Engineering, Systems Architecting and Engineering, USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Speaker: Mark S. Avnet, Assistant Professor of Industrial & Systems Engineering and Aerospace Engineering, Texas A&M University
Talk Title: The Multilevel Framework of System Safety: Foundational Research and Future Prospects
Series: SAE Distinguished Speaker Series
Abstract: Although it is generally accepted that accidents in complex systems result from many interacting factors that are generally analyzed separately using methods from distinct disciplines, researchers have only scratched the surface in developing collaborative and holistic approaches to address system safety. Recent research provides a system-level perspective but does not fully incorporate the richness of disciplinary methods developed over several decades. Based on the historical timeline of methodological approaches and semi-structured interviews with safety experts, this research develops a framework for examining system safety from a multilevel perspective, including proximate technical causes, human error, organizational culture, and societal influences. At each level of the framework, a set of methods, tools, and disciplinary knowledge exists and has been widely applied. Each disciplinary perspective provides a unique lens with which to examine system safety. The framework provides a platform for interdisciplinary research and can serve as the basis for specific practical guidelines for design, management, accident investigation, and policymaking. The System Safety Database, intended as the engine for operationalizing the framework, will generate reports and provide essential information to those responsible for ensuring safety, investigating accidents, conducting system safety research, and/or managing hazardous systems. The framework and accompanying database will provide stakeholders at all levels, from operators to policymakers, with the tools and perspectives needed to improve safety in complex socio-technical systems.
RSVP at the event link below.
Biography: Mark S. Avnet is an Assistant Professor of Industrial and Systems Engineering and Aerospace Engineering at Texas A&M University. He is the director of the System Architecture and Management Laboratory, which conducts interdisciplinary research on an array of socio-technical systems and engineering design problems. Mark holds an S.B. in Physics from the Massachusetts Institute of Technology, an M.A. in Science, Technology, and Public Policy from The George Washington University, and a Ph.D. in Engineering Systems from MIT. He has worked in industry as a software developer and has served at NASA Centers and Headquarters in a variety of technical and policy roles. Before joining the faculty at Texas A&M, Mark was a management consultant with McKinsey and Company, where he focused on the design and implementation of operational improvement programs with an emphasis on manufacturing optimization, procurement, and organizational change in the aerospace industry. His current research focuses on the technical and organizational factors that influence safety in complex systems.
Host: Prof. Azad Madni, INCOSE USC
More Info: http://goo.gl/forms/2dbgE1twsd
Webcast: https://bluejeans.com/249816249 ; Meeting ID: 249816249 ; Dial in: +1-888-240-2560More Information: May Speaker_Flyer.pdf
Location: Ronald Tutor Hall of Engineering (RTH) - 526
WebCast Link: https://bluejeans.com/249816249 ; Meeting ID: 249816249 ; Dial in: +1-888-240-2560
Audiences: Everyone Is Invited
Contact: Azad Madni
Event Link: http://goo.gl/forms/2dbgE1twsd
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PhD Defense - Seyed Jalal Kazemitabar Amirkolaei - "Scalable Processing of Spatial Queries"
Mon, May 30, 2016 @ 01:30 PM - 03:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Seyed Jalal Kazemitabar Amirkolaei, PhD Candidate
Talk Title: Scalable Processing of Spatial Queries
Abstract: In recent years, geospatial data have been produced in mass e.g., through billions of smartphones and wearable devices. Current exponential growth in data generation by mobile devices on the one hand, and the rate and complexity of recent spatial queries on the other hand, highlights the importance of scalable query processing techniques. Traditional database technology, which operates on centralized architectures to process persistent and less dynamic spatial objects does not meet the requirements for scalable geospatial data processing.
In this thesis, we specifically focus on two primary challenges in scaling spatial queries, i.e., the communication and computation costs, while guaranteeing the correctness of query results. We utilize techniques such as batch processing and use of parallelized framework to address these challenges.
We address the location tracking cost towards achieving scalability in communication-intensive queries. The location tracking cost between the moving objects and the query processing server is a key factor in processing many moving object continuous queries. The challenge is that increasing the number of queries and objects would require frequent location updates which results in draining the battery power on mobile devices. Thus, existing approaches would not scale unless query correctness is compromised. In this thesis, we propose batch processing of spatial queries as a method to optimize the location tracking cost to scale to large numbers of queries and objects without either compromising the query correctness or using excessive battery power. In our approach, the queries are categorized into independent groups and then processed in parallel. We specifically apply our approach to the proximity detection query and optimize the communication cost while processing millions of queries.
Processing some spatial queries has become more resource-intensive in recent years. This is due to various reasons such as the introduction of queries that are more computationally complex compared to the classic ones, as well as an increase in the input size (e.g., the number of GPS-enabled devices). In this thesis, we propose optimized algorithms and utilize MapReduce to process a complex spatial problem, i.e., the Multi-Criteria Optimal Location (MCOL) problem. First, we formalize it as a Maximal Reverse Skyline (MaxRSKY) query. For the first time, we present an optimized solution that scales to millions of objects over a cluster of MapReduce nodes. Specifically, rather than batch processing the query which is typical of a MapReduce solution, we first partition the space and run a precomputation phase where we identify potential regions hosting the optimum solution, and then load balance the regions across the Reducers in a dynamic way to reduce the total execution time.
Host: Seyed Jalal Kazemitabar Amirkolaei
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Ryan Rozan