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SUNMONTUEWEDTHUFRISAT
Events for October 21, 2015
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Meet USC: Admission Presentation, Campus Tour, and Engineering TalkWed, Oct 21, 2015
Viterbi School of Engineering Undergraduate Admission
Receptions & Special Events
This half day program is designed for prospective freshmen and family members. Meet USC includes an information session on the University and the Admission process, a student led walking tour of campus, and a meeting with us in the Viterbi School. During the engineering session we will discuss the curriculum, research opportunities, hands-on projects, entrepreneurial support programs, and other aspects of the engineering school. Meet USC is designed to answer all of your questions about USC, the application process, and financial aid.
Reservations are required for Meet USC. This program occurs twice, once at 8:30 a.m. and again at 12:30 p.m. Please make sure to check availability and register online for the session you wish to attend. Also, remember to list an Engineering major as your "intended major" on the webform!Location: Ronald Tutor Campus Center (TCC) - USC Admission Office
Audiences: Everyone Is Invited
Contact: Viterbi Admission
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Canstruction
Wed, Oct 21, 2015
Viterbi School of Engineering Student Organizations
Student Activity
Help out people in need by donating canned food!! Students and faculty come together for this annual event to collect cans and donate them to the LA Food Bank. On the last day of the drive, we bring all the cans together to make a Canstruction. Collection is from 10/14 - 11/20.
Collection Bin Locations:
ACCT 101 Office
Crocker Library (in HOH)
Popovich Hall Rm 200
Deans Office BRI 100
Advising Office BRI 104Location: Various Locations (look at description)
Audiences: Everyone Is Invited
Contact: USC NOBE
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PhD Defense - Bo Wu
Wed, Oct 21, 2015 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PHD Defense -Bo Wu
Wed, Oct 21, 2015 @ 10:00am-12:00pm
SAL 213
PhD candidate: Bo Wu
Committee:
Craig A. Knoblock (Chair)
Cyrus Shahabi
Daniel O'Leary
Title: Iteratively Learning Data Transformation Programs from Examples
Abstract:
Data transformation is an essential preprocessing step in most data analysis applications. It often requires users to write many trivial and task-dependent programs, which is time consuming and requires the users to have certain programming skills. Recently, programming-by-example (PBE) approaches enable users to generate data transformation programs without coding. The user provides the PBE approaches with examples (input-output pairs). These approaches then synthesize the programs that are consistent with the given examples.
However, real-world datasets often contain thousands of records with various formats. To correctly transform these datasets, existing PBE approaches typically require users to provide multiple examples to generate the correct transformation programs. These approaches' time complexity grows exponentially with the number of examples and in a high polynomial degree with the length of the examples. Users have to wait a long time to see any response from the systems when they work on moderately complicated datasets. Moreover, existing PBE approaches also lack the support for users to verify the correctness of the transformed results so that they can determine whether they should stop providing more examples.
To address the challenges of existing approaches, we propose an approach that generates programs iteratively, which exploits the fact that users often provide multiple examples iteratively to refine programs learned from previous iterations. By collecting and accumulating key information across iterations, our approach can efficiently generate the new transformation programs by avoiding redundant computing. Our approach can also recommend potentially incorrect records for users to examine, which can save users effort in verifying the correctness of the transformation results.
To validate the approach in this thesis, we evaluated IPBE, the implementation of our iterative programming-by-example approach, against several state-of-the-art alternatives on various transformation scenarios. The results show that users of our approach used less time and achieved higher correctness compared to other alternative approaches.
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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Communications, Networks & Systems (CommNetS) Seminar
Wed, Oct 21, 2015 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Soheil Feizi, MIT
Talk Title: Learning (from) networks: fundamental limits, algorithms, and applications
Series: CommNetS
Abstract: Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging.
In this talk, we illustrate the use of spectral, combinatorial, and statistical inference techniques in several significant network science problems. First, we consider the problem of network alignment where the goal is to find a bijective mapping between nodes of two networks to maximize their overlapping edges while minimizing mismatches. To solve this combinatorial problem, we present a new scalable spectral algorithm, and establish its efficiency theoretically and experimentally over several synthetic and real networks. Next, we introduce network maximal correlation (NMC) as an essential measure to capture nonlinear associations in networks. We characterize NMC using geometric properties of Hilbert spaces and illustrate its application in learning network topology when variables have unknown nonlinear dependencies. Finally, we discuss the problem of learning low dimensional structures (such as clusters) in large networks, where we introduce logistic Random Dot Product Graphs, a new class of networks which includes most stochastic block models as well as other low dimensional structures. Using this model, we propose a spectral network clustering algorithm that possesses robust performance under different clustering setups. In all of these problems, we examine underlying fundamental limits and present efficient algorithms for solving them. We also highlight applications of the proposed algorithms to data-driven problems such as functional and regulatory genomics of human diseases, and cancer.
Biography: Soheil Feizi is a PhD candidate at Massachusetts Institute of Technology (MIT), co-supervised by Prof. Muriel Médard and Prof. Manolis Kellis. His research interests include analysis of complex networks and the development of inference and learning methods based on Optimization, Information Theory, Machine Learning, Statistics, and Probability, with applications in Computational Biology, and beyond. He completed his B.Sc. at Sharif University of Technology, awarded as the best student of his class. He received the Jacobs Presidential Fellowship and EECS Great Educators Fellowship, both from MIT. He has been a finalist in the Qualcomm Innovation contest. He received an Ernst Guillemin Award for his Master of Science Thesis in the department of Electrical Engineering and Computer Science at MIT.
Host: Dr. Salman Avestimehr
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Annie Yu
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Aerospace and Mechanical Engineering Seminar Series
Wed, Oct 21, 2015 @ 03:30 PM - 04:30 PM
Aerospace and Mechanical Engineering
Conferences, Lectures, & Seminars
Speaker: John Junkins, Professor of Aerospace Engineering at Texas A&M University
Talk Title: Astrodynamics for Modern Space Operations: Recent Analytical, Computational and Experimental Research
Series: Aerospace and Mechanical Engineering Seminar Series
Abstract: We address recent advances in analytical, computational and experimental studies aimed at challenges posed by the growth of space debris in near earth orbit. Since two large satellite collisions in 2007 and 2009, space debris has emerged as a challenge to the future utilization of low earth orbit. The Kessler Syndrome describes the potentially unstable increase in the population of space debris due to the increase in future probability of collisions. The future collision probability is increased by the large debris population wake of each collision. Some studies indicate that removing largest space derelict objects such as spent boosters and dead satellites is the most effective means for arresting growth of space debris, along with end-of-life de-orbit plans for all future launches. While present day collision risks are tolerable for most purposes, one or two additional large object collisions could increase the probability of collision to a point that future utilization of some orbit regimes could be severely degraded. This paper overviews two sets of research relevant to these challenges: (1) Methods for de-orbiting large derelict objects not designed for rendezvous and docking, and (2) new methods in astrodynamics for rapid/precise orbit propagation and mission analysis relevant to the challenges posed by orbit debris.
For both sets of research, we overview key issues, basic developments, and current status of closure between theory, computation and experiments. We discuss critical obstacles for these developments to be realized as operational technology for debris mitigation missions. Finally, we observe related applications where the methodology presented is potentially transferrable.
Host: Prof. Firdaus Udwadia
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Valerie Childress
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EE DISTINGUISHED LECTURER SERIES
Wed, Oct 21, 2015 @ 03:30 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Naomi Ehrich Leonard, Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering / Princeton University
Talk Title: On the Nonlinear Dynamics of Collective Decision-Making in Nature and Design
Series: Distinguished Lecturer Series
Abstract: The successful deployment of complex multi-agent systems requires well-designed, agent-level control strategies that guarantee system-level dynamics to be robust to disturbance and adaptive in the face of changes in the environment. In applications, such as mobile sensor networks, limitations on individual agents in sensing, communication, and computation create a further challenge. However, system-level dynamics that are both robust and adaptive are observed in animal groups, from bird flocks to fish schools, despite limitations on individual animals in sensing, communication, and computation. To better understand and leverage the parallels between networks in nature and design, a principled examination of collective dynamics is warranted. I will describe an analytical framework based on nonlinear dynamical systems theory for the realization of collective decision-making that allows for the rigorous study of the mechanisms of observed collective animal behavior together with the design of distributed strategies for collective dynamics with provable performance.
Biography: Naomi Ehrich Leonard is the Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and an associated faculty member of the Program in Applied and She received a John D. and Catherine T. MacArthur Foundation Fellowship in 2004, the UCSB Mohammed Dahleh Award in 2005, the Glenn L. Martin Medal from the University of Maryland in 2014, and the Nyquist Lecture Award from the ASME in 2014. She is a Fellow of the IEEE, ASME, SIAM, and IFAC. She received the B.S.E. degree in Mechanical Engineering from Princeton University in 1985 and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Maryland in 1991 and 1994. From 1985 to 1989, she worked as an engineer in the electric power industry.
Host: Sandeep Gupta, Justin Haldar, Urbashi Mitra
Webcast: https://bluejeans.com/694216021Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
WebCast Link: https://bluejeans.com/694216021
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Interviewing Strategies and Techniques
Wed, Oct 21, 2015 @ 04:00 PM - 05:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Discover tips on how to prepare for both technical and behavioral interviews, as well as the proper steps for follow-up!
Location: Seeley G. Mudd Building (SGM) - 123
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Services
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Turner Construction Information Session
Wed, Oct 21, 2015 @ 06:00 PM - 08:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Join representatives of this company as they share general company information and available opportunities.
Location: Seeley G. Mudd Building (SGM) - 101
Audiences: All Viterbi
Contact: RTH 218 Viterbi Career Services
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USC Graduate Engineering Info Session: Xi'an
Wed, Oct 21, 2015 @ 07:00 PM - 09:00 PM
Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
The information session will include a presentation on: Master's & Ph.D. programs available at the USC Viterbi School of Engineering, how to apply, scholarships, student life, and more. Students will also have the chance to ask questions and receive official brochures and handout information from USC.
Learn More and Register to Attend
Location: Westin, Xi'an, China
Audiences: Students with an undergraduate background in engineering, math or science
Contact: Laura Hartman
