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Events for February 25, 2016
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Six Sigma Black Belt Thu, Feb 25, 2016
Executive Education
Conferences, Lectures, & Seminars
Abstract: Course Dates (15 Day Program)
Week 1: February 22-26, 2016
Week 2: April 11-15, 2016
Week 3: May 2-6, 2016
Learn the advanced problem-solving skills you need to implement the principles, practices and techniques of Six Sigma to maximize performance and cost reductions in your organization. During this three-week practitioner course, you will learn how to measure a process, analyze the results, develop process improvements and quantify the resulting savings. You will be required to complete a project demonstrating mastery of appropriate analytical methods and pass an examination to earn Six Sigma Black Belt Certificate.
This practitioner course for Six Sigma implementation provides extensive coverage of the Six Sigma process as well as intensive exposure to the key analytical tools associated with Six Sigma, including project management, team skills, cost analysis, FMEA, basic statistics, inferential statistics, sampling, goodness of fit testing, regression and correlation analysis, reliability, design of experiments, statistical process control, measurement systems analysis and simulation. Computer applications are emphasized.
More Info: https://gapp.usc.edu/professional-programs/short-courses/industrial-systems/six-sigma-black-belt
Audiences: Registered Attendees
Contact: Viterbi Professional Programs
Event Link: https://gapp.usc.edu/professional-programs/short-courses/industrial-systems/six-sigma-black-belt
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CS Colloquium: Chao Wang (Virginia Tech) - Symbolic Analysis for Detecting and Mitigating Errors in Concurrent Software
Thu, Feb 25, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Chao Wang, Virginia Tech
Talk Title: Symbolic Analysis for Detecting and Mitigating Errors in Concurrent Software
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
The use of multi-core architecture is now pervasive, spanning from embedded systems and smart phones, to commodity PCs, all the way to high-end servers and distributed systems. As such, developers must write concurrent software. However, writing correct and efficient concurrent software is difficult. Although automated analysis to aid in their development would be invaluable, existing methods are either fast but inaccurate, or accurate but slow, due to the inherent difficulty in circumventing the path and interleaving explosion problem. In this talk, I will introduce a series of symbolic predictive analysis methods for analyzing concurrent software. This analysis consists of two steps. First, we derive a predictive model from the execution traces collected at run time as well as the software code. The model captures not only the given executions but also the alterative interleavings of events in these executions. Then, we use symbolic analysis to check if errors exist in these alternative interleavings. This is accomplished by capturing these interleavings and the error conditions using a set of logic formulas and deciding them using a Satisfiability Modulo Theory (SMT) solver. Although our primary focus is to reduce the cost associated with analyzing and verifying concurrent software, the predictive model and related analysis techniques are also useful in addressing issues related to performance and security.
Biography: Chao Wang is an Assistant Professor of the ECE Department and the CS Department (by courtesy) of Virginia Tech. He received the ONR Young Investigator award in 2013 and the NSF CAREER award in 2012. His area of specialization is Software Engineering and Formal Methods, with emphasis on concurrency, formal verification, and program synthesis. He published a book and more than fifty papers in top venues of related field including ICSE, FSE, ASE, ISSTA, CAV, PLDI, and POPL. He received the FMCAD Best Paper award in 2013, the ACM SIGSOFT Distinguished Paper award in 2010, the ACM TODAES Best Paper of the Year award in 2008, and the ACM SIGDA Outstanding PhD Dissertation award in 2004. Dr. Wang received his PhD degree from the University of Colorado at Boulder in 2004. From 2004 to 2011, he was a Research Staff Member at NEC Laboratories of America in Princeton, NJ, where he received a Technology Commercialization award in 2006.
Host: CS Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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ICT Distinguished Lecture Series
Thu, Feb 25, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science, USC Viterbi School of Engineering
Conferences, Lectures, & Seminars
Speaker: James W. Pennebaker, Social Psychologist and the Centennial Liberal Arts Professor of Psychology at the University of Texas at Austin
Talk Title: The synchronous massive online course (SMOC) and new model of online education
Abstract: Growing out of research on social and personality psychology and computerized text analysis, Pennebaker and his colleague Sam Gosling have developed a live online class that relies on daily testing, small online discussion groups, and a TV talk show format. Based on four classes ranging from 800 to 1500 students, significant gains were seen in performance in the class over previous courses. More striking, students taking the SMOC do better in courses in subsequent semesters and achieve large reductions in the achievement gap.
Biography: James Pennebaker is the Regents Centennial Professor of Psychology and Executive Director of Project 2021 at the University of Texas at Austin. Author of over 300 publications and 9 books, his work on physical symptoms, expressive writing, and language psychology is among the most cited in psychology and the social sciences. He has received multiple research and teaching awards.
Light refreshments will be served.
Host: Stefan Scherer, ICT
Location: Institute For Creative Technologies (ICT) - Theater
Audiences: Everyone Is Invited
Contact: Orli Belman/ICT
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PhD Defense - Chin-Kai Chang "Autonomous Mobile Robot Localization and Navigation in Urban Environment"
Thu, Feb 25, 2016 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Defense - Chin-Kai Chang "Autonomous Mobile Robot Localization and Navigation in Urban Environment" 2/25; 3pm HNB RM 15
Date and Location: Thursday, February 25th, 3:00 pm at HNB RM15.
Title: Autonomous Mobile Robot Localization and Navigation in Urban Environment
PhD Candidate: Chin-Kai Chang
Committee: Laurent Itti (chair), Hao Li, Bosco Tjan(outside member)
Unmanned ground vehicles (UGV) is one of the highly versatile carriers for transportation, surveillance and search and rescue task. For the service type mobile robot that ability to travel through indoor and outdoor environment may encounter complex challenges different than that of street vehicles. The urban pedestrian environment is typically GPS-denied which demands a further integrated approach of perception, estimation, planning and motion control to surmount. In this thesis, we present the design and implementation of Beobot 2.0 - an autonomous mobile robot that operates in unconstrained urban environments. We developed a distributed architecture to satisfy the requirement for computationally intensive algorithms. Furthermore, we propose several biological-inspired visual recognition methodologies for indoor and outdoor navigation. We describe novel vision algorithms base on saliency, gist, image contour and region segment to construct several perception modules such as place recognition, landmark recognition, and road lane detection. To conquer the latencies and update frequencies of each perception module while performing real-time navigation task. We further investigate hierarchical map representation to fuse the quick, yet limited state information while time-consuming but higher discriminating data remains in processing. We validated our system using a ground vehicle that autonomously traversed several times in multiple outdoor routes, each 400m or longer, in a university campus. The routes featured different road types, environmental hazards, moving pedestrians, and service vehicles. In total, the robot logged over 10km of successfully recorded experiments, driving within a median of 1.37m laterally of the center of the road, and localizing within 0.97m (median) longitudinally of its true location along the route.
Location: Hedco Neurosciences Building (HNB) - 015
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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CS Colloquium: Hyun Oh Song (Stanford) -Beyond supervised pattern recognition: Efficient learning with latent combinatorial structure
Thu, Feb 25, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Hyun Oh Song, Stanford
Talk Title: Beyond supervised pattern recognition: Efficient learning with latent combinatorial structure
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Supervised pattern recognition with 10^6 training data and 10^9 layered parameters has brought tremendous advances in artificial intelligence. However, there are two main limitations to this approach: 1) The knowledge learned in one area doesn't easily transfer to another and 2) supervising every single task is not only infeasible but also requires huge amounts of human labeled data which is costly and time consuming. In this talk, I will suggest a unifying framework which jointly reasons the prediction variable and the underlying latent combinatorial structure of the problem as a way to address such limitations. To demonstrate the practical benefits of the approach, we explore classification, localization, clustering, and retrieval tasks under settings that go beyond fully supervised pattern recognition.
Biography: Hyun Oh Song is a postdoc in SAIL in the computer science department at Stanford University. He received Ph.D. in Computer Science at UC Berkeley in late 2014 under the supervision of Prof. Trevor Darrell. He is a recipient of five year Ph.D. fellowship from Samsung Lee Kun Hee Scholarship Foundation. His research interest lies at the intersection between machine learning, computer vision, and optimization. He has an academic website at http://ai.stanford.edu/~hsong.
Host: CS Department
Webcast: https://bluejeans.com/501895444Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/501895444
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Institute of Industrial Engineers General Meeting featuring Accenture
Thu, Feb 25, 2016 @ 07:00 PM - 08:00 PM
Viterbi School of Engineering Student Organizations
Student Activity
Come out to the Institute of Industrial Engineer's General Meeting featuring Accenture! Accenture reps will be there to answer questions and give you all of the details about careers at the company. This is a great opportunity for anyone interested in a career in consulting, especially underclassmen. We can't wait to see you all there!
Location: Von Kleinsmid Center For International & Public Affairs (VKC) - 211
Audiences: Everyone Is Invited
Contact: USC Institute of Industrial Engineers
