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Conferences, Lectures, & Seminars
Events for July

  • NL Seminar-Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

    Fri, Jul 07, 2017 @ 03:00 PM - 04:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Amir Hossein Yazdavar, Wright State University

    Talk Title: Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

    Series: Natural Language Seminar

    Abstract: With the rise of social media, millions of people express their moods, feelings and daily struggles with mental health issues routinely on social media platforms like Twitter. Un like traditional observational cohort studies conducted through questionnaires and self reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential of detecting clinical depression symptoms which emulate the PHQ9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.



    Biography: Amir is a 2nd year PhD Researcher at Knoesis Center Wright State University, OH under the guidance of Prof. Amit P. Sheth, the founder and executive director of Knoesis Center. He is broadly interested in machine learning incl. deep learning and semantic web incl. creation and use of knowledge graphs and their applications to NLP NLU and social media analytics. He has a particular interest in the extraction of subjective information with applications to search, social and biomedical health applications. At Knoesis Center, he is working on several real world projects mainly focused on studying human behavior on the web via Natural Language Understanding, Social Media Analytics utilizing Machine learning Deep learning and Knowledge Graph techniques. In particular, his focus is to enhance statistical models via domain semantics and guidance from offline behavioral knowledge to understand users behavior from unstructured and large scale Social data.

    Host: Marjan Ghazvininejad and Kevin Knight

    More Info: http://nlg.isi.edu/nl-seminar/

    Location: Information Science Institute (ISI) - 6th Flr Conf Rm -# 689

    Audiences: Everyone Is Invited

    Posted By: Peter Zamar

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  • CS Colloquium: Andy Plumptre (Wildlife Conservation Society) - What we know and what we don't know about catching poachers: making ranger patrols more effective

    Mon, Jul 10, 2017 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Andy Plumptre, Wildlife Conservation Society

    Talk Title: What we know and what we don't know about catching poachers: making ranger patrols more effective

    Series: CS Colloquium

    Biography: Andy Plumptre, PhD is a tropical conservation scientist who has been working for the past 25 years in the Albertine Rift Region of Africa, one of the most biodiverse parts of the continent. His work has focused on many different issues related to the conservation of this region including developing new methods for surveying primates in forests, improving ranger patrolling in protected areas, conservation planning for the Albertine Rift, building national capacity to undertake monitoring and research, supporting transboundary conservation, and establishing new protected areas.

    Host: Milind Tambe

    Location: Ronald Tutor Hall of Engineering (RTH) - 526

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • CAIS Seminar: Dr. Andy Plumptre - How do you spend scarce conservation funding wisely: the science and art of conservation planning

    Tue, Jul 11, 2017 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Dr. Andy Plumptre,

    Talk Title: How do you spend scarce conservation funding wisely: the science and art of conservation planning

    Series: Center for AI in Society (CAIS) Seminar Series

    Abstract: Protected areas have been established for different reasons: protect natural scenery, protect species for sport hunting or use, conserve biodiversity and others. The global community has committed to protecting about 17% of the earths land and 10% of the marine realm for conservation. In many countries we have already achieved these figures but still don't conserve all species. This is because there has not been any systematic conservation planning used in identifying where should be conserved. Tools have been developed that can help plan and this talk will describe these and give some examples of their use in Africa.

    Biography: Andy Plumptre, PhD is a tropical conservation scientist who has been working for the past 25 years in the Albertine Rift Region of Africa, one of the most biodiverse parts of the continent. His work has focused on many different issues related to the conservation of this region including developing new methods for surveying primates in forests, improving ranger patrolling in protected areas, conservation planning for the Albertine Rift, building national capacity to undertake monitoring and research, supporting transboundary conservation, and establishing new protected areas.

    Host: Milind Tambe

    Location: Ronald Tutor Hall of Engineering (RTH) - 526

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • NL Seminar-Parsing Graphs with Regular Graph Grammars

    Fri, Jul 14, 2017 @ 03:00 PM - 04:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Sorcha Gilroy, University of Edinburgh

    Talk Title: Parsing Graphs with Regular Graph Grammars

    Series: Natural Language Seminar

    Abstract: Recently, several datasets have become available which represent natural language phenomena as graphs. Hyperedge Replacement Languages HRL have been the focus of much attention as a formalism to represent the graphs in these datasets. Chiang et al. 2013 prove that HRL graphs can be parsed in polynomial time with respect to the size of the input graph. We believe that HRL may be more expressive than is necessary to represent semantic graphs and we propose looking at Regular Graph Languages RGL Courcelle, 1991, which is a subfamily of HRL, as a possible alternative. We provide a top down parsing algorithm for RGL that runs in time linear in the size of the input graph.



    Biography: Sorcha is a 2nd year PhD student at the University of Edinburgh and is a student in the Center for Doctoral Training in Data Science. Her PhD is focused on formal languages of graphs for NLP and her supervisors are Adam Lopez and Sebastian Maneth. She completed her undergraduate degree in mathematical sciences at University College Cork and her masters degree in data science at the University of Edinburgh. She is at ISI as an intern in the NLP group.

    Host: Marjan Ghazvininejad and Kevin Knight

    More Info: http://nlg.isi.edu/nl-seminar/

    Webcast: http://webcastermshd.isi.edu/Mediasite/Play/c523b7ef95b443e8b29cfac3092e00081d

    Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey

    WebCast Link: http://webcastermshd.isi.edu/Mediasite/Play/c523b7ef95b443e8b29cfac3092e00081d

    Audiences: Everyone Is Invited

    Posted By: Peter Zamar

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  • CS Colloquium: Josiane Zerubia (INRIA, France) - Marked Point Processes for Object Detection and Tracking in High Resolution Images: Applications to Remote Sensing and Biology

    Tue, Jul 18, 2017 @ 10:30 AM - 11:30 AM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Josiane Zerubia, INRIA, France

    Talk Title: Marked Point Processes for Object Detection and Tracking in High Resolution Images: Applications to Remote Sensing and Biology

    Series: CS Colloquium

    Abstract: In this talk, we combine the methods from probability theory and stochastic geometry to put forward new solutions to the multiple object detection and tracking problem in high resolution remotely sensed image sequences. First, we present a spatial marked point process model to detect a pre-defined class of objects based on their visual and geometric characteristics. Then, we extend this model to the temporal domain and create a framework based on spatio-temporal marked point process models to jointly detect and track multiple objects in image sequences. We propose the use of simple parametric shapes to describe the appearance of these objects. We build new, dedicated energy based models consisting of several terms that take into account both the image evidence and physical constraints such as object dynamics, track persistence and mutual exclusion. We construct a suitable optimization scheme that allows us to find strong local minima of the proposed highly non-convex energy.

    As the simulation of such models comes with a high computational cost, we turn our attention to the recent filter implementations for multiple objects tracking, which are known to be less computationally expensive. We propose a hybrid sampler by combining the Kalman filter with the standard Reversible Jump MCMC. High performance computing techniques are also used to increase the computational efficiency of our method. We provide an analysis of the proposed framework. This analysis yields a very good detection and tracking performance at the price of an increased complexity of the models. Tests have been conducted both on high resolution satellite and microscopy image sequences.

    Keywords:
    Multiple object tracking, object detection, marked point process, Kalman filter, satellite image sequences, microscopy data sequences, high resolution.

    Biography: Josiane Zerubia has been a permanent research scientist at INRIA since 1989 and director of research since July 1995. She was head of the PASTIS remote sensing laboratory (INRIA Sophia-Antipolis) from mid-1995 to 1997 and of the Ariana research group (INRIA/CNRS/University of Nice), which worked on inverse problems in remote sensing and biological imaging, from 1998 to 2011. From 2012 to 2016, she was head of Ayin research group (INRIA-SAM) dedicated to models of spatio-temporal structure for high resolution image processing with a focus on remote sensing and skincare imaging.

    She has been professor at SUPAERO (ISAE) in Toulouse since 1999. Before that, she was with the Signal and Image Processing Institute of the University of Southern California (USC) in Los-Angeles as a postdoc. She also worked as a researcher for the LASSY (University of Nice/CNRS) from 1984 to 1988 and in the Research Laboratory of Hewlett Packard in France and in Palo-Alto (CA) from 1982 to 1984. She received the MSc degree from the Department of Electrical Engineering at ENSIEG, Grenoble, France in 1981, the Doctor of Engineering degree, her PhD and her 'Habilitation', in 1986, 1988, and 1994 respectively, all from the University of Nice Sophia-Antipolis, France.

    She is a Fellow of the IEEE (2003- ) and IEEE SP Society Distinguished Lecturer (2016-2017). She was a member of the IEEE IMDSP TC (SP Society) from 1997 till 2003, of the IEEE BISP TC (SP Society) from 2004 till 2012 and of the IVMSP TC (SP Society) from 2008 till 2013. She was associate editor of IEEE Trans. on IP from 1998 to 2002, area editor of IEEE Trans. on IP from 2003 to 2006, guest co-editor of a special issue of IEEE Trans. on PAMI in 2003, member of the editorial board of IJCV from 2004 till March 2013 and member-at-large of the Board of Governors of the IEEE SP Society from 2002 to 2004. She has also been a member of the editorial board of the French Society for Photogrammetry and Remote Sensing (SFPT) since 1998, of the Foundation and Trends in Signal Processing since 2007 and member-at-large of the Board of Governors of the SFPT since September 2014. She has been associate editor of the on-line resource Earthzine (IEEE CEO and GEOSS) since 2006.

    She was co-chair of two workshops on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'01, Sophia Antipolis, France, and EMMCVPR'03, Lisbon, Portugal), co-chair of a workshop on Image Processing and Related Mathematical Fields (IPRM'02, Moscow, Russia), technical program chair of a workshop on Photogrammetry and Remote Sensing for Urban Areas (Marne La Vallee, France, 2003), co-chair of the special sessions at IEEE ICASSP 2006 (Toulouse, France) and IEEE ISBI 2008 (Paris, France), publicity chair of IEEE ICIP 2011 (Brussels, Belgium), tutorial co-chair of IEEE ICIP 2014 (Paris, France), general co-chair of the workshop EarthVision at IEEE CVPR 2015 (Boston, USA) and a member of the organizing committee and plenary talk co-chair of IEEE-EURASIP EUSIPCO 2015 (Nice, France). She also organized and chaired an international workshop on Stochastic Geometry and Big Data at Sophia Antipolis, France, in November 2015. She is part of the organizing committees of the workshop EarthVision at IEEE CVPR 2017 (Honolulu, USA), GRETSI 2017 symposium (Juan les Pins, France) and ISPRS 2020 congress (Nice, France).

    Her main research interest is in image processing using probabilistic models. She also works on parameter estimation, statistical learning and optimization techniques.

    Host: Ram Nevatia, Antonio Ortega

    Webcast: https://bluejeans.com/137883736

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://bluejeans.com/137883736

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • CAIS Seminar: Dr. Amy Greenwald (Brown University) - The Interplay of Agent and Market Design

    Fri, Jul 21, 2017 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Dr. Amy Greenwald, Brown University

    Talk Title: The Interplay of Agent and Market Design

    Series: Center for AI in Society (CAIS) Seminar Series

    Abstract: We humans make hundreds of routine decisions daily. More often than not, the impact of our decisions depends on the decisions of others. As AI progresses, we are offloading more and more of these decisions to artificial agents. Dr. Greenwald's research is aimed at building AI agents that make effective decisions in multi-agent--part human, part artificial--environments. The bulk of her efforts in this space have been relevant to economic domains, mostly in service of perfecting market designs. In this talk, she will discuss AI agent design in applications ranging from renewable energy markets to online ad exchanges to wireless spectrum auctions

    Biography: Dr. Amy Greenwald is an Associate Professor of Computer Science at Brown University in Providence, Rhode Island. She studies game-theoretic and economic interactions among computational agents, applied to areas like autonomous bidding in wireless spectrum auctions and ad exchanges. In 2011, she was named a Fulbright Scholar to the Netherlands (though she declined the award). She was awarded a Sloan Fellowship in 2006; she was nominated for the 2002 Presidential Early Career Award for Scientists and Engineers (PECASE); and she was named one of the Computing Research Association's Digital Government Fellows in 2001. Before joining the faculty at Brown, Dr. Greenwald was employed by IBM's T.J. Watson Research Center. Her paper entitled "Shopbots and Pricebots" (joint work with Jeff Kephart) was named Best Paper at IBM Research in 2000.

    Host: Milind Tambe

    Location: Ronald Tutor Hall of Engineering (RTH) - 217

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • NL Seminar- Neural Sequence Models: Interpretation and Augmentation

    Fri, Jul 21, 2017 @ 03:00 PM - 04:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Xing Shi, USC/ISI

    Talk Title: Neural Sequence Models: Interpretation and Augmentation

    Series: Natural Language Seminar

    Abstract: Recurrent neural networks RNN have been successfully applied to various Natural Language Processing tasks, including language modeling, machine translation, text generation, etc. However, several obstacles still stand in the way: First, due to the RNN's distributional nature, few interpretations of its internal mechanism are obtained, and it remains a black box. Second, because of the large vocabulary sets involved, the text generation is very time consuming. Third, there is no flexible way to constrain the generation of the sequence model with external knowledge. Last, huge training data must be collected to guarantee the performance of these neural models, whereas annotated data such as parallel data used in machine translation are expensive to obtain. This work aims to address the four challenges mentioned above.

    To further understand the internal mechanism of the RNN, I choose neural machine translation NMT systems as a testbed. I first investigate how NMT outputs target strings of appropriate lengths, locating a collection of hidden units that learns to explicitly implement this functionality. Then I investigate whether NMT systems learn source language syntax as a by product of training on string pairs. I find that both local and global syntactic information about source sentences is captured by the encoder. Different types of syntax are stored in different layers, with different concentration degrees.

    To speed up text generation, I proposed two novel GPU-based algorithms. 1 Utilize the source/target words alignment information to shrink the target side run-time vocabulary. 2 Apply locality sensitive hashing to find nearest word embeddings. Both methods lead to a 2-3x speedup on four translation tasks without hurting machine translation accuracy as measured by BLEU. Furthermore, I integrate a finite state acceptor into the neural sequence model during generation, providing a flexible way to constrain the output, and I successfully apply this to poem generation, in order to control the pentameter and rhyme.

    Based on above success, I propose to work on the following. 1 Go one further step towards interpretation: find unit feature mappings, learn the unit temporal behavior, and understand different hyper-parameter settings. 2 Improve NMT performance on low-resource language pairs by fusing an external language model, feeding explicit target-side syntax and utilizing better word embeddings.




    Biography: Xing Shi is a PhD student at ISI working with Prof. Kevin Knight.

    Host: Marjan Ghazvininejad 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

    Posted By: Peter Zamar

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  • CAIS Seminar: Dr. Yevgeniy Vorobeychik (Vanderbilt University) - The Art and Science of Adversarial Machine Learning

    Mon, Jul 24, 2017 @ 11:00 AM - 12:00 PM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Dr. Yevgeniy Vorobeychik, Vanderbilt University

    Talk Title: The Art and Science of Adversarial Machine Learning

    Series: Center for AI in Society (CAIS) Seminar Series

    Abstract: The success of machine learning has led to numerous attempts to apply it in adversarial settings like spam and malware detection. The core challenge in this class of applications is that adversaries are not static data generators, but make a deliberate effort to either evade the classifiers deployed to detect them, or degrade the quality of the data used to train the classifiers. I will discuss our recent research into the problem of adversarial classifier evasion, specifically the theoretical foundations of black-box attacks on classifiers, and several of our efforts in designing evasion-robust classifiers on binary feature spaces, including a principled, theoretically-grounded, retraining method.

    Second, I will discuss scientific foundations of classifier evasion modeling. A dominant paradigm in the machine learning community is to model evasion in "feature space" through direct manipulation of classifier features. In contrast, the cyber security community developed several "problem space" attacks, where actual instances (e.g., malware) are modified, and features are then extracted from the evasive instances. I will show, through a case study of PDF malware detection, that feature-space models are a very poor proxy for problem space attacks. Then I will demonstrate a simple "fix" to identify a small set of features which are invariant (conserved) with respect to evasion attacks, and constrain these features to remain unchanged in feature-space models. Lastly, I will show that such conserved features exist and cannot be inferred using standard regularization techniques, but can be automatically identified for a given problem-space evasion model.

    Biography: Yevgeniy Vorobeychik is an Assistant Professor of Computer Science, Computer Engineering, and Biomedical Informatics at Vanderbilt University. He received Ph.D. (2008) and M.S.E. (2004) degrees in Computer Science and Engineering from the University of Michigan, and a B.S. degree in Computer Engineering from Northwestern University. His work focuses on adversarial reasoning in AI, computational game theory, security and privacy, network science, and agent-based modeling. He received an NSF CAREER award in 2017, was an invited early career spotlight speaker at IJCAI 2016.

    Host: Milind Tambe

    Location: Ronald Tutor Hall of Engineering (RTH) - 217

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Repeating EventSix Sigma Black Belt

    Tue, Jul 25, 2017

    Executive Education

    Conferences, Lectures, & Seminars


    Speaker: N/A, N/A

    Talk Title: Six Sigma Black Belt

    Abstract:
    Course Dates:
    Week 1: July 25-27, 2017
    Week 2: August 15-17, 2017
    Week 3: September 12-15, 2017
    Week 4: October 24-27, 2017

    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.

    NOTE: Participants must provide a windows based computer running Microsoft Office to the seminar.

    Host: CAPP

    More Info: https://gapp.usc.edu/professional-programs/short-courses/industrial-systems/six-sigma-black-belt

    Audiences: Everyone Is Invited

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    Posted By: Viterbi Professional Programs

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  • Six Sigma Black Belt

    Tue, Jul 25, 2017 @ 09:00 AM - 05:00 PM

    Executive Education

    Conferences, Lectures, & Seminars


    Speaker: TBD, TBD

    Talk Title: Six Sigma Black Belt

    Abstract: Earn Six Sigma Black Belt Certification through the USC Viterbi School of Engineering, with Trojan Family pricing available.

    Host: Professional Programs

    More Info: https://gapp.usc.edu/professional-programs/short-courses/industrial-systems/six-sigma-black-belt

    Audiences: Registered Attendees

    Posted By: Viterbi Professional Programs

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  • Repeating EventSix Sigma Black Belt

    Wed, Jul 26, 2017

    Executive Education

    Conferences, Lectures, & Seminars


    Speaker: N/A, N/A

    Talk Title: Six Sigma Black Belt

    Abstract:
    Course Dates:
    Week 1: July 25-27, 2017
    Week 2: August 15-17, 2017
    Week 3: September 12-15, 2017
    Week 4: October 24-27, 2017

    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.

    NOTE: Participants must provide a windows based computer running Microsoft Office to the seminar.

    Host: CAPP

    More Info: https://gapp.usc.edu/professional-programs/short-courses/industrial-systems/six-sigma-black-belt

    Audiences: Everyone Is Invited

    View All Dates

    Posted By: Viterbi Professional Programs

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  • Repeating EventSix Sigma Black Belt

    Thu, Jul 27, 2017

    Executive Education

    Conferences, Lectures, & Seminars


    Speaker: N/A, N/A

    Talk Title: Six Sigma Black Belt

    Abstract:
    Course Dates:
    Week 1: July 25-27, 2017
    Week 2: August 15-17, 2017
    Week 3: September 12-15, 2017
    Week 4: October 24-27, 2017

    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.

    NOTE: Participants must provide a windows based computer running Microsoft Office to the seminar.

    Host: CAPP

    More Info: https://gapp.usc.edu/professional-programs/short-courses/industrial-systems/six-sigma-black-belt

    Audiences: Everyone Is Invited

    View All Dates

    Posted By: Viterbi Professional Programs

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  • MHI CommNetS

    Mon, Jul 31, 2017 @ 02:00 PM - 03:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars


    Speaker: Dario Paccagnan, ETH Zurich

    Talk Title: Distributed optimization through game design

    Series: CommNetS

    Abstract: A fundamental challenge in multiagent systems is to design local control algorithms to ensure a desirable collective behaviour. In recent years, game theory has emerged as a valuable language to decouple a system-level objective into local utility functions to assign to each agent. In this talk, we consider a class of resource allocation problems and exemplify how tools from game theory can be leveraged to produce distributed algorithms with guaranteed performance, even in the presence of uncertainty. Our main contribution consists in showing how an improvement in the worst case performance comes at the expenses of the best case one. We conclude by discussing how this fundamental tradeoff can be overcome if we allow local algorithms to depend on a higher degree of system-level information.

    Biography: Dario Paccagnan is a doctoral student at the Automatic Control Laboratory, ETH Zurich since January 2015 under the supervision of Prof. John Lygeros. He is currently visiting the University of California, Santa Barbara hosted by Prof. Jason Marden. He earned his Laurea (B.Sc.) and Laurea Magistrale (M.Sc.) in Aerospace Engineering from the University of Padova in 2011 and 2014. In the same year he received the M.Sc. in Mathematical Modelling from the Technical University of Denmark, all with Honours. From January to August 2014 he has been a visiting scholar at Imperial College of London, hosted by Prof. Alessandro Astolfi. Dario's research interests lie at the intersection between Game Theory and Distributed Control. Applications include resource allocation problems, optimisation of energy systems and control of traffic networks.

    Host: Prof. Mihailo Jovanovic

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

    Audiences: Everyone Is Invited

    Posted By: Annie Yu

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