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Conferences, Lectures, & Seminars
Events for July
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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
Thomas Lord Department of 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
Contact: 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
Thomas Lord Department of 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
Contact: Assistant to CS chair
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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
Thomas Lord Department of 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/137883736Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://bluejeans.com/137883736
Audiences: Everyone Is Invited
Contact: 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
Thomas Lord Department of 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
Contact: Assistant to CS chair
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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
Thomas Lord Department of 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
Contact: Assistant to CS chair
