SUNMONTUEWEDTHUFRISAT
Conferences, Lectures, & Seminars
Events for April
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CS Colloq: Aruna Balasubramania
Thu, Apr 01, 2010 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Talk Title:
Architecting Protocols to Improve Connectivity in Diverse Mobile NetworksSpeaker: Dr. Aruna BalasubramanianHost: Prof. Ramesh GovindanAbstract: Today, mobile networks and mobile devices enable applications for millions of users in diverse network environments. However, the potential of mobile networks has not not yet been fully realized because such networks are often unreliable and prone to disconnection. Mobile network environments, ranging from well-connected mesh networks to extremely sparse Delay Tolerant Networks (DTNs), face a variety of connectivity challenges due to unpredictable links, coverage holes, and losses in the wireless medium.In this talk, I will present a suite of protocols that overcome unreliability and improve connectivity in diverse mobile networks. At one end of the connectivity spectrum are sparsely connected DTNs, where the lack of an end-to-end path causes traditional routing protocols to break down. I will present RAPID, a DTN routing protocol that uses opportunistic replication coupled with a utility-driven algorithm to significantly improve a given routing metric. At the other end of the spectrum are well-connected mesh networks, where factors such as multipath fading lead to short disruptions that affect performance of interactive applications such as Voice over IP. I will present ViFi, a mesh network protocol that reduces disruptions using a probabilistic relaying algorithm that leverages overheard packets. Using RAPID and ViFi as examples, I will show how utility-driven and probabilistic algorithms can be used to implement protocols in a decentralized and highly uncertain wireless environment. Our deployment and experimental evaluation of these protocols in outdoor mobile testbeds demonstrate the effectiveness of our approach.I will briefly describe some of my more recent works on improving energy efficiency in mobile devices. Finally, I will conclude by outlining future research challenges in designing self-adapting protocols to allow seamless operation and in improving the usability of next generation mobile devices.Bio: Aruna Balasubramanian is a fifth year PhD candidate at the University of Massachusetts Amherst in the Department of Computer Science. Her research interests are broadly in systems and networking. She is specifically interested in mobile and sensor systems, delay tolerant networks, and energy efficiency. Her current research focus is on building robust wireless protocols that allow mobile access in diverse network environments. Her work has appeared in such conferences as ACM Sigcomm and ACM Mobicom. She is a Program Committee Co-chair for the Ph.D Forum held in conjunction with ACM MobiSys 2010, and was the General Co-Chair for the 2009 PhD Forum. She was a Program Committee Member of ACM CHANTS 2009. She is the recipient of a Microsoft Graduate Research Fellowship.Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Front Desk
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CS DLS: Prof. John Hopcroft
Tue, Apr 06, 2010 @ 04:15 PM - 05:50 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
DLS - Bekey KeynoteTalk Title: "Computer science theory to support research in the information age"Speaker: Prof. John Hopcroft - Cornell UniversityHosts: Prof. Shang-Hua Teng / Prof. Michael Arbib Abstract:The last forty years have seen computer science evolve as a major academic discipline. Today the field is undergoing a fundamental change. Some of the drivers of this change are the internet, the World Wide Web, large quantities of information in digital form and wide spread use of computers for accessing information. The change is requiring universities to revise the content of computer science programs. This talk will cover the changes in the theoretical foundations of computer science needed to support the information age.Bio:John Hopcroft is the IBM Professor of Engineering and Applied Mathematics at Cornell University. He started his career on the Faculty at Princeton in 1964 and moved to Cornell in 1967. In 1987 he became the chair of the Department of Computer Science. In 1993 he became Associate Dean for College Affairs, and in 1994 he became Dean of the College of Engineering in which job he served until 2001 when he returned to the Department of Computer Science.He earned his B.S. in Electrical Engineering from Seattle University in 1961 and Ph.D. in Electrical Engineering from Stanford University in 1964. He has honorary degrees from Seattle University, the National College of Ireland, the University of Sydney, St Petersburg State University. He is an honorary professor of the Beijing Institute of Technology and an Einstein Professor of the Chinese Academy of Sciences.. His current research interests are in the area of information capture and access.Hopcroft has served on numerous advisory boards including the Air Force Science Advisory Board, NASA's Space Sciences Board and National Research Council's Board on Computer Science and Telecommunications. In 1986 he was awarded the Turing Award by the Association for Computing Machinery and in 1992, President H. W. Bush appointed him to the National Science Board, which oversees the National Science Foundation. He is a member of the National Academy of Engineering, the National Academy of Science, and the American Academy of Arts and Sciences. He is a Fellow of the Association for Computing Machinery, the Institute of Electrical and Electronics Engineering, and the American Association for the Advancement of Science. He serves on the Packard Foundation's Science Advisory Board, Microsoft's Technical Advisory Board for Research Asia and the advisory boards of IIIT Delhi and the College of Engineering at Seattle University.
Location: Ethel Percy Andrus Gerontology Center (GER) - Auditorium
Audiences: Everyone Is Invited
Contact: CS Front Desk
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CS Colloq: Dr. Yan Liu
Thu, Apr 08, 2010 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Talk Title: Learning from Structured Data: Models and ApplicationsSpeaker: Dr. Yan LiuHost: Prof. Gaurav SukhatmeAbstract: Structured-input/output data emerge rapidly in a large number of applications, such as computational biology, social network analysis and climate modeling. In this talk, I will examine two tasks under this topic: one is given the data with underlying structures, how we can recover the graph structures automatically. Specifically, we develop Granger temporal models, an emerging collection of graphical model techniques that allow us to model causal relationships from time series data by appealing Granger causality with success in biology and climate application; the other tasks is given the data with structured-input, how we can make use of the structure information for better modeling. Specifically, we develop Topic-Link LDA model, a Bayesian hierarchical model for topic modeling and social network analysis from blog data.Bio: Yan Liu is a Research Staff Member at IBM TJ Watson Research. She received her M.Sc and Ph.D. degree from Carnegie Mellon University in 2004 and 2006. Her research interest includes machine learning and data mining algorithms for business analytics, social network analysis, computational biology and climate modeling. She has received several awards, including 2007 ACM Dissertation Award Honorable Mention, best application paper award in SDM 2007, winner of KDD Cup 2007, 2008, 2009 and INFORMS data mining competition 2008. She has published over 30 referred articles and served as a program committee of SIGKDD, CIKM, SIGIR, ICDM and several workshops in NIPS and ICDM.
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Front Desk
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CS Colloq: Fernando De la Torre
Wed, Apr 14, 2010 @ 11:00 AM - 12:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Talk Title: Learning Components for Human SensingSpeaker: Prof. Fernando De la Torre - Carnegie Mellon UniversityHost: Prof. Gerard MedioniAbstract:Providing computers with the ability to understand human behavior from sensory data (e.g. video, audio, or wearable sensors) is an essential part of many applications that can benefit society such as clinical diagnosis, human computer interaction, and social robotics. A critical element in the design of any behavioral sensing system is to find a good representation of the data for encoding, segmenting, classifying and predicting subtle human behavior. In this talk I will propose several extensions of Component Analysis (CA) techniques (e.g. kernel principal component analysis, support vector machines, and spectral clustering) that are able to learn spatio-temporal representations or components useful in many human sensing tasks.In the first part of the talk I will give an overview of several ongoing projects in the CMU Human Sensing Laboratory, including our current work on depression assessment from video, as well as hot-flash detection from wearable sensors. In the second part of the talk I will show how several extensions of the CA methods outperform state-of-the-art algorithms in problems such as temporal alignment of human behavior, temporal segmentation/clustering of human activities, joint segmentation and classification of human behavior, and facial feature detection in images. The talk will be adaptive, and I will discuss the topics of major interest to the audience.Biography:Fernando De la Torre received his B.Sc. degree in Telecommunications (1994), M.Sc. (1996), and Ph. D. (2002) degrees in Electronic Engineering from La Salle School of Engineering in Ramon Llull University, Barcelona, Spain. In 1997 and 2000 he was an Assistant and Associate Professor in the Department of Communications and Signal Theory in Enginyeria La Salle. Since 2005 he has been a Research Assistant Professor in the Robotics Institute at Carnegie Mellon University. Dr. De la Torre's research interests include computer vision and machine learning, in particular face analysis, optimization and component analysis methods, and its applications to human sensing. Dr. De la Torre co-organized the first workshop on component analysis methods for modeling, classification and clustering problems in computer vision in conjunction with CVPR'07, and the workshop on human sensing from video jointly with CVPR'06. He has also given several tutorials at international conferences (ECCV'06, CVPR'06, ICME'07, ICPR'08) on the use and extensions of component analysis methods. Currently he leads the Component Analysis Laboratory (http://ca.cs.cmu.edu ) and the Human Sensing Laboratory (http://humansensing.cs.cmu.edu ).
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: CS Front Desk
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Planning and Learning in Information Space
Thu, Apr 15, 2010
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Time: 12:00 PMRoom: EEB 248Talk Title: Planning and Learning in Information SpaceSpeaker: Professor Nicholas Roy Host: Professor Gaurav SukhatmeAbstract:Decision making with imperfect knowledge is an essential capability for unmanned vehicles operating in populated, dynamic domains. For example, a UAV flying autonomously indoors will not be able to rely on GPS for position estimation, but instead use on-board sensors to track its position and map the obstacles in its environment. The planned trajectories for such a vehicle must therefore incorporate sensor limitations to avoid collisions and to ensure accurate state estimation for stable flight -- that is, the planner must be be able to predict and avoid uncertainty in the state, in the dynamics and in the model of the world. Incorporating uncertainty requires planning in information space, which leads to substantial computational cost but allows our unmanned vehicles to plan deliberate sensing actions that can not only improve the state estimate, but even improve the vehicle's model of the world and how people interact with the vehicle.I will discuss recent results from my group in planning in information space; our algorithms allow robots to generate plans that are robust to state and model uncertainty, while planning to learn more about the world. I will describe the navigation system for a quadrotor helicopter flying autonomously without GPS using laser range-finding, and will show how these results extend to autonomous mapping, general tasks with imperfect information, and human-robot interaction.Bio:Nicholas Roy is an Associate Professor in the Department of Aeronautics & Astronautics at the Massachusetts Institute of Technology and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. He received his Ph. D. in Robotics from Carnegie Mellon University in 2003. His research interests include autonomous systems, mobile robotics, human-computer interaction, decision-making under uncertainty and machine learning.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: CS Front Desk
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CS Colloq: Niv Buchbinder - CANCELLED
Thu, Apr 15, 2010 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Talk Title: Randomized k-Server Conjecture (Online Algorithms meet Linear Programming)
Speaker: Dr. Niv Buchbinder
Host: Prof. David KempeTALK CANCELLEDAbstract:
The k-server problem is one of the most central and well studied problems in competitive analysis and is considered by many to be the "holy grail" problem in the field. In the k-server problem, there is a distance function d defined over an n-point metric space and k servers located at the points of the metric space. At each time step, an online algorithm is given a request at one of the points of the metric space, and it is served by moving a server to the requested point. The goal of an online algorithm is to minimize the total sum of the distances traveled by the servers so as to serve a given sequence of requests. The k-server problem captures many online scenarios, and in particular the widely studied paging problem.A twenty year old conjecture states that there exists a k-competitive online algorithm for any metric space. The randomized k-server conjecture states that there exists a randomized O(log k)-competitive algorithm for any metric space. While major progress was made in the past 20 years on the deterministic conjecture, only little is known about the randomized conjecture.We present a very promising primal-dual approach for the design and analysis of online algorithms. We survey recent progress towards settling the k-server conjecture achieved using this new framework.Bio:
Niv Buchbinder is a post-doctoral researcher at Microsoft Research, New England at Cambridge, MA.
Previously, he was a Ph.D. student in Computer Science at Technion, Israel Institute of Technology under the supervision of Prof Seffi Naor.
His main research interests are algorithms for combinatorial problems in offline and online settings. He is also interested in algorithmic game theory problems.
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Front Desk
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CS Colloq: Jesse Davis
Thu, Apr 15, 2010 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Talk Title: Predicate Invention and Transfer LearningSpeaker: Jesse DavisHost: Prof. Gaurav Sukhatme and Prof. Craig KnoblockAbstract: Machine learning has become an essential tool for analyzing biological and clinical data, but significant technical hurdles prevent it from fulfilling its promise. Standard algorithms make three key assumptions: the training data consist of independent examples, each example is described by a pre-defined set of attributes, and the training and test instances come from the same distribution. Biomedical domains consist of complex, inter-related, structured data, such as patient clinical histories, molecular structures and protein-protein interaction information. The representation chosen to store the data often does not explicitly encode all the necessary features and relations for building an accurate model. For example, when analyzing a mammogram, a radiologist records many properties of each abnormality, but does not explicitly encode how quickly a mass grows, which is a crucial indicator of malignancy. In the first part of this talk, I will focus on the concrete task of predicting whether an abnormality on a mammogram is malignant. I will describe an approach I developed for automatically discovering unseen features and relations from data, which has advanced the state-of-the-art for machine classification of abnormalities on a mammogram. It achieves superior performance compared to both previous machine learning approaches and radiologists.In the second part of this talk, I will address the problem of generalizing across different domains. Unlike machines, humans are able take knowledge learned in one domain and apply it to an entirely different one. Computationally, the missing link is the ability to discover structural regularities that apply to many different domains, irrespective of their superficial descriptions. This is arguably the biggest gap between current learning systems and humans. I will describe an approach based on a form of second-order Markov logic, which discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. This approach has successfully transferred learned knowledge between a molecular biology domain and a Web one. The discovered patterns include broadly useful properties of predicates, like symmetry and transitivity, and relations among predicates, like various forms of homophily.Bio: Jesse Davis is a post-doctoral researcher at the University of Washington. He received his Ph.D in computer science at the University of Wisconsin – Madison in 2007 and a B.A. in computer science from Williams College in 2002. His research interests include machine learning, statistical relational learning, transfer learning, inductive logic programming and data mining for biomedical domains.
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Front Desk
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CS Colloq: Barak Fishbain
Wed, Apr 21, 2010 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Talk Title: Network flow algorithms for sensor networks and visual data analysisSpeaker: Barak FishbainHost: Prof. Cyrus ShahabiAbstract:As digital environments become increasingly complex, and the tools for managing information become increasingly advanced, it is essential to assist users in selecting their short term and long term attentional focus. In this talk a novel graph-cut based approaches for multi-dimensional data analysis are presented. These methods are highly robust and most efficient which allows for the analysis of significantly large data sets. Air quality control and video segmentation are presented as representative applications.
Air quality control is addressed by the use of sensors network, where each sensor is mounted on a moving vehicle, for the purpose of detecting various threats. An example scenario is that of multiple taxi cabs each carrying a detector. The detectors' positions are continuously reported from GPS data. The level of detected risk is then reported from each detector at each position. The problem is to delineate the presence of a potentially dangerous source and its approximate location by identifying a small area that has an elevated concentration of reported risk. This problem of using spatially deployed mobile
detector networks to identify and locate risks is modeled and formulated. Then it is shown to be solvable in polynomial time and with a combinatorial network flow algorithm. The efficiency of the algorithm enables its use in real time, and in areas containing a large number of deployed detectors.
In video segmentation a typical goal is to group together similar objects, or pixels in the case of image processing. At the same time another goal is to have each group distinctly dissimilar from the rest and possibly to have the group size fairly large. These goals are often combined as a ratio optimization problem. State-of-the-art methods address these ratio problems by employing nonlinear continuous approaches, such as spectral techniques.
These spectral techniques deliver solutions in real numbers which are not feasible to the discrete partitioning problem. Furthermore, these continuous approaches are relatively computationally expensive. In this talk a novel graph-cut based approaches for optimally solving a set of segmentation ratio problems are presented. These algorithms guarantee optimal solution to the respective problem and consistent output between different runs.
These methods are most efficient which allows for the segmentation of significantly large video data sets.
The work was done with Prof. Dorit S. Hochbaum, University of California at Berkeley.Bio:Barak Fishbain received his Ph.D in EE from Tel-Aviv University, Israel in 2008. His research interests are Computer Vision, Image Processing, Video Surveillance and Medical Imaging. Currently he is a postdoctoral fellow in the Dept. of Industrial Engineering and Operations Research in the University of California at Berkeley, USA
Location: Charles Lee Powell Hall (PHE) - 333
Audiences: Everyone Is Invited
Contact: CS Front Desk
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CS Colloq: Chun-Nan Hsu - CANCELLED
Wed, Apr 21, 2010 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
CANCELLEDTalk Title: Accelerating Machine Learning by Aggressive ExtrapolationSpeaker: Chun-Nan HsuHost: Prof. Dennis McLeodAbstract:This talk presents how to accelerate statistical machine learning algorithms for large scale applications by aggressive extrapolation. Extrapolation methods, such as Aitken's acceleration, have the advantage that they can achieve quadratic convergence with an overhead linear to the dimension of the training data. However, they can be numerically unstable and their convergence is only locally guaranteed. We show that this can be fixed by a double extrapolation method. There are two options for the extrapolation, global or component-wise. Previously, it was not clear which option is more effective. We show a general condition to determine which option will be more effective and show how to apply the condition to the training of Bayesian networks and conditional random fields (CRF). Then we show that extrapolation can accelerate on-line learning with a method called Periodic Step-size Adaptation (PSA). We show that PSA is an approximation of a theoretic "single-pass" on-line learning method, which can converge to an empirical optimum in a single pass through the training examples. With a single-pass on-line learning method, disk I/O can be minimized when a training set is too large to fit in memory. Experimental results for a wide variety of models, including CRF, linear SVM, and convolutional neural networks, show that single-pass performance of PSA is always very close to empirical optimum. Finally, an application to gene mention tagging for biological text mining will be presented, which achieved the top score in BioCreative 2 challenge.Bio:Dr. Chun-Nan Hsu is a computer scientist at Information Sciences Institute (ISI). Prior to joining ISI, he is Research Fellow and Leader of the Adaptive Internet Intelligent Agents (AIIA) Lab at the Institute of Information Science, Academia Sinica, Taipei, Taiwan. His research interests include machine learning, data mining, databases and bioinformatics. He earned his M.S. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles, CA, in 1992 and 1996, respectively. In 1996, before he passed his doctoral oral exam, he had been offered a position as Assistant Professor at the Department of Computer Science and Engineering, Arizona State University, Tempe, AZ. He taught there for two years before he returned to Taiwan in 1998. Since 2005, he has been the principal investigator of the Advanced Bioinformatics Core, National Research Program in Genomic Medicine, Taiwan, and leading one of the largest research efforts in computerized drug design and discovery in Taiwan. In 2006, the first drug candidate due to the use of the software his team developed was commercialized. In 2007, his teams achieved the best scores in the BioCreative 2 text mining challenge. Dr. Hsu has published 78 scientific articles since 1993. Some of the articles have been cited more than 300 times. Currently, Dr. Hsu has been working on applying artificial intelligence to computational biology and bioinformatics.
Location: Mark Taper Hall Of Humanities (THH) - 114
Audiences: Everyone Is Invited
Contact: CS Front Desk
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CS Colloq: Augustin Chaintreau
Thu, Apr 22, 2010 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Talk Title: Addressing the Mobile Social Data DelugeSpeaker: Dr. Augustin ChaintreauHost: Prof. Ramesh GovindanAbstract: Extracting the full economic and scientific value of the "data deluge", which follows from the information produced and consumed online by individuals, is redefining the frontier of computer science.
Five years after one of the first experiments on mobile social dynamics, the size and scope of data collected or accessed through mobile devices have increased dramatically. In this talk, it is argued that understanding and releasing the potential of mobile social networks is possible provided that three key challenges are addressed: the lack of a guiding theory, the need to design algorithms exploiting social properties, and the presence of entities with competing goals.
Although these broad challenges are likely to exist for some time, this talk presents three examples in which these issues are addressed, using new analytical and algorithmic tools, to improve the efficiency of information dissemination.Bio: A. Chaintreau graduated in 2006 from Ecole Normale Superieure, Paris. He joined Technicolor (previously known as Thomson) to contribute to the creation of a new research lab on advanced communication platforms, where his research deals with mobile and social dynamics in information systems. Prior to that, he worked at Intel Research Cambridge where he was involved in conducting the first measurement campaign of opportunistic mobile dissemination. During his Ph.D, made under the supervision of Francois Baccelli, he worked with Alcatel Bell, and the IBM Watson T.J. Research Center, on characterizing scalable resource sharing systems in the presence of fairness and reliability constraints.
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Front Desk
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CS Colloq: Jan Vondrak
Fri, Apr 23, 2010 @ 10:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Talk Title: Randomized rounding in the matroid polytopeSpeaker: : Dr. Jan Vondrak, IBM AlmadenAbstract:The question how to round a fractional solution in a matroid polytope has emerged recently in various settings, ranging from welfare maximization and max-min allocation problems, to degree-bounded spanning trees and the asymmetric traveling salesman problem. In all these applications, it is useful to have a randomized rounding procedure which in expectation preserves the fractional solution, and satisfies strong concentration bounds for certain functions of the rounded solution. We propose a simple rounding procedure which has the above properties for any matroid and satisfies Chernoff-type concentration bounds for linear functions as well as monotone submodular functions. I will illustrate the usefulness of this technique on various examples.Joint work with Chandra Chekuri (UI Urbana-Champaign) and Rico Zenklusen (ETH Zurich).Bio:Jan Vondrak got his PhD from MIT in 2005, under the supervision of Michel Goemans. He spent 1 year as a postdoc at Microsoft Research and 3 years in the department of mathematics at Princeton University. Since 2009, he has been a research staff member at IBM Almaden.
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Front Desk
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CS Colloq: Amarjeet Singh
Tue, Apr 27, 2010 @ 03:30 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Talk Title: Application driven research in sensing and mobile computingSpeaker: Prof. Amarjeet SinghHost: Prof. Gaurav SukhatmeAbstract: Recent advancements in sensing technologies and mobile proliferation have significantly impacted several applied domains including environmental sensing and rural technology respectively. In sensing domain, applications that exhibit complex dynamics across both space and time and that can only be partially observed are of particular interest. In rural technology, primary focus is on applications that are user friendly even for an illiterate person. For both environmental sensing and rural technology, there exists a problem of constrained resources. Further, lack of prior use of advanced technology in both the domains necessitates that for high fidelity understanding of such environments an iterative approach wherein real world deployment experiences in application domain should guide both the advancements in systems as well as deployment methodology.In this talk, I will first present our real world deployment experiences (using Networked Info Mechanical System – NIMS, developed at UCLA) in several critical environmental sensing applications including monitoring pollution in rivers and algae growth in lakes.
Large spatial expanse of such applications, together with limited available resources (sensing time or battery capacity) for mobile agents motivated our further research in performing efficient path planning for these mobile agents. I will present novel approximation algorithms for solving this NP-hard problem of path planning for mobile agents in such complex environments. In particular, we used Gaussian Process modeling to accurately represent the dynamics we observed in our real world deployments. We exploit several machine learning concepts to provide strong theoretical guarantees for the proposed algorithms. Several field experiments were performed, in addition to using multiple real world sensing datasets, to validate the effectiveness of the proposed algorithms for real world sensing applications.I will then move from mobile sensing for environmental applications to application driven research using mobile computing for addressing several challenges in socially responsive applications, particularly in the context of developing countries. I will first present the contextual difference between challenges in developing and developed countries for applications in mobile computing. Motivated by India specific contexts, I will then present some early stage work in two specific application areas of mobile computing – healthcare and GPS-less localization.Bio:Amarjeet Singh is currently an Asst. Professor in Mobile and Ubiquitous Computing group at Indraprastha Institute of Information Technology, Delhi. He completed his MS and Phd in Electrical Engineering from UCLA in 2007 and 2009 respectively. He was awarded
2009 Chorafas Foundation Award for applied research with long range implications. He was also a recipient of 2007 Edward K. Rice outstanding MS student in School of Engineering at UCLA. From 2002 – 2004, he worked as Senior Research and Development Engineer at Tejas Networks, Bangalore, India. His undergraduate education was in Electrical Engineering from Indian Institute of Technology, Delhi in 2002.
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Front Desk
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Optimizing Sensing from Water to the Web
Fri, Apr 30, 2010 @ 10:30 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Talk to take place at USC-ISI in Marina del Rey
4676 Admiralty Way
Marina del Rey, CA 90292Title: "Optimizing Sensing from Water to the Web"Speaker: Andreas Krause, California Institute of TechnologyLocation: 11th floor Large Conference Room @ USC/ISIAbstract: Where should we place sensors to quickly detect contamination in drinking water distribution networks? Which blogs should we read to learn about the biggest stories on the web? These problems share a fundamental challenge: How can we obtain the most useful information about the state of the world, at minimum cost?Such sensing problems are typically NP-hard, and were commonly addressed using heuristics without theoretical guarantees about the solution quality. In this talk, I will present algorithms which efficiently find provably near-optimal solutions to large, complex sensing problems. Our algorithms exploit submodularity, an intuitive notion of diminishing returns, common to many sensing problems; the more sensors we have already deployed, the less we learn by placing another sensor. To quantify the uncertainty in our predictions, we use probabilistic models, such as Gaussian Processes. In addition to identifying the most informative sensing locations, our algorithms can handle more challenging settings, where sensors need to be able to reliably communicate over lossy links, where mobile robots are used for collecting data or where solutions need to be robust against adversaries, sensor failures and dynamic environments.I will also present results applying our algorithms to several real-world sensing tasks, including environmental monitoring using robotic sensors, activity recognition using a built sensing chair, deciding which blogs to read on the web, and a sensor placement competition.Bio: Andreas Krause is an assistant professor of Computer Science at the California Institute of Technology. He received his Ph.D. from Carnegie Mellon University in 2008. Krause is a recipient of an NSF CAREER award and the Okawa Foundation Research Grant recognizing top young researchers in telecommunications. His research on sensor placement and optimized information gathering received awards at several premier conferences, as well as the best research paper award of the ASCE Journal of Water Resources Planning and Management.
Location: 11th floor Large Conference Room @ USC/ISI
Audiences: Everyone Is Invited
Contact: CS Front Desk
