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  • Rising Stars in EECS: 2015 - Eva Song, Princeton University. “A New Approach to Lossy Compression and Applications to Security,”
    the Wyner s soft covering lemma yields simple achievability proofs for classical source coding problems The cases of the point to point rate distortion function the rate distortion function with side information at the decoder i e the Wyner Ziv problem and the multi terminal source coding inner bound i e the Berger Tung problem are examined Furthermore a non asymptotic analysis is used for the point to point case to examine the upper bound on the excess distortion provided by this method The likelihood encoder is also compared both in concept and performance to a recent alternative technique using properties of random binning Also the likelihood encoder source coding technique is further used to obtain new results in rate distortion based secrecy systems Several secure source coding settings such as using shared secret key and correlated side information are investigated It is shown mathematically that the rate distortion based formulation for secrecy fully generalizes the traditional equivocation based secrecy formulation The extension to joint source channel security is also considered using similar encoding techniques The rate distortion based secure source channel analysis has been applied to optical communication for reliable and secure delivery of an information source through an

    Original URL path: https://risingstars15-eecs.mit.edu/eva-song/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - Veronika Strnadova-Neeley, UC Santa Barbara. “Efficient Clustering and Data Reduction Methods for Large-Scale Structured Data”
    has focused on developing efficient methods to analyze vast amounts of information My contribution to this line of research focuses on new algorithms for large scale clustering and data reduction by exploiting inherent low dimensional structure to overcome the challenges of significant amounts of missing and erroneous entries In particular over the past few years together with collaborators from Lawrence Berkeley National Lab UC Santa Barbara UC Berkeley and the Joint Genome Institute I have developed a fast algorithm for the linkage group finding phase of genetic mapping as well as a novel data reduction method for analyzing genetic mapping data The efficiency of these algorithms has helped to produce accurate maps for large complicated genomes such as wheat by relying on assumptions on the underlying ordered structure of the data The efficiency and accuracy of these methods suggests that in order to further advance state of the art clustering and data reduction methods we should be looking closer at the structure of the data from a given application of interest Assumptions on this structure may lead to much faster algorithms without losing much in terms of solution quality even with high amounts of missing or erroneous data entries In

    Original URL path: https://risingstars15-eecs.mit.edu/veronika-strnadova-neeley/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - Huan Sun, UC Santa Barbara. “Intelligent and Collaborative Question Answering”
    knowledge bases to answer user queries It successfully deals with the challenge that answers to user queries could not be simply retrieved by exact keyword and graph matching due to different information representations ii Combining knowledge bases with the Web We recognized that knowledge bases are usually far from complete and information required to answer questions may not always exist in knowledge bases This framework mines answers directly from large scale web resources and meanwhile employs knowledge bases as a significant auxiliary to boost question answering performance 2 Human collaborative query resolution We made the first attempt to quantitatively analyze expert routing behaviors i e how an expert decides where to transfer a question when she could not solve it A computational routing model was then developed to optimize team formation and team communication for more efficient problem solving Future directions of my research include leveraging both machines and humans for better question answering and decision making in various domains such as healthcare and business intelligence Bio Huan Sun is a Ph D candidate in the Department of Computer Science at the University of California Santa Barbara and is expected to graduate in September 2015 Her research interests lie in

    Original URL path: https://risingstars15-eecs.mit.edu/huan-sun/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - Ewa Syta, Yale University. “Certificate Cothority: Towards Trustworthy Collective CAs”
    of the entire PKI and in turn everyone on the Internet Due to this weakest link security hackers have stolen the master keys of CAs such as DigiNotar and Comodo and successfully generated fake certificates for website spoofing and man in the middle attacks We propose to replace current high value certificate authorities with a certificate cothority CC a practical system which embodies strongest link security by allowing all participants to validate certificates before they are issued and endorsed and therefore proactively prevent their misuse We build certificate cothorities using an instantiation of a collective authority cothority an architecture we propose to enable thousands of participants to witness validate and co sign an authority s public actions with moderate delays and costs Each of potentially thousands of hosts comprising a certificate cothority independently validates each new batch of certificates either contributing a share of a collective digital signature or withholding it and raising an alarm if misbehavior is detected This collective signature attests to the client that not just one but many ideally thousands well known servers independently checked and signed off on a certificate Therefore a certificate cothority guarantees strongest link security whose strength increases as the collective grows

    Original URL path: https://risingstars15-eecs.mit.edu/ewa-syta/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - Rabia Tugce Yazicigil, Columbia University. “Enabling 5/Next-G Wireless Communications with Energy-Efficient, Compressed Sampling Rapid Spectrum Sensors”
    shared spectrum access are expected to deliver superior spectrum efficiency over existing scheduled access systems We focus on lower tiered smart devices that evaluate the spectrum dynamically and opportunistically use the underutilized spectrum These smart devices require spectrum sensing for interferer avoidance The integrated interferer detectors need to be fast wideband and energy efficient We are developing quadrature analog to information converters QAIC a novel compressed sampling CS technique for bandpass signals With a QAIC the wideband spectrum can be sampled at a substantially lower rate set by the information bandwidth rather than the much higher Nyquist rate set by the instantaneous bandwidth As a result innovative spectrum sensor RF ICs can be designed to simultaneously deliver a very short scan time a very wide span and a high frequency resolution while requiring only modest hardware and energy resources This is not possible with existing spectrum scanning solutions Our first QAIC RF IC demonstration scans a wideband 1GHz span with a 20MHz resolution bandwidth in 4 4μsecs offering 50x faster scan time compared to traditional sweeping spectrum scanners and 6 3x compressed aggregate sampling rate compared to traditional concurrent Nyquist rate approaches The unique QAIC bandpass architecture is 50x more energy efficient compared to traditional spectrum scanners and 10x more energy efficient compared to existing lowpass CS spectrum sensors Bio Rabia Tugce Yazicigil received the B S degree in electronics engineering from Sabanci University Istanbul Turkey in 2009 and the M S degree in electrical and electronics engineering from École Polytechnique Fédérale de Lausanne EPFL Lausanne Switzerland in 2011 She is currently a Ph D candidate in the electrical engineering department at Columbia University New York advised by Prof Peter Kinget and co advised by Prof John Wright Her interdisciplinary research work focuses on developing and implementing novel spectrum sensing

    Original URL path: https://risingstars15-eecs.mit.edu/rabia-tugce-yazicigil/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - Qi (Rose) Yu, University of Southern California. “Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning”
    road traffic and user checkins Complex spatial and temporal dependencies pose new challenges to largescale spatiotemporal data analysis Existing models usually assume simple interdependence and are computationally expensive In this work we propose a unified lowrank tensor learning framework for multivariate spatiotemporal analysis which can conveniently incorporate different properties in the data such as spatial clustering temporal periodicity and shared structure among variables We demonstrate how the framework can be applied to two central tasks in spatiotemporal analysis cokriging and forecasting We develop an efficient greedy algorithm to solve the resulting optimization problem with convergence guarantees Empirical evaluation shows that our method is not only significantly faster than existing methods but also more accurate Bio Qi Rose Yu is a fourth year Ph D candidate at the University of Southern California with a particular interest in Machine Learning and Data Mining Rose s research focuses on largescale spatiotemporal data analysis where she designs algorithms to perform predictive tasks in applications including climate informatics mobile intelligence and social media Her work is supported by USC Annenberg Graduate Fellowship She has interned in Microsoft R D Intel Lab Yahoo Labs and IBM Watson Research Center She was selected and funded as one

    Original URL path: https://risingstars15-eecs.mit.edu/qi-rose-yu/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - Zhou Yu, Carnegie Mellon University. “Engagement in Multimodal Interactive Conversational Systems”
    or react to their human interacts nonverbal behaviors or internal states such as the level of engagement This problem can also be found in other interactive systems Drawing knowledge from human human communication dynamics I use multimodal sensors and computational methods to understand and model user behaviors when interacting with a system that has conversational abilities e g spoken dialog systems virtual avatars humanoid robots By modeling the verbal and nonverbal behaviors such as smiles we infer high level psychological state of the user such as attention and engagement I focus on maintaining engaging conversations by modeling users engagement states in real time and making conversational systems adapt to their users via techniques such as adaptive conversational strategies and incremental speech production I apply my multimodal engagement model in both non task oriented social dialog framework and task oriented dialog framework that I designed I developed an end to end non task oriented multimodal virtual chatbot TickTock which serves as a framework for controlled multimodal conversation analysis TickTock can carry on free form everyday chatting conversations with users in both English and Chinese languages Together with ETS Speech and Dialog team I developed task oriented system HALEF which is also a distributed web based system HALEF has both visual and audio sensing capabilities for human behavior understanding Users can access the system via a web browser which in turn reduces the cost and effort in data collections HALEF can be easily adapted to different tasks We implemented an application so that the system acts as an interviewer to help users prepare for job interviews For demos please visit my webpage http www cs cmu edu zhouyu Bio Zhou is a fifth year Ph D student in the Language Technology Institute School of Computer Science Carnegie Mellon University where she works

    Original URL path: https://risingstars15-eecs.mit.edu/zhou-yu/ (2015-12-05)
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  • Rising Stars in EECS: 2015 - Audrey Resutek
    09 2013 Sara Alspaugh UC Berkeley Characterizing Data Exploration Behavior to Identify Opportunities for Automation Audrey Resutek 2015 10 19T03 35 42 00 00 1 09 2012 Elnaz Banan Sadeghian Georgia Institute of Technology Detector for Two Dimensional Magnetic Recording Audrey Resutek 2015 10 19T03 37 45 00 00 1 09 2011 Katherine Bouman MIT Visual Vibrometry Estimating Material Properties from Small Motions in Video Audrey Resutek 2015 10 19T03

    Original URL path: https://risingstars15-eecs.mit.edu/author/aresutek/ (2015-12-05)
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