archive-edu.com » EDU » M » MIT.EDU

Total: 105

Choose link from "Titles, links and description words view":

Or switch to "Titles and links view".
  • Rising Stars in EECS: 2015 - Silvio Micali
    his PhD in Computer Science from the University of California at Berkeley Since 1983 he has been on the MIT faculty in Electrical Engineering and Computer Science Department where he is Ford Professor of Engineering Since January 2015 he is the Associate Head of the Department of Electrical Engineering and Computer Science Silvio s research interests are cryptography zero knowledge pseudo random generation secure protocols and mechanism design Silvio is

    Original URL path: https://risingstars15-eecs.mit.edu/silvio-micali/ (2015-12-05)
    Open archived version from archive


  • Rising Stars in EECS: 2015 - Henny Admoni, Yale University. “Nonverbal Communication in Human-Robot Interaction”
    communication in order to develop social robots that interact with people in natural effective ways Application areas include social robots that help elderly users with tasks like preparing meals or getting dressed manufacturing robots that act as intelligent third hands improving efficiency and safety for workers and robot tutors that provide students with personalized lessons to augment their classroom time Nonverbal communication such as gesture and eye gaze is an integral part of typical human communication Nonverbal communication happens bidirectionally in an interaction so social robots must be able to both recognize and generate nonverbal behaviors These behaviors are extremely dependent on context with different types of behaviors accomplishing different communicative goals like directing attention or managing conversational turn taking To be effective in the real world nonverbal behaviors must occur in real time in dynamic unstructured interactions My research focuses on developing bidirectional context aware real time nonverbal behaviors for personally assistive robots Developing effective nonverbal communication for robots engages a number of disciplines including autonomous control machine learning computer vision design and cognitive psychology My approach to this research is three fold First I conduct well controlled human robot interaction studies to understand people s perceptions of robots Second I build computational models of nonverbal behavior using data from human human interactions Third I develop robot agnostic behavior controllers for collaborative human robot interactions based on my models of human behavior and test these behavior controllers in real world human robot interactions Bio Henny Admoni is a PhD candidate at the Social Robotics Laboratory in the Department of Computer Science at Yale University where she works with Professor Brian Scassellati This winter Henny will begin as a Postdoctoral Fellow at the Robotics Institute at Carnegie Mellon University working with Siddhartha Srinivasa Henny creates and studies intelligent autonomous robots

    Original URL path: https://risingstars15-eecs.mit.edu/henny-admoni/ (2015-12-05)
    Open archived version from archive

  • Rising Stars in EECS: 2015 - Ilge Akkaya, UC Berkeley. “Compositional actor-oriented learning and optimization for swarm applications”
    to mitigate the heterogeneity within Internet of Things applications by presenting an actor oriented framework which enables developing compositional learning and optimization applications that operate on streaming data Ptolemy Learning Inference and Optimization Toolkit PILOT achieves this by presenting a library of reusable interfaces to machine learning control and optimization tasks for distributed systems A key goal of PILOT is to enable system engineers who are not experts in statistics and machine learning to use the toolkit in order to develop applications that rely on on line estimation and inference In this context we provide domain specific specializations of general learning and control techniques including parameter estimation and decoding on Bayesian networks model predictive control and state estimation Recent and ongoing applications of the framework include cooperative robot control real time audio event detection and constrained reactive machine improvisation A second branch of my research aims at maintaining separation of concerns in model based design In industrial cyber physical systems composition of sensors middleware computation and communication fabrics yields a highly complex and heterogeneous design flow Separation of concerns becomes a crucial quality in model based design of such systems We introduce the aspect oriented modeling AOM paradigm which addresses

    Original URL path: https://risingstars15-eecs.mit.edu/ilge-akkaya/ (2015-12-05)
    Open archived version from archive

  • Rising Stars in EECS: 2015 - Sara Alspaugh, UC Berkeley. “Characterizing Data Exploration Behavior to Identify Opportunities for Automation”
    a GUI in order to obtain different views of the data It is not always clear which views will be effective for a given dataset or question how to be systematic about which views to examine or how to map a high level question into a series of low level actions to answer it This results in unnecessary repetition disrupted mental flow ad hoc and hard to repeat workflows and inconsistent exploratory coverage Identifying useful repeatable exploration workflows opportunities for automation of tedious tasks and intelligent interfaces better suited for expressing exploratory questions all require a better understanding of data exploration behavior We seek this through three means We analyze interaction records logged from data analysis tools to identify behavioral patterns and assess the utility of log data for building intelligent assistance and recommendation algorithms that learn from user behavior Preliminary results reveal that while logs can say which functions are used in which contexts more comprehensive instrumentation and collection is likely needed to train intelligent exploration assistants We interview experts about their data exploration habits and frustrations to identify good exploratory workflows and ascertain important features not provided by existing tools Preliminary results reveal opportunities to make data exploration more thorough and efficient We design and evaluate a prototype for obtaining quick data overviews to assess new interface elements designed to better match data exploration needs Preliminary results suggest that small simple automation in existing tools would decrease user effort increase exploratory coverage and help users identify erroneous assumptions more readily Bio Sara Alspaugh is a computer scientist and PhD candidate at the UC Berkeley In her research she mines user interaction records logged from data analysis tools to better characterize data exploration behavior identify challenges and opportunities for automation and improve system and interface design She also conducts

    Original URL path: https://risingstars15-eecs.mit.edu/sara-alspaugh/ (2015-12-05)
    Open archived version from archive

  • Rising Stars in EECS: 2015 - Elnaz Banan Sadeghian, Georgia Institute of Technology. “Detector for Two-Dimensional Magnetic Recording”
    dimensional magnetic recording TDMR I work toward realization of this technology specifically to design a detector which can recover the data from extremely dense hard drives This is a challenge in part because this novel technology shrinks the widths of the data tracks to such an extent that an attempt to read data from one track will inevitably lead to interference from neighboring tracks and in part because of the challenging nature of the magnetic medium itself The combination of interference between different tracks and along adjacent bits on each track is a key challenge for TDMR and motivates the development of two dimensional signal processing strategies of manageable complexity to mitigate this two dimensional interference To address this issue we have designed a novel detection strategy for TDMR recording channel with multiple read heads Our method suppresses the intertrack interference and thereby reduces the detection problem to a traditional one dimensional problem so that we may leverage existing one dimensional iterative detection strategies Simulation results show that our proposed detector is able to reliably recover five tracks from an array of five read heads at an acceptable signal to noise ratio Further we are working on a detector which

    Original URL path: https://risingstars15-eecs.mit.edu/elnaz-banan-sadeghian/ (2015-12-05)
    Open archived version from archive

  • Rising Stars in EECS: 2015 - Katherine Bouman, MIT. “Visual Vibrometry: Estimating Material Properties from Small Motions in Video”
    Estimating Material Properties from Small Motions in Video The estimation of material properties is important for scene understanding with many applications in vision robotics and structural engineering We have connected fundamentals of vibration mechanics with computer vision techniques in order to infer material properties from small often imperceptible motion in video Objects tend to vibrate in a set of preferred modes The shapes and frequencies of these modes depend on the structure and material properties of an object Focusing on the case where geometry is known or fixed we have shown how information about an object s modes of vibration can be extracted from video and used to make inferences about that object s material properties We demonstrate our approach by estimating material properties for a variety of rods and fabrics by passively observing their motion in high speed and regular framerate video Bio Katherine Bouman received a B S E in Electrical Engineering from University of Michigan Ann Arbor MI in 2011 respectively and an S M degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology MIT Cambridge MA in 2013 She is currently a Ph D candidate in the Computer Vision group at MIT

    Original URL path: https://risingstars15-eecs.mit.edu/905-2/ (2015-12-05)
    Open archived version from archive

  • Rising Stars in EECS: 2015 - Carrie Cai, MIT. “Wait-Learning: Leveraging Wait Time for Education”
    learning leveraging wait time for education Despite the struggle to find time for learning there are numerous times in a day that are wasted due to brief moments of waiting such as waiting for the elevator waiting for wifi to connect or waiting for an instant message reply Combining wait time with productive work opens up a new class of software systems that overcomes the problem of limited time while addressing the frustration often associated with waiting My goal is to understand how to detect and manage these waiting moments and to discover essential design principles for wait learning systems I have designed and built several systems that enable wait learning WaitChatter delivers second language vocabulary exercises while users wait for instant message replies and FlashSuite integrates learning across diverse kinds of waiting including elevators wifi and email loading Through developing and evaluating these systems we identify waiting moments to use for learning and ways to encourage learning unobtrusively while maximizing engagement A study of WaitChatter with 20 participants found that wait learning can be an effective and engaging way to learn During two weeks of casual instant messaging participants learned and retained an average of 57 Spanish and French

    Original URL path: https://risingstars15-eecs.mit.edu/carrie-jun-cai/ (2015-12-05)
    Open archived version from archive

  • Rising Stars in EECS: 2015 - Precious Cantú, École Polytechnique Fédérale de Lausanne (EPFL). “Patterning via Optical Saturable Transitions”
    to approximately half the wavelength This so called far field diffraction limit or the Abbe limit after Prof Ernst Abbe who first recognized this effectively prevents the use of long wavelength photons 300nm from patterning nanostructures Bio Dr Precious Cantú is a Postdoctoral Researcher in the Materials Science and Engineering Department at École Polytechnique Fédérale de Lausanne EPFL where she works with Professor Francesco Stellacci in the Supramolecular Nanomaterials and

    Original URL path: https://risingstars15-eecs.mit.edu/precious-cantu/ (2015-12-05)
    Open archived version from archive



  •