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An Interdepartmental Research Center
Committed to Advancing the Analytical Information and Decision Sciences.

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Upcoming LIDS Events

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  • July
    11

    LIDS Thesis Defense

    Truncated Bayesian Nonparametrics

    33-206

    1:00pm

LIDS Research Highlights

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Camera Control for Learning Nonlinear Target Dynamics via Bayesian Non-Parametric Dirichlet-Process Gaussian-Process (DPGP) Models

Mobile sensors are often deployed to cooperatively perform surveillance of an environment and track objects of interest: for example, intrusion detection throughout a secure facility, and anomaly detection in manufacturing plants. In many such cases it is infeasible to provide accurate sensor coverage of the entire environment, and thus an environment model and associated controller that accounts for sensor field-of-view (FoV) geometry is necessary to determine sensor configurations that minimize uncertainty.

Modeling and Mitigating Cascading Failures in Interdependent Power Grids and Communication Networks

Many of today’s critical infrastructures are organized in the form of a network, which are dependent on one another. A particular example is the power grid and the communication network used to control the grid. While this dependence is beneficial during normal operation, as it allows for more efficient operation, it can be harmful when the networks are under stress. Indeed, in such interdependent network infrastructures, a cascade of failures may occur where power failures can lead to communication failures, which, in turn, lead to cascading power failures.

Reinforcement Learning with Multi-Fidelity Simulators

Simulators play a key role as testbeds for robotics control algorithms, but deciding when to use them versus collecting real-world data is often treated as an art. We have developed a framework for efficient reinforcement learning (RL) in a scenario where multiple simulators or a target task, each with varying levels of fidelity, are available. Our framework is designed to limit the number of samples used in each successively higher-fidelity/cost simulator by allowing a learning agent to choose to run trajectories at the lowest level simulator that will still provide it with information.

LIDS News

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Photo of Munther Dahleh

June 28, 2016

Munther Dahleh Selected IFAC Fellow

April 20, 2016

David Forney: The Man Who Launched a Million Modems

April 20, 2016

Self-Driving Cars, Meet Rubber Duckies

April 11, 2016

Using New Models and Big Data to Better Understand Financial Risk

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Laboratory for Information and Decision Systems

Massachusetts Institute of Technology
77 Massachusetts Avenue
Room 32-D608
Cambridge, MA 02139

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  • About LIDS
    • History
    • LIDS Advisory Committee
    • Directions to LIDS Offices
  • Research
    • Systems, Networks, and Control
    • Communications, transmission of information, and Networks
    • Inference & Statistical Data Processing
    • Research Highlights
    • Research Archive
  • Labs and Groups
    • Aerospace Controls Laboratory (ACL)
    • Aerospace Robotics and Embedded Systems Group (ARES)
    • Communications and Networking Research Group (CNRG)
    • Inference and Stochastic Networks Group (ISNG)
    • Stochastic Systems Group (SSG)
    • Wireless Communication and Network Sciences Laboratory (WGroup)
  • People
    • Administrative Staff
    • Faculty/PIs
    • Research Staff
    • Students
    • Research Affiliates
  • News & Events
    • LIDS News
    • Event Calendar
    • LIDS Seminar Series
    • Conferences and Workshops
  • Resources
    • Information for Visitors and New Members
    • Book a Conference Room
    • Copy/Fax/Print/Scan
    • LIDS Publications
    • Request Repairs
    • Ship & Receive Mail
    • Travel Rules and Reimbursements

Contact Us:
617-253-2142

Laboratory for Information
and Systems Decisions

Massachusetts Institute of Technology
77 Massachusetts Avenue
Room 32-D608
Cambridge, MA 02136