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

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LIDS Research Highlights

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.

Network Localization and Navigation

Reliable and accurate localization of mobile nodes is a key enabler for numerous emerging applications in the commercial, public safety, and military sectors. Achieving such location-awareness by conventional techniques is challenging in harsh environments with limited infrastructures. This work proposes a new paradigm called network localization and navigation (NLN), in which mobile nodes exploit spatiotemporal network cooperation for positional inference.

Approximate Decentralized Bayesian Inference

Recent trends in the growth of datasets, and the methods by which they are collected, have led to increasing interest in the parallelization of machine learning algorithms. Parallelization results in reductions in both the memory usage and computation time of learning, and allows data to be collected and processed by a network of learning agents rather than by a single central agent.