MIT LIDS | Laboratory for Information & Decision Systems

Upcoming LIDS Events

September

19

LIDS & Stats Tea

Automated As-Is Modelling of Indu...

LIDS Lounge

3:00pm
September

20

Other LIDS Events

Topics in Information and Inferen...

32-D677

4:00pm
September

21

Other LIDS Events

Information Revolution – Happines...

LIDS Lounge

3:00pm

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.

Focused Active Inference

Probabilistic graphical models compactly represent the structure of stochastic processes, and exploiting the sparsity of that structure often leads to computationally efficient inference algorithms, which process realized observations to update a belief about latent states that are not observable.

The benefits of partial resource pooling in resource allocation

It is well known that resource pooling (or, equivalently, the use of flexible resources that can serve multiple types of requests) significantly improves the performance of service systems. On the other hand, complete resource pooling often results in higher infrastructure (communication and coordination) costs. This leads us to explore the benefits that can be derived by a limited amount of resource pooling, and the question whether a limited amount of pooled resources can deliver most of the benefits of complete resource pooling.