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

Upcoming LIDS Events

September

28

LIDS & Stats Tea

Neural Auto-associative Memory Vi...

LIDS Lounge

4:30pm
October

3

Other LIDS Events

A Permutation-based Model for Cro...

32-D677

4:00pm
October

4

LIDS Seminar Series

LIDS Seminar - Frank Kschischang

32-141

4:00pm

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.

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.

Distributed Asynchronous Methods for Minimizing Sums of Non-Separable Functions

Recently, there has been a flurry of research activity for study of non-separable network optimization prob- lems. The problem formulation is very general and captures many important problems, such as parameter estimation in sensor networks and pattern recognition in machine learning settings. Due to the versatility of the problem formulation, fast distributed algorithms for this problem can provide drastic improvement in the system wide performance for many applications. The main challenge is to develop a distributed algorithm, which is necessary in many large scale applications.