Current projects include: 

• Mobility Modeling: We work on the development of a data-driven mobility model, using statistical likelihoods for path selection and speed of movement. Our focus is on the development of a framework to support any mobility traces and for ease of evaulation of various communication protocols, deployments, etc under such models.
• Energy Efficient Wireless Protocols: We work on the development of wireless protocols which consider energy metrics for lifetime extension in sensor netowrks.
• Mobile Computing & Applications: We work on the development of socially influenced mobile phone applications for developing countries. Challenges include working with limited and out-dated hardware, limited infrastructure and cost models in deployed areas, and facilitating current computation models to the platform. Another focus is on the development of peer-to-peer mobile and social information networks.
• Architectures for Emerging Interconnect Technologies: With the emergance of new on-chip interconnect technologies, CAD is facing a drastic shift in optimization priorities, such as power, area, and bandwidth. We are developing new architectures and optimization techniques for emerging technologies, such as RF, optical, and/or plasmonics.

Members of the Lab are engaged in three major projects: 

• FLORA - a declarative object-oriented language for programming knowledge intensive applications.
• The LMC (Logic Programming-Based Model Checking)
• XSB - A high-performance logic programming and deductive database system.

Specific Projects: 
Network localization: We develop efficient localization algorithm in which sensors collaborate with each other to locate themselves and self-organize into a connected network.
Topology discovery: We work on distributed algorithms that discover and maintain high-order topological features (e.g., holes not covered by sensors) or the geometric shape of the sensor field.
Mobility: We work on heterogeneous sensor networks that integrate mobile nodes (robots, people holding cell phones) with static monitoring sensor nodes and foster novel applications.

Our research covers a range of topics in natural language processing. A current focus is using Deep Learning techniques to build concise representations of the meanings of words in all significant languages, and use these powerful features to recognize entities and measure sentiment and other properties of texts. Another focus involves analyzing Wikipedia to identify the fame and significance of historical figures as reported in our book Who's Bigger? and associated website. Our Lydia technology has been licensed by General Sentiment, a social media analysis startup.

Theoretical and experimental analysis of string, graph, and combinatorial algorithms, including 

• applications in computational biology and combinatorial computing,
• randomized algorithms, with applications to scheduling, and
• computational geometry and approximation algorithms, particularly with applications to computer graphics and manufacturing.