Lab Home | Phone | Search
Center for Nonlinear Studies  Center for Nonlinear Studies
 Colloquia Archive 
 Postdoc Seminars Archive 
 Quantum Lunch 
 CMS Colloquia 
 Q-Mat Seminars 
 Q-Mat Seminars Archive 
 Kac Lectures 
 Dist. Quant. Lecture 
 Ulam Scholar 
 Summer Research 
 Student Application 
 Past Visitors 
 PD Travel Request 
 History of CNLS 
 Maps, Directions 
 CNLS Office 

Correlations and Dynamics in Information Science (2011-2013)

Information, its processing, transport and vulnerability are matters of national security. The understanding and management of information systems in computer and communication networks, biological systems and social dynamics play a vital role in multiple national security challenges. With this proposal, the Center for Nonlinear Studies (CNLS) targets fundamental science challenges that are central to long-term strategies for improving information management. The science efforts which focus on understanding the effects of ‘correlations and dynamics’ in information systems will elucidate the effects of random influences (stochasticity), the consequences and prospects of coordinating the action of pairs or higher numbers of units or ‘nodes’, the dynamics of timing and coordinating signals and the accuracy of network models. In conjunction with the science efforts, the CNLS-center will coordinate scientific collaborations, explore new avenues, host two weekly seminar series in the area of Information Science and Technology (IS&T), organize high-profile conferences, provide parallel computing resources of novel architecture, and provide science-based management of postdoc, student and visitor programs. The high-profile interface with the worldwide science community that has been a hallmark of CNLS activities will be an asset in growing the basic science of IS&T and making it real world relevant.

Focus Areas

  • Sensing and processing of information, bio-informatics and bio-inspired methods of speeding searches and fast processing of information.
  • Modeling and analysis of complex systems, applied mathematics analysis of tractable models, methods to develop real-world models, description of epidemics, and the development of fast, robust methods for real-time cyber security analysis.
  • Inference and learning with stochastic information, statistical mechanics techniques to understand information science questions in the large systems limit.