The next-generation battlefield will be populated with a vast number of interconnected, heterogeneous and sometimes autonomous agents including devices, networks, software, and humans. Defending such complex and/or autonomous systems will be impossible for humans to do alone, making our research key in defending such system.
In response to the ongoing and future challenges facing the Cyber and Information Domain, our research directly supports the Army and DoD enterprise in the research, development, experimentation, testing, evaluation and operationalization of computationally intelligent, assured (secure, resilient, robust, safe, trusted), and distributed decision-support systems for autonomous cyber operations in highly-contested, complex battlefield environments. To build, assess and deploy smart, autonomous cyber-systems that enable intelligent, assured and federated decision-making, our research explores the science of information, computation, learning, and fusion for adaptive, collaborative pattern discovery, reasoning, perception, action and decision-making given heterogenous, complex, disparate data spanning devices, networks, software, and humans.
Our research aims to develop models and tools for collective intelligence, likely augmented by interacting with human cyber analysts and decision-makers. In conducting basic and applied research in the areas of data science, operations research, artificial intelligence, cognitive science, computing and advanced analytics, our research seeks to tackle a multitude of challenges in infrastructure and architecture engineering, individual and collective decision-making, stealth and resilience, as well as society. Specifically, our research aims to provide new capabilities to:
- Shift emphasis from sensing to information awareness
- Understand the underpinning of autonomy
- Relieve human cognitive overload in dealing with the data deluge problem
- Enhance human-machine interface in information processing
- Cope with various complex disparate data/information types
- Integrate a diversity of unique reasoning and learning components collaborating simultaneously
- Bridge correlational with causal discovery
- Determine solutions or obstructions to local-to-global data fusion problems
- Mechanize reasoning/learning and computing in the same computational environment
- Yield provably efficient procedures to enable or facilitate advanced data analytics
- Deal with high-dimensional and massive datasets with provably guaranteed performance