Research
Research Themes
The Center for Computational Intelligence focuses on the following areas of research. Teams of research experts have been compiled to collaborate with numerous avenues to assist industries in these areas. Expand the below accordions to learn more.
- Personalized Learning: Developing AI tools for personalized learning, tutoring, and advising while considering ethical issues, potential biases, and privacy concerns.
- Educational Data Analytics: Leveraging large educational datasets to predict and identify patterns that can inform decision-making to improve student and institution outcomes.
- Public Health Predictive Modeling: Develop statistical models, and machine learning algorithms to predict disease outbreaks and public health trends and patterns.
- Health(care) Data Integration: Analyzing diverse health data sources, including electronic health records, clinical trial events, wearable devices, and health surveys and available public data. (community-level vulnerability index, etc.)
- Human Performance Optimization: Use AI and machine learning to analyze warfighter/human performance/athlete data, injury prediction, recovery, training, and performance optimization.
- Data Surveillance System: Analysis of systematic and ongoing health related data collection for effective planning, implementation and evaluation of health practices.
- Health Monitoring: Using AI tools and techniques for monitoring and caring for the elderly population.
- Movement Science: Building models to understand how and why organisms move and/or respond to stimuli.
- Intrusion Detection Systems: Develop AI tools for intrusion detection.
- Monitoring Systems: Developing machine learning and AI tools for the monitoring of electrical systems, mechanical systems, health structures such as bridges and buildings, environmental sensors, pollution, and climate.
- Smart Grid and Energy Management: This area uses machine learning and AI to provide systems that can predict outages, load, demand, and energy consumption.
- Systems Biology: This area covers mathematical models and simulations to study complex biological systems such as neuron connections. It also covers the statistical analysis of biological data to predict properties and discover associations.
- Bioinformatics Software and Tools: Developing computational tools, algorithms, and databases for various aspects of biological data analysis.
- ChemInformatics: Applying informatics methods to solve chemical problems.
Computational Biology, Chemistry and Biomedical Sciences Research Experts
- Embodied Intelligence: Development of robotic systems that can intelligently interact with the environment around them to effect change. This gives a physical body to AI algorithms, where this embodiment is believed to be a required part of creating intelligence necessary to perform tasks alongside humans.
- Personalized Software Agents: Demonstrating the viability for collaborative, ethical AI development to facilitate decision making in community-based emergency response situations such as medical, disaster response, law enforcement and policy domains.
- Human-Machine Teamwork: Enabling people and machines to work effectively on physical and cognitive work to improve productivity, work quality, safety, and quality of life for the people in the workforce.
- Mathematical Foundations: Developing algorithms for probability, statistics, signal processing, and information theory.
- Computational Modeling: Translating physical, biological, or economic models into code for simulation and analysis.
- Machine Learning and AI: Creating data-driven approaches to solve complex, high-dimensional problems where traditional models fail.
- Numerical Analysis: Solving linear systems, eigenvalue problems, and differential equations, especially for simulation-driven data generation.
- Applications: Research is actively applied to fields like bioinformatics, GIS and environment modeling, health analytics, and financial risk assessment.