Institute for Data, Intelligent Systems, and Computation

Research

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Catastrophe Modeling
CatModeling is a rigorous probabilistic approach to the study of natural disasters, and their consequences. Recent trends have seen CatModeling applied beyond natural disasters, to rare events like financial crises, political unrest, and pandemics.
Computer Vision
Developing novel computationally efficient computer vision algorithms for visual understanding, forensic analysis, real/fake image/video detection, and AI-assisted healthcare
Data-Driven Inference & Control of Dynamic Systems
New initiative - focus on problems in the smart electric grid where much of the data infrastructure is in place & evaluation of developed methodologies.
FinTech and BlockChain
Use of machine learning for automated comparison & consistency and regulatory compliance & enforcement.
Human-Centered Computing
NEW
Machine Learning in Materials Science
Use of machine learning & deep learning for modeling complex physical systems of materials & chemical processes.
Mathematical Optimization and Data Science
Research on mathematical optimization (MO), which involves the development of mathematical and computational tools that facilitate discovery, design, and decision-making throughout science, engineering, and business.
Probabilistic Modeling
Developing innovative approaches in probabilistic analysis & transferring methodologies from one field to another.
Quantum Computing & Optimization Lab (QCOL)
NEW GROUP - Quantum Computing has the potential to solve problems faster than classical supercomputers.
Robotics
Developing theoretical & computational foundations to achieve long-term robot autonomy.
Scalable Systems & Software
Developing software and hardware to maximize performance and reduce energy consumption in multi-core and distributed systems.