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The goal of this project is to construct a statistical morphing continuum theory to model the complex and non-equilibrium physics of microstructured eddies in compressible turbulence.

The goal is to use mobile phone data to analyze mobile behaviors in order to simulate the dispersion of influenza in populations.

The goal of the BEAMS Initiative is to engage the AI community in fostering the development of responsible and ethical tools to support simulation modeling methods.

The IOTN is designed to accelerate the generation of novel mechanism-driven immune-based cancer prevention and therapies, and the development of safe and effective personalized immuno-oncology approaches.

Data informatics methods can aid scientists in discovering and rapidly identifying pathways for materials discovery. Integrating statistical and geometric methods together with computational simulations, these methods can guide experimental strategies, providing robust interpretations of results, and guiding further experimentation.

Bayesian experimental design is a tool for guiding experiments founded on the principle of expected information gain. I.e., which experiment design will inform the most about the model can be predicted before experiments in a laboratory are conducted.

This project proposes to develop an operational system and tools for monitoring the vegetation state of a fire-prone shrubland ecosystem.

The ultimate goal of this project is to understand the fundamentals of triboelectrification through a first-principles investigation, both experimentally and theoretically.

The goal in this project is to explore new ways to design learning and inference methods that are robust to distributional uncertainty and data corruption.

The objective of this project is to understand how people describe locations on social media during natural disasters.

The project engages high school students in high-need and high-potential school districts with cutting-edge web GIS knowledge and skills in order to encourage them to pursue STEM careers.

We are developing new methodologies and theories, and collaboriting with domain experts to address real-world applications.

Our overall research program deals with timely societal challenges in energy, water, and environment. A point of emphasis is the efficient and cost-effective conversion of solar energy to electrical and chemical energies.

This project will develop an algorithm to assist foster care caseworkers in identifying youth in need of services before crises occur, and to allocate those services in a fair and just way.

This proposal leverages the data-driven model design, high-performance computing, and three-dimensional (3D) virtual reality to construct unprecedented high-resolution 3D models of the Pacific Rim of Fire. The 3D numerical simulations of the Pacific Rim of Fire will investigate a new class of volcanoes that occur at the edges of subduction zones, rather than typical arc volcanoes located above the subducted plate. In addition, the numerical simulations will examine the dimensions of slab-driven mantle flow, addressing outstanding questions of coupling between the tectonic plates and the Earth’s mantle.

This project will develop new efficient methods to help scientists and engineers, especially in manufacturing and robotics, to simplify complex data such that dynamic processes underlying the data can be better represented, understood and controlled.

This project will use GIS and individual-level data to understand the relationship between spatial, social, and cultural factors in accounting for individuals’ linguistic knowledge.

This project aims to improve the trapping of materials to prevent greenhouse gases from being released before a vehicle’s catalytic converter activates during start-up.

Responding to an impending hazard means that time is limited, so analysis and decision-making must proceed on an accelerated timetable. Modeling, numerical simulation, leading to predictive capacity, play an important role during this pre-event timeframe. This project will seek to combine different data sources, to make quantifiable statistical predictions that can be used by civil protection authorities to inform decision-making.