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Cyberinfrastructure

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Pioneering real-world AI solutions, building talent, and driving regional transformation with global impact.

Visualization Wall

A high-resolution, large-format display environment for scientific data exploration, multidisciplinary collaboration, and high-profile presentations.

High-Performance Computing (HPC)

Provides high-performance computing capacity for running AI models, scientific simulations, and large-scale data processing workloads.

Coming soon

Curated datasets used or created by UTEP researchers will be made available to affiliated users, as appropriate. These resources are presented as AAII cyberinfrastructure to support research and collaboration, not as research productivity metrics (e.g., funding amounts or publication counts).

Training and support in the use of the following tools and platforms:

PyTorch

Deep learning framework for building and training neural networks

TensorFlow

End-to-end open-source platform for machine learning

FLOSIC

Software for self-interaction correction in density functional theory

Docker

Containerization platform for deploying and managing applications

Podman

Daemonless container engine for developing, managing, and running containers

SLURM

Workload manager for high-performance computing environments

Anaconda

Free data science platform for Python and R

NVIDIA NGC

AI-driven platform and marketplace for GPU-accelerated software

Multimodal open-source models

Models that integrate multiple sources of information (e.g., images, text, audio) to more effectively address machine-learning tasks, including the fusion of data from multiple sensors and cross-modal analysis.


Category: Vision-Language encoder


Category: Vision → Text / Instruction tuning


Category: Multimodal LLM (image+text)


Category: Text → Image


Category: Speech → Text


Category: Vision-Language encoder


Category: Language understanding, reasoning, and text generation

Scientific Workflow

Connect data, models, and simulations into reproducible AI-enabled research pipelines.

SWIM (Scientific Workflow Integration & Management)

A platform that connects scientific models with the data they need, providing cloud hosting and an easy-to-use interface for integration, execution and interpretation of water models.

Active Models in SWIM

Middle Rio Grande Water Balance Model

Simulates regional water flows, reservoir operations, and groundwater use across the Middle Rio Grande under historical and projected climate conditions.

Integration type: Data-to-Model

Middle Rio Grande Hydroeconomic Optimization Model

Optimizes reservoir operations and water allocation decisions under economic and institutional constraints.

Integration type: Data-to-Model

Automating Multivariable Workflow Composition for Model-to-Model Integration

Model-to-Model integration automatically connects two scientific models by using the outputs of one as inputs to another. This enables seamless multi-model workflows for more complete and realistic scenario analysis.

Integration type: Model-to-Model

For questions or to learn more, please contact aaii@utep.edu.