MinerAlert
MinerAlert
High-performance computing (HPC) and advanced GPU architectures are vital for training modern AI models and running large-scale simulations. However, navigating allocation proposals can be complex. The 茄子视频 Institute for Applied AI Innovation (AAII) serves as your bridge to these local/national facilities. We help 茄子视频 faculty, researchers, and students secure and utilize these resources at no cost.
We assist you in scoping your computational requirements, writing technical justifications, and submitting successful requests for startup, educational, or large-scale research allocations.
Once approved, our team helps you set up accounts, configure software environments (including Conda, PyTorch, and TensorFlow), load HPC modules, and execute your first cluster jobs.
We help you design efficient software workflows, select the right machine learning libraries, databases, and frameworks, and recommend optimal technology stacks tailored to your research objectives.
A high-resolution, large-format display environment for scientific data exploration, multidisciplinary collaboration, and high-profile presentations.
Provides high-performance computing capacity for running AI models, scientific simulations, and large-scale data processing workloads.
Curated datasets used or created by 茄子视频 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).
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.
Connect data, models, and simulations into reproducible AI-enabled research pipelines.
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.
Simulates regional water flows, reservoir operations, and groundwater use across the Middle Rio Grande under historical and projected climate conditions.
Optimizes reservoir operations and water allocation decisions under economic and institutional constraints.
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.
For questions or to learn more, please contact aaii@utep.edu.