Tag: Research Computing Training

UFIT Awarded Contract with USDA

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UFIT was recently awarded a multi-year contract to collaborate with the USDA’s Agricultural Research Service (ARS) to deliver AI training. UFIT Research Computing staff will offer sessions using established AI Practicum training modules as as develop new modules in consult with ARS staff. This contract will enhance existing Practicum AI content and allow UFIT to more rapidly build online content for the UF community and beyond. The new modules developed with the USDA will also be available to UF faculty, staff and students, with particular benefit to the IFAS community.

Part of the contract’s funding will be used to hire a new AI training staff member, who will develop the new training modules, deliver in-person sessions, and produce online content for UF’s Professional Workforce Development site.

Eight new, four-hour, beginner and intermediate courses are planned. As well, and in consult with ARS and IFAS, some advanced AI topics are under consideration including areal image analysis, agriculture-specific computer vision, genomics, and agriculture-specific reinforcement learning for robotics.

Production of online content is underway. The in-person content already available to the UF community will be offered to ARS staff starting in Spring 2023. Anyone with questions about UFIT’s contract with the USDA is welcome to email Training and Biocomputing Specialist Dr. Matt Gitzendanner.

Medical Imaging for AI Research Inquiries

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UFIT is hosting two MONAI-focused tutorials in July. Both tutorials will be held via Zoom:

Tutorial Name: MONAI Core
Date: Tuesday, July 12, 12:00–1:00 p.m.
Registration Link
Description: MONAI Core is a PyTorch based and GPU-accelerated deep learning framework, specifically designed for medical imaging. This tutorial will cover:
• Why MONAI Core: the unique and impactful features of MONAI Core
• MONAI Core on HiPerGator: end-to-end demo on HiPerGator

Tutorial Name: MONAI Label for Medical Imaging with NVIDIA
Date: Tuesday, July 26, 12:00–1:00 p.m.
Registration Link
Description: MONAI Label is an open-source medical-imaging-specific tool for both AI-assisted annotation and building your own AI annotation models. This tutorial has two parts:
• An in-depth MONAI Label introduction
• A step-by-step demo on HiPerGator

MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. MONAI provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm. Anyone with questions about the MONAI tutorials or other training opportunities offered by UFIT Research Computing may contact Dr. Matt Gitzendanner.