Past Events

University of Florida Hackathon 2022 - GPU Hackathon

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University of Florida (UF) Hackathon is a multi-day intensive hands-on event designed to help computational scientists and researchers port and optimize their applications using GPUs. It pairs participants with dedicated mentors experienced in GPU programming and development in AI, HPC, and data science applications.

Spring 2022 HiPerGator Symposium

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UF Information Technology (UFIT) Research Computing will host the Spring 2022 HiPerGator Symposium on March 24 from 9:00 a.m. - 1:00 p.m.

Spring 2022 HiPerGator Training

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The UFIT Research Computing Spring Training Schedule is below. HiPerGator training sessions will be held on most Thursdays from 10:40 to noon. This semester, we plan to offer both in-person (in the UFII Seminar Room, CISE E252) and Zoom options. Please register by 9:00am the day of the training to receive the Zoom link

Practicum AI Beginner Workshops

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If you have an interest in artificial intelligence (AI) but do not know where to start, the Practicum AI (Beginner) workshop series is for you! This series comprises five modules – What is AI, AI Ethics, Reproducible AI, Practicum Python, and Deep Learning Foundations.

Practicum AI Intermediate Workshops

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Building on the Practicum AI (Beginner) workshops, our Practicum AI (Intermediate) series provides a broad overview of the different kinds of deep learning systems in use today. The learning experiences in this series will cover the basics of Convolutional Neural Networks (CNNs), Natural Language Processing (NLP), Recurrent Neural Networks (RNNs), Transformers, and Transfer Learning.

MONAI Label for Medical Imaging

MONAI Label is an open-source medical-imaging-specific tool for both AI-assisted annotation and building your own AI annotation models. This tutorial will have two parts: a) in-depth MONAI Label introduction; b) step-by-step demos on HiperGator.

Nvidia DLI Workshop: Applications of AI for Anomaly Detection

Data integrity is critical to research and business. AI models can be trained and deployed to automatically analyze datasets, define “normal behavior,” and identify breaches in patterns quickly and effectively. These models can then be used to predict future anomalies. With massive amounts of data available online and across research domains and subtle distinctions between normal and abnormal patterns, it’s critical to use AI to quickly detect anomalies that pose a threat.