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January

Thursday, 1/23 - Distributed Python

The workshop is designed for researchers seeking to optimize their workflows for large-scale data processing and parallel computing. Participants will explore the fundamentals of distributed computing with Python, learning how to leverage Dask for efficient task parallelism and Ray for scalable machine learning and reinforcement learning applications. Through hands-on exercises, attendees will gain practical experience in deploying these tools to handle massive datasets, optimize computational resources, and streamline workflows.
Fondren Library 110 | 3:30 PM - 5:00 PM

 

February

Thursday, 2/6 - Graph Machine Learning Fundamentals

The workshop is designed to equip researchers with the skills to apply graph neural networks (GNNs) to complex datasets. Participants will learn the fundamentals of graph-based data representations, followed by practical training on using PyTorch Geometric (PyG) for building, training, and evaluating GNN models.
Fondren Library 110 | 3:30 PM - 5:00 PM

Tuesday, 2/11 - Fundamentals of Deep Learning
Part 1 of 2

This workshop, designed by NVIDIA Deep Learning Institute (DLI), will cover foundational techniques for learning deep learning models, including CNNs, data augmentation, pretrained models, and natural language processing, with practical implementation in Python and PyTorch. The workshop objectives are to learn fundamental techniques and tools for training deep learning models, gain practical experience with common deep learning model architectures, and build confidence to tackle projects using modern deep learning frameworks.

Participants are required to have prior knowledge of Python and PyTorch. After successfully completing the assessment and attending both sessions, participants will receive an NVIDIA DLI certificate to validate their skills and enhance their career opportunities.
Fondren Library 109 | 9:00 AM - 1:00 PM

 

Tuesday, 2/18 - Fundamentals of Deep Learning
Part 2 of 2

This workshop, designed by NVIDIA Deep Learning Institute (DLI), will cover foundational techniques for learning deep learning models, including CNNs, data augmentation, pretrained models, and natural language processing, with practical implementation in Python and PyTorch. The workshop objectives are to learn fundamental techniques and tools for training deep learning models, gain practical experience with common deep learning model architectures, and build confidence to tackle projects using modern deep learning frameworks.

Participants are required to have prior knowledge of Python and PyTorch. After successfully completing the assessment and attending both sessions, participants will receive an NVIDIA DLI certificate to validate their skills and enhance their career opportunities.
Fondren Library 109 | 9:00 AM - 1:00 PM

 

March

Thursday, 3/10 - Privacy-Preserving Machine Learning

This workshop will introduce the basics of federated learning and implement a federated learning model using NVIDIA FLARE, an open-source framework. It includes hands-on demonstrations of image classification with federated learning on SuperPOD and explores potential research directions in federated learning, offering insights into future opportunities in the field.
Fondren Library 109 | 9:00 AM - 11:00 AM