The Office of Research Cyberinfrastructure promotes workshops, events, and training for researchers at University of Central Florida (UCF). Subscribe to updates to receive email notifications for upcoming events and new opportunities. View past events in our events archive.
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Upcoming Events
The Office of Research Cyberinfrastructure is hosting a one-day Research Computing for Beginners Bootcamp designed to introduce participants to essential tools, including Git, Python, R, and Data Visualization techniques. This bootcamp requires no prior experience and is structured to provide a beginner-friendly learning environment.
The event will have four sessions:
Session 1: Git version control for Beginners
This session introduces the fundamentals of Git and command-line version control for managing code and collaborative projects.
Session 2: A Gentle Introduction to Python for Research Computing
This beginner-friendly session covers core Python concepts for research workflows, including data handling, basic analysis, and scripting.
Session 3: Introduction to Programming in R
This session provides a practical introduction to R for data preprocessing and analysis.
Session 4: Data Visualization using Python
This session focuses on analyzing and visualizing data using Python libraries such as Matplotlib, Seaborn, and Plotly.
For further information on sessions and tentative agenda please visit:
https://rci.research.ucf.edu/events/research-computing-full-day-bootcamp-for-beginners-june-2026/
Please note: All the sessions have a hands-on component. To participate in the hands-on exercises during the session, you will need to bring your own laptop equipped with a web browser as well as install any software specific to that lesson. Refer to each lesson's description for specific instruction.
The Office of Research Cyberinfrastructure is hosting a one-day Research Computing Advanced Bootcamp for users interested in specialized topics in research computing such as strategies for leveraging multi-GPU architectures in parallel workflows, GPU profiling, limitations of pandas for large DataFrames, other high-performance tools for DataFrames, querying large language models via Python APIs, reproducibility practices, and automated plotting techniques. The workshop will include three sessions featuring hands-on exercises, followed by an open discussion and Q&A.
Session 1: Distributed GPU Architecture for LLMs
This session introduces GPU computing fundamentals and memory considerations in machine learning workflows.
It also examines multi-GPU strategies—including model parallelism, Distributed Data Parallel (DDP), and Fully Sharded Data Parallel (FSDP)—through practical examples and hands-on exercises.
Session 2: Handling Large DataFrames in Python
This session explores the performance and memory limitations of pandas when working with large-scale datasets.
It presents modern alternatives such as Polars and covers efficient data handling techniques, including optimized storage formats, chunked processing, and extensions to distributed and GPU-enabled frameworks.
Session 3: Python and DataFrames for Sensible Experiment Management
This session focuses on developing structured and reproducible workflows for computational research.
Participants will build a benchmarking framework for LLM inference while learning best practices in data aggregation, API integration, and automated visualization.
For further information on sessions and tentative agenda please visit:
https://rci.research.ucf.edu/events/research-computing-full-day-advanced-bootcamp-june-2026/
Please note: All the sessions have a hands-on component. To participate in the hands-on exercises during the session, you will need to bring your own laptop equipped with a web browser as well as install any software specific to that lesson. Refer to each lesson's description for specific instruction.