Workshops, seminars, and training events for researchers

Upcoming Events

  • 2024 UCF Research Computing Symposium

    Digital Learning Center - Room 136

    The UCF Research Computing Symposium will be held on October 1, 2024, at the UCF Digital Learning Center and is organized and sponsored by the Office of Research. The aim is to bring together researchers in UCF who leverage advanced computing resources to accelerate their research; resources such as UCF’s Stokes and Newton from ARCC, national super-computing facilities from agencies …

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  • Introduction to PyTorch: A quickstart workshop in running ML jobs on ACCESS

    R1:101: Research 1, Room 101

    In this workshop, participants will leverage national compute resources within the NSF-funded ACCESS ecosystem to run simple machine learning experiments and familiarize themselves with Pytorch. Designed as a concise introduction to the use of Pytorch, the presenter will share their experience using Pytorch for research. Topics we will cover include environment setup, various machine learning pipelines, loading pretrained models, and …

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  • Unlocking the Power of GenAI on AWS for Research Productivity

    Virtual

    This workshop will teach you how to leverage Amazon Bedrock, a platform for developing and deploying large language models, to create powerful GenAI applications tailored to specific domain knowledge. You will learn to build personalized assistants that can summarize complex topics, answer questions, provide recommendations, and more. By harnessing the power of large language models and Research Augmented Generation (RAG), …

    UCF Research Cyberinfrastructure Events
  • Prompt Engineering 101: Shaping AI Conversations

    Virtual

    In this hands-on workshop, participants will dive into the world of prompt engineering—a crucial skill for effectively interacting with AI models like ChatGPT. Tailored for beginners to intermediates, this session will explore the art and science of crafting prompts that yield accurate, creative, and useful outputs. By the end of the workshop, attendees will have a solid understanding of prompt …

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  • Scientific Computing using R

    Virtual

    The goal of this lesson is to teach new programmers to write modular code to perform a data analysis. R is used to teach these skills because it is a commonly used programming language in many scientific disciplines. However, the emphasis is not on teaching every aspect of R, but instead on language agnostic principles like automation with loops and …

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  • Foundations of Data Management: Finding Data Sources

    Virtual

    Data and statistics play an important role in conducting research, yet understanding how to find, analyze, and manage data can be complicated. If you are interested in developing data skills, this session on finding data sources will provide an overview of introductory information to aid you on your path to being a data expert.The workshop will introduce the basic concepts …

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  • Parallel Computing with MATLAB: Part 2 of 2

    Virtual

    The first part of this workshop focuses on parallel computing with MATLAB on the desktop. The host introduces the concept of parallel computing and briefly covers optimizing code before diving into parallelism in MATLAB and common programming constructs such as parfor (including optimization and performance considerations), and parfeval. Next, scaling options are introduced with discussion about GPUs and clusters/clouds and …

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  • Probabilistic programming languages in Python

    Virtual

    In this workshop, we will discuss probabilistic programming in Python. We'll first go over the basics of and motivations behind probabilistic programming. After this brief review, we will learn how to use Python probabilistic programming libraries to build example statistical models and inference pipelines. Pre-requisites: Attendees should be familiar with Python and Bayesian data analysis. Presented by David Wright.

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  • Good Enough Practices in Scientific Computing

    Virtual

    This workshop is targeted at a broad audience of researchers who want to learn how to be more efficient and effective in their data analysis and computing, whatever their career stage.The lesson is inspired by and based on the paper, Good Enough Practices in Scientific Computing (Wilson et al., 2017): “a set of good computing practices that every researcher can …

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  • Methods for reproducible computational research

    Virtual

    Published computational research should be reproducible today, tomorrow, and ten years from now. In this workshop, we will go over tools and practices you can employ to ensure that this is the case. Topics will include containerization, bit-for-bit reproduction of software environments, and software design practices for reproducible pipelines. Presented by David Wright.

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  • Transform Research & Learning with Google Cloud

    Virtual

    Discover How Google Generative AI Can: Accelerate Discovery: Uncover patterns and correlations in your data that would take years to find manually. Scale Innovation: Generate novel hypotheses, design innovative experiments, and explore new research avenues. Enhance Collaboration: Streamline literature reviews, co-author papers, and communicate findings more effectively. Secure Funding: Craft compelling grant proposals and showcase the impact of your research …

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