{
	"event_id": "1124259",
	"eventinstance_id": "4151692",
	"calendar": {
		"id": 6812,
		"title": "UCF Research Cyberinfrastructure Events",
		"slug": "ucf-research-cyberinfrastructure-events",
		"url": "https://events.ucf.edu/calendar/6812/ucf-research-cyberinfrastructure-events/"
	},
	"id": "4151692",
	"title": "Research Computing (full\u002Dday) Advanced Bootcamp",
	"subtitle": null,
	"description": "\u003Cp\u003E\u003Cspan\u003EThe Office of Research Cyberinfrastructure is hosting a one\u002Dday \u003C/span\u003E\u003Cstrong\u003EResearch Computing Advanced Bootcamp\u003C/strong\u003E\u003Cspan\u003E\u003Cstrong\u003E\u0026nbsp\u003B\u003C/strong\u003Efor users interested in specialized topics in research computing such as strategies for leveraging tools for understanding system profiling, limitations of pandas for large DataFrames, other high\u002Dperformance tools for DataFrames,\u0026nbsp\u003B\u0026nbsp\u003Bquerying large language models via Python APIs, reproducibility practices, and automated plotting techniques. The workshop will include three sessions featuring hands\u002Don exercises, followed by an open discussion and Q\u0026amp\u003BA.\u003C/span\u003E\u003Cspan\u003E\u0026nbsp\u003B\u003C/span\u003E\u003Cspan\u003E\u003C/span\u003E\u003C/p\u003E\u000A\u003Cp\u003E\u003Cstrong\u003ESession 1: Handling Large DataFrames in Python\u003C/strong\u003E\u003Cbr\u003EThis session explores the performance and memory limitations of pandas when working with large\u002Dscale datasets.\u003Cbr\u003EIt presents modern alternatives such as Polars and covers efficient data handling techniques, including optimized storage formats, chunked processing, and extensions to distributed and GPU\u002Denabled frameworks.\u003C/p\u003E\u000A\u003Cp\u003E\u003Cstrong\u003ESession 2: Python and DataFrames for Sensible Experiment Management\u003C/strong\u003E\u003Cbr\u003EThis session focuses on developing structured and reproducible workflows for computational research.\u003Cbr\u003EParticipants will build a benchmarking framework for LLM inference while learning best practices in data aggregation, API integration, and automated visualization.\u003C/p\u003E\u000A\u003Cp\u003EFor further information on sessions and tentative agenda please visit:\u0026nbsp\u003B\u003Cbr\u003E\u003Ca href\u003D\u0022https://rci.research.ucf.edu/events/research\u002Dcomputing\u002Dfull\u002Dday\u002Dadvanced\u002Dbootcamp\u002Djune\u002D2026/\u0022 target\u003D\u0022_blank\u0022\u003Ehttps://rci.research.ucf.edu/events/research\u002Dcomputing\u002Dfull\u002Dday\u002Dadvanced\u002Dbootcamp\u002Djune\u002D2026/\u003C/a\u003E\u003C/p\u003E\u000A\u003Cp\u003E\u003Cem\u003E\u003Cstrong\u003EPlease note:\u003C/strong\u003E All the sessions have a hands\u002Don component. To participate in the hands\u002Don 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\u0027s description for specific instruction.\u0026nbsp\u003B\u003C/em\u003E\u003C/p\u003E",
	"location": "Partnership 3 Building: 233",
	"location_url": "https://goo.gl/maps/YpbWXxXX3HA81B9p9",
	"virtual_url": null,
	"registration_link": "https://ucf.qualtrics.com/jfe/form/SV_0vnNiJGBJ9wISr4",
	"registration_info": null,
	"starts": "Tue, 23 Jun 2026 08:30:00 -0400",
	"ends": "Tue, 23 Jun 2026 16:30:00 -0400",
	"ongoing": "False",
	"category": "Workshop/Conference",
	"tags": ["llm","gpu programming","Python","research computing"],
	"contact_name": "Research IT Events",
	"contact_phone": null,
	"contact_email": "ResearchITEvents@ucf.edu",
	"url": "https://events.ucf.edu/event/4151692/research-computing-full-day-advanced-bootcamp/"
}