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UID:pretalx-scipy-2026-BMPMUR@pretalx.com
DTSTART;TZID=CST:20260713T080000
DTEND;TZID=CST:20260713T120000
DESCRIPTION:Through the construction of a Deep Research Agent\, tutorial pa
 rticipants will learn the fundamental building blocks of LLM-driven applic
 ations. Starting with in-context learning and prompt design\, we will prog
 ress through memory management\, tool integration via the Model Context Pr
 otocol (MCP)\, and planning workflows. Participants will build a working a
 gent that can query a Zotero citation library\, synthesize literature summ
 aries\, and engage in multi-turn research conversations. We will also disc
 uss failure modes\, limitations\, and the role of such agents in an age of
  coding assistants.
DTSTAMP:20260622T100915Z
LOCATION:AI/ML
SUMMARY:Building A Deep Research Agent - Benjamin Batorsky\, Eric Ma
URL:https://pretalx.com/scipy-2026/talk/BMPMUR/
END:VEVENT
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UID:pretalx-scipy-2026-VNQPKP@pretalx.com
DTSTART;TZID=CST:20260714T080000
DTEND;TZID=CST:20260714T120000
DESCRIPTION:Through the use of NetworkX's API\, tutorial participants will 
 learn about the basics of graph theory and its use in applied network scie
 nce. Starting with a computationally-oriented definition of a graph and it
 s associated methods\, we will progress through the following concepts: pa
 th and structure finding\, visualization\, and graph storage on disk. We w
 ill also offer tutorial participants the option of one advanced topic over
 view\, including the use of graphs alongside LLMs for knowledge retrieval\
 , scalable alternatives to NetworkX including cuGraph\, and the use of lin
 ear algebraic translation of graph problems to speed up computations.
DTSTAMP:20260622T100915Z
LOCATION:Other
SUMMARY:Network Analysis Made Simple - Eric Ma
URL:https://pretalx.com/scipy-2026/talk/VNQPKP/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-scipy-2026-YWHVF7@pretalx.com
DTSTART;TZID=CST:20260717T104500
DTEND;TZID=CST:20260717T111500
DESCRIPTION:Canvas Chat is a browser-based tool that combines Python's data
  science stack with large language model connectivity\, enabling natural l
 anguage interaction with data. Built on Pyodide\, it runs entirely in the 
 browser with no server-side computation required. Users bring their own AP
 I keys for LLM access\, while all session data persists locally in Indexed
 DB. The visual\, non-linear interface represents conversations as nodes on
  an infinite canvas\, supporting branching\, merging\, and stateful explor
 ation of data analysis workflows. This talk demonstrates how browser-based
  Python plus LLMs can democratize data science by removing infrastructure 
 barriers while preserving privacy and reproducibility.
DTSTAMP:20260622T100915Z
LOCATION:Memorial Hall
SUMMARY:Canvas Chat - non-linear workflows for AI-assisted data science - E
 ric Ma
URL:https://pretalx.com/scipy-2026/talk/YWHVF7/
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