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DTSTART:20251026T030000
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SUMMARY:Rust for High Performance Computing (HPC) in Python - Cheuk Ting H
 o
DTSTART;TZID=Poland:20260720T160000
DTEND;TZID=Poland:20260720T163000
DTSTAMP:20260911T201636Z
UID:pretalx-euroscipy-2026-CQHPYG@pretalx.com
DESCRIPTION:Python has become the most widely used language in scientific 
 computing and data science due to its approachable syntax\, vast ecosystem
  of libraries\, and rapid prototyping capabilities. However\, its interpre
 ted nature often poses a performance bottleneck for computationally intens
 ive tasks common in High Performance Computing (HPC) used in scientific an
 d data work\, such as large-scale simulations\, complex data analysis\, an
 d machine learning model training. \n\nEnhancing Python's performance\, th
 erefore\, is critical for scientific computing: it allows researchers and 
 engineers to maintain the productivity and flexibility of the Python envir
 onment while achieving the necessary speed and scalability to tackle deman
 ding\, real-world HPC problems without needing to switch to lower-level la
 nguages entirely.
LOCATION:Room 1.19 (Ground Floor\, Shannon)
URL:https://pretalx.com/euroscipy-2026/talk/CQHPYG/
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SUMMARY:Do you know how well your model is doing? Evaluate your LLMs - Che
 uk Ting Ho
DTSTART;TZID=Poland:20260722T110000
DTEND;TZID=Poland:20260722T123000
DTSTAMP:20260911T201636Z
UID:pretalx-euroscipy-2026-MYVDAK@pretalx.com
DESCRIPTION:Large Language Models (LLMs) are becoming central to modern ap
 plications\, yet effectively evaluating their performance remains a signif
 icant challenge. How do you objectively compare different models\, benchma
 rk the impact of fine-tuning\, or ensure your LLM responses adhere to safe
 ty guidelines (guard-railing)? This hands-on workshop addresses these crit
 ical questions.
LOCATION:Room 1.38 (Ground Floor\, Turing)
URL:https://pretalx.com/euroscipy-2026/talk/MYVDAK/
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