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DTSTAMP:20250925T121425Z
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DTSTART;TZID=America/New_York:20250923T190000
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DESCRIPTION:Large language models (LLMs) have shown remarkable potential in
  various domains\, particularly in code optimization and generation for ma
 chine programming. However\, their performance in low-resource scenarios\,
  such as parallel code generation\, remains limited. In this talk\, I will
  discuss our recent advancements in enhancing LLMs' capabilities for these
  challenges through agent-based approaches\, fine-tuning techniques\, and 
 strategic data curation. Additionally\, I will introduce our work on repre
 sentation learning for code data\, focusing on tasks like compiler optimiz
 ation. I will also demonstrate the use of machine learning methods for eff
 icient workload partitioning in system optimization\, which can also facil
 itate scalable distributed training and inference of large-scale machine l
 earning models. Finally\, I will share insights into future research direc
 tions in these areas.\n\nSpeaker(s): Guixiang \n\nVirtual: https://events.
 vtools.ieee.org/m/473029
LOCATION:Virtual: https://events.vtools.ieee.org/m/473029
ORGANIZER:ieee.lvs.wie@gmail.com
SEQUENCE:10
SUMMARY:Women in AI Series 2025 - Empowering LLMs for Scalable and Efficien
 t Machine Programming: Guixiang Ma
URL;VALUE=URI:https://events.vtools.ieee.org/m/473029
X-ALT-DESC:Description: <br /><p>Large language models (LLMs) have shown re
 markable potential in various domains\, particularly in code optimization 
 and generation for machine programming. However\, their performance in low
 -resource scenarios\, such as parallel code generation\, remains limited. 
 In this talk\, I will discuss our recent advancements in enhancing LLMs' c
 apabilities for these challenges through agent-based approaches\, fine-tun
 ing techniques\, and strategic data curation. Additionally\, I will introd
 uce our work on representation learning for code data\, focusing on tasks 
 like compiler optimization. I will also demonstrate the use of machine lea
 rning methods for efficient workload partitioning in system optimization\,
  which can also facilitate scalable distributed training and inference of 
 large-scale machine learning models. Finally\, I will share insights into 
 future research directions in these areas.</p>
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