Kurt Niemi
Machine Learning Engineer
Alpharetta, Georgia, United States
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With over two decades of expertise in software development, Kurt serves as a Senior Member of the Technical Staff at VMWare. A lifelong learner, he is on the cusp of obtaining his Master's Degree in Machine Learning from Georgia Tech, complementing his Bachelor's in Computer Science from Michigan State University.
A fusion of passion and profession, Kurt delves into the world of LLMs and Generative AI, exploring their myriad applications both in his professional sphere and during his leisure hours. Notably, his innovative spirit led him to invent and secure US Patent 11,645,507 B2, titled "Providing Models to Client Devices".
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Harnessing LLMs for Educators: Crafting Standards-aligned Lesson Plans & Exercises
Delve into the transformative potential of Large Language Models (LLMs) for educators. With the sophistication of LLMs such as OpenAI's GPT-series, educators now have a powerful ally in curriculum development. This session is tailored for educators eager to understand and apply LLM capabilities in designing lesson plans and exercises that align with educational standards, ensuring both innovation and compliance in instruction.
The session will also address the importance of having an expert (educator) in the loop, to review the output of LLMs.
We will also discuss how educators could collaborate to share created lesson plans and securely share feedback that could be incorporated to further reduce any corrections to LLM output
Fine-Tuning LLMs for Public Health: Generating insights from unstructured data
Generative AI, particularly Large Language Models (LLMs), hold the promise to revolutionize various sectors, including public health. One notable application is in harnessing the power of these models to sift through and derive insights from vast amounts of unstructured data.
In this session, the presenter will share firsthand experience from participating and using LLMs in the CDC-hosted competition, "Unsupervised Wisdom: Explore Medical Narratives on Older Adult Falls."
Join us as we delve into:
How to Fine-Tune LLMs: Unpack the methodologies and techniques essential for tailoring generic LLMs to specialized tasks, and why customization is vital for specific applications like public health.
Data Acquisition with RLHF (Reinforcement Learning Human Feedback): Dive into the mechanisms of RLHF, understanding its role in training LLMs, its advantages over traditional methods, and how it ensures data quality and relevance.
Designed for a diverse audience – from civilian and defense government agency representatives to public health experts, academia, and AI practitioners – this session offers a concrete example of how cutting-edge AI can intersect with critical public sector missions.
Kurt Niemi
Machine Learning Engineer
Alpharetta, Georgia, United States
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