NISS AI, Statistics & Data Science in Practice: Frank Wei Talk on Large Language Models: Transforming AI Architectures and Operational Paradigms
Date: Tuesday, February 18, 2025 - 3:00pm to 4:00pm ET
Full details available on event page here: NISS AI, Statistics & Data Science in Practice: Frank Wei Talk on Large Language Models: Transforming AI Architectures and Operational Paradigms | National Institute of Statistical Sciences
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Join us for an engaging talk by Frank Wei, AI/ML Leaderand Practitioner at General Motors. The presentation, "Large Language Models: Transforming AI Architectures and Operational Paradigms," will explore the evolution of LLMs, their groundbreaking architecture, and their transformative impact on machine learning paradigms. Gain insights into cutting-edge advancements in AI, including deployment strategies, operational frameworks, and a demonstration of an innovative LLM-based solution addressing real-world challenges.
Speaker
Frank Wei, AI/ML Leader and Practitioner at General Motors
Moderator
Nancy McMillan, Data Science Research Leader, Health Research & Analytics Business Line at Battelle
Talk Title: Large Language Models: Transforming AI Architectures and Operational Paradigms
Abstract: The emergence of Large Language Models (LLMs) represents a paradigm shift in artificial intelligence, fundamentally transforming our approach to natural language processing and machine learning architectures. In this presentation, we will navigate through the evolutionary trajectory of LLMs, beginning with their historical foundations and theoretical underpinnings that have shaped the current landscape of AI. We will then delve into the architectural intricacies of transformer-based models, examining their self-attention mechanisms, positional encodings, and multi-head architectures that enable unprecedented language understanding and generation capabilities. As we explore the transformative impact of LLMs on traditional machine learning paradigms, we will analyze the evolution from conventional ML to LLM, highlighting the specialized operational frameworks, deployment strategies, and infrastructure requirements that distinguish these approaches. This transition encompasses novel considerations in computational orchestration, model versioning, prompt engineering, and systematic evaluation methodologies. We will critically examine how these operational paradigms are reshaping feature engineering, model architectures, and deployment pipelines in AI systems. To demonstrate these theoretical and operational principles in practice, we will conclude with a demonstration of our innovative LLM-based solution, illustrating how sophisticated architectural designs and robust operational frameworks converge to address complex real-world challenges.
Event Disclaimer
The views and opinions expressed by the speakers during this event are their own and do not necessarily reflect the views, positions, or policies of their employers, affiliated organizations, or any other entity. The speakers are participating in a personal capacity, and their statements should not be attributed to their respective companies or institutions.
About the Speaker
Frank Wei, who holds a Ph.D. from George Mason University, has spent the past decade solving complex business challenges across agricultural, healthcare, and automotive industries through innovative AI solutions. As a technical leader, Frank excels in architecting machine learning systems and guiding projects from proof-of-concept to production. His expertise spans setting technical direction for AI initiatives, designing scalable system architectures, and building high-performing teams. Throughout his career, Frank has demonstrated particular strength in resource allocation and mentorship, helping data scientists and developers reach their full potential while delivering impactful business solutions. His work combines deep technical knowledge with practical implementation, having successfully developed and deployed AI solutions ranging from predictive analytics, computer vision to natural language processing systems. Frank's contributions to the field are reflected in his multiple publications and patents, showcasing his commitment to advancing AI technology while solving real-world industry problems.
About the Moderator
Nancy McMillan currently serves as Data Science Research Leader within Battelle's Health Research & Analytics Business Line. For a diverse set of federal government clients, she currently leads development of a large language model (LLM) based biocuration acceleration pipeline and user tool, development of pipelines, analytics, and visualizations of electronic initial case reporting data, and development of analytical methods for achieving abbreviated new drug application (ANDA) approval for an agile drug manufacturing technology. Nancy has a long history of collaborative work across Battelle bringing statistics and machine learning to Battelle's deep capability in biology, chemistry, and material science. As a researcher and Project Management Professional, Nancy has worked and published on environmental exposure and risk assessment; transportation safety benefits; quantitative risk assessment related to chemical, biological, radiological and nuclear (CBRN) terrorism; bio surveillance; and bioinformatics. She managed the Health Analytics Division from 2017-2023, a team of approximately 100 data scientists that supports Battelle's contract research business. Nancy is a member of the Board of Trustees for the National Institute of Statistical Sciences (NISS), the Chair of NISS's Affiliates Committee, and a member of the Organ Procurement and Transplantation Network's Data Advisory Committee.
The NISS AI, Statistics and Data Science in Practice is a monthly event series will bring together leading experts from industry and academia to discuss the latest advances and practical applications in AI, data science, and statistics. Each session will feature a keynote presentation on cutting-edge topics, where attendees can engage with speakers on the challenges and opportunities in applying these technologies in real-world scenarios. This series is intended for professionals, researchers, and students interested in the intersection of AI, data science, and statistics, offering insights into how these fields are shaping various industries. The series is designed to provide participants with exposure to and understanding of how modern data analytic methods are being applied in real-world scenarios across various industries, offering both theoretical insights, practical examples, and discussion of issues.
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Randy Freret
National Institute of Statistical Sciences
rfreret@niss.org------------------------------