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Bio

Candice Chambers is a passionate Ph.D. student in Computer Science, specializing in the integration of Model-Based Systems Engineering (MBSE) with Natural Language Processing (NLP), Large Language Models (LLMs), Functional Reasoning, and Cognitive Architectures. Her research explores how LLMs and NLP can optimize complex system design and enhance the functional reasoning capabilities of intelligent systems.

With a B.S. in Applied Mathematics and a minor in Computer Science, Candice brings a strong analytical foundation to her work. Her transition into computer science at the doctoral level reflects her dedication to bridging theory and application, driving innovations that improve the efficiency and intelligence of modern engineering systems.

She has contributed to the field through publications such as:

• "Towards Knowledge Extraction and Parsing of XML Metadata for SysML System Architecture Modeling", presented at the 2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (IEEE Link).
• "Functional Reasoning of System Architecture in the System Modeling Language (SysML) With XML Representation", presented at the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (ASME Link).

Currently, she is actively researching and working on LLMs, MBSE, NLP, Functional Reasoning, and Cognitive Architectures, focusing on how these technologies can be leveraged to advance system modeling methodologies, automation, and intelligent decision-making in engineering and design.