~~Abstract~~
During system design and development, ensuring that the right and optimal architecture/design decisions are made is a significant challenge. Often, the learning of whether the decision is optimal or not, and the impact on the Measures of Effectiveness (MOEs) of the system, occur late in the development lifecycle. System architects and designers undergo various experiential learnings during the development of many systems over the years. In this talk, we will discuss a framework that leverages machine learning models to learn from the decision learning cycles and advise on the uncertainty of various decisions. The framework enables the codification of decisions and progressive maturity of the architectural knowledge base.

~~Speaker~~
Dr. Ramakrishnan Raman, ESEP.
Principal Systems Engineer – Honeywell. Assistant Director – INCOSE Asia Oceania
Dr. Ramakrishnan Raman (https://www.linkedin.com/in/ramakrishnanraman) has B.Tech and MS (by Research) degrees from IIT Madras and Ph.D. from IIIT Bangalore. He is a certified Six Sigma Black Belt and INCOSE certified ESEP (Expert Systems Engineering Professional). He has extensive systems and software engineering experience in domains of Building/Industrial Automation and Aerospace. His areas of interest include system architecture design, software architecture design, lean product development, and machine learning as applied to complex engineering systems.

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