Speaker 1:

The rapid pace of technological advancement and the complexity of real-world challenges demand that computational intelligence (CI) research quickly move from innovation to application. Traditional project management methods often fall short in supporting the iterative nature of CI development. This presentation explores how Agile methodologies can bridge this gap by enabling faster, more adaptive application of academic research to real-world projects. Agile's emphasis on iterative development, prototyping, and testing allows teams to run experiments and refine solutions based on real-time feedback. It fosters cross-disciplinary collaboration between data scientists, domain experts, and stakeholders, ensuring that CI systems are co-created efficiently. Real-world examples demonstrate how Agile accelerates the implementation of neural networks, fuzzy logic, and evolutionary algorithms across domains like adaptive learning, decision-making, and optimization. Core Agile practices—such as sprint planning, daily stand-ups, and incremental delivery—support continuous validation and refinement while promoting ethical, explainable applications in critical sectors like healthcare, finance, and smart infrastructure. Ultimately, Agile transforms CI development from a static process to a dynamic, evolving approach that enhances both impact and resilience.

Speaker 2:

How computational intelligence techniques—such as machine learning, neural networks, and optimization algorithms—are being practically applied to enhance go-to-market strategies and optimize revenue operations at scale.