• Integrates systems engineering and analysis models (SAI).• Leads research in interoperability between modeling languages and specialized engineering disciplines.• Specializes in engineering information language formalization, focusing on system behavior and assembly, and implicit knowledge capture.• Uses ontology-based language development, such as OWL and PSL, for automated consistency checking of rules.• Developed an ontology modeling architecture as the basis for the Systems Modeling Language (SysML).• SysML is the most widely-used graphical language for systems engineering, currently being prepared for the Object Management Group (OMG).Artificial intelligence (AI) has the potential to transform society and people’s lives across various sectors, from commerce and healthcare to transportation and cybersecurity. While AI can drive inclusive economic growth and scientific advancements, it also poses significant risks that can negatively impact individuals, groups, organizations, communities, society, the environment, and the planet.AI Risk Management Framework (AI RMF) defines an AI system as an engineered or machine-based system that can generate outputs like predictions, recommendations, or decisions influencing real or virtual environments. AI systems operate with varying levels of autonomy.While there are standards and best practices to mitigate risks in traditional software systems, AI risks are unique. For instance, AI systems may be trained on data that changes over time, affecting system functionality and trustworthiness. AI systems and their contexts are often complex, making it difficult to detect and respond to failures. They are inherently socio-technical, influenced by societal dynamics and human behavior. AI risks and benefits arise from the interplay of technical aspects and societal factors related to system use, interactions with other AI systems, operation, and deployment context.AI poses unique challenges for deployment and utilization, amplifying, perpetuating, or exacerbating inequitable outcomes without proper controls. However, with controls, AI systems can mitigate these outcomes. AI risk management is crucial for responsible AI development and use. Responsible AI practices align AI system design, development, and uses with intended aims and values, emphasizing human centricity, social responsibility, and sustainability. AI risk management prompts critical thinking about context and potential impacts, enhancing trustworthiness and public trust.