My interests focus on applied research where domain knowledge combined with data can provide solutions that mainstream big data and machine learning techniques cannot address. By combining state-of-the-art natural language processing and machine learning technologies with symbolic logic-based knowledge representation and reasoning techniques, my research takes advantage of robust data-driven learning, prediction, and explanation methods, complemented by knowledge-based approaches to capturing the necessary domain knowledge required to interpret and validate the outcomes of data analysis in different contexts. Applications of my research include data platforms for information extraction from structured and unstructured sources, summarization of causal relationships among events described in natural language text, context-specific data fusion and decision-making under uncertainty, generation of simulation models from specifications in natural language, and configuration, diagnosis, and optimization of technical systems and associated business processes and software systems. The outcomes of my research have contributed to overcoming data silos and providing intelligent tools for Asset Management, Health Care, Oil & Gas and steel industries, Waste Management, Sustainable Development, Transportation, the Government sector.