Maria Sorokina, PhD is a data and computational biology professional working at the intersection of life sciences, semantics, and large-scale connected data systems. She currently works at AstraZeneca, where she focuses on building and evolving AI-ready data foundations, including knowledge graphs, ontologies, and semantic frameworks that support integration, reuse, and advanced analytics across complex scientific domains. Her work addresses core challenges in pharmaceutical R&D and operations, particularly the transformation of heterogeneous biological, chemical, and clinical data into connected, computable knowledge that can be reliably used by both humans and machine-learning systems. Maria holds a PhD in computational biology from Genoscope (CEA, France), where her research focused on metabolic networks and bioinformatics. Her background spans academic research and industry roles in France and Germany, including work in systems biology, natural products cheminformatics, and biomedical data science. She has contributed to peer-reviewed publications and open scientific resources used by the global research community. At Future Labs Live Basel 2026, Maria will share perspectives from large-scale pharmaceutical data platforms, discussing how semantic technologies, knowledge graphs, and connected data approaches are being applied in practice to support AI and data-driven operations in a global pharmaceutical environment. Maria's expertise, combined with her passion for driving digital transformation and connectivity in the pharmaceutical sector, makes her a valuable asset in shaping the future of healthcare.
Enterprise AI is failing at scale: despite massive investment, only a small fraction of organizations generate real value. The core problem is context. While generative AI can reason fluently, it lacks the semantic grounding required to operate reliably in complex enterprise environments. Knowledge graphs are often treated as databases rather than executable context, leading to brittle systems and confident hallucinations.
In pharma operations, where regulatory, supply chain, and safety constraints are non-negotiable, semantic precision is essential. AstraZeneca’s Operations Knowledge Fabric shifts from data lakes to semantic-first data products, living ontologies, and graph-native infrastructure. By embedding meaning directly into data, we enable faster AI deployment, higher-quality decisions, and a foundation for autonomous, domain-aware systems.
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