Isak Samsten

Isak Samsten is a Senior Lecturer at Stockholm University's Department of Computer and Systems Sciences (DSV), specializing in data science and machine learning. He leads research in temporal machine learning, counterfactual explanations, and interdisciplinary applications in healthcare and environmental science. His work includes developing the wildboar Python module for time series analysis. Current research projects focus on AI for insurance fraud detection and environmental remediation. Samsten is affiliated with the Data Science Research Group, which bridges algorithmic innovation with practical decision-making. He holds an ORCID identifier (0000-0002-3056-6801) and is active in publishing influential papers on topics like time series classification, ESG performance prediction, and clinical decision support systems.

• Education: Unspecified in text (assumed doctoral degree given academic rank)
• Affiliations: DSV, Stockholm University; Data Science Research Group

Research Interests: Time series analysis, interpretable machine learning, healthcare informatics, environmental sustainability metrics, and AI ethics. Key contributions include shapelet-based classification methods (e.g., Castor algorithm) and counterfactual explanation frameworks (e.g., Glacier system).

Grants & Awards: None explicitly listed in provided text.

Labs/Teams: Leads the Data Science Research Group, collaborating on projects like AI to detect unclear insurance claims and Toxicity guided inverse design of materials.