Author: Laura Altin

Workshop on Uncertainty in Machine Learning (February 2-4 2026 in Tartu, Estonia) Motivation and Focus The notion of uncertainty is of major importance in machine learning and constitutes a key element of modern machine learning methodology. In recent years, it has gained in importance due to the increasing relevance of machine learning for practical applications,…

Primary focus area: AI for e-governance Secondary focus areas: adaptation of foundation models Abstract This project aims to develop AI tools that support self-regulated learning (SRL) in schools, aligning with how the human brain learns. These tools will be grounded in cognitive science to ensure reliability, trust, and real-world applicability. Small-scale classroom experiments will validate…

Primary Focus Area: AI for CybersecuritySecondary Focus Areas: Hybrid AI pipelines, Adaptation of foundation models, Safeguards and trust in AI, Privacy and security in AI, AI for e-governance, AI for healthcare, AI for business processes AbstractCyber threats are growing in scale and complexity, producing massive volumes of security data that challenge timely analysis. This RBO…

Primary focus area: AI for business processesSecondary focus areas: safeguards and trust in AI; AI for e-governance Abstract This project develops methods for real-time, adaptive optimization of business processes using structured and unstructured data. By detecting performance degradations, diagnosing their causes, and recommending data-driven interventions, the approach shifts from static process redesign to dynamic, operational-level…

Primary focus area – F5: AI for e-governanceSecondary focus areas – F1: hybrid AI pipelines, F2: adaptation of foundation models, F6: AI for healthcare, F8: AI for cybersecurity Abstract This project develops AI-based methods for generating a realistic, privacy-preserving synthetic digital twin of the Estonian population. Initial efforts focus on synthesizing data from the population…

Primary Focus Area: Privacy and Security in AISecondary Focus Areas: Adaptation of Foundation Models; AI for Cybersecurity Abstract:This RBO aims to prototype secure, cloud-based AI workflows using privacy-preserving computation methods like secure multi-party computation (MPC), federated learning, and trusted execution environments (TEEs). These technologies can enable sensitive data to be used in training and inference…

Primary focus area: Hybrid AI pipelinesSecondary focus areas: AI for healthcare Abstract:This RBO aims to create a hybrid AI dialogue system that integrates large language models (LLMs) with domain-specific guidance to produce structured yet flexible interactions. Our prototype will target mental health self-help by supporting techniques like cognitive reframing and problem-solving. The system will dynamically…

Primary focus area: Safeguards and trust in AISecondary focus areas: Adaptation of foundation models Abstract:Seq2seq models used in translation and speech recognition often produce errors like repetition or irrelevance. These are difficult to manage due to a lack of reliable uncertainty estimation. This RBO aims to distinguish and quantify two types of uncertainty: content uncertainty…