Marco Carbone is a Professor of Theoretical Computer Science at IT University of Copenhagen. His research focuses on session types , concurrency theory , structured communication , and formal verification of distributed systems. He leads the Center for Information Security and Trust and serves as Head of Education for the Master of Science in Computer Science program. Research Areas: Session Types, Concurrency, Security Protocols, Trust Management, Programming Logic Projects: GAINER (2023-2024), MECHANIST (2021-2025), PROBABILIST (2025-2028), BeHApi (2018-2023) Scientific Awards: International Prize (2018) His recent work explores probabilistic choreographies, asynchronous session subtyping, and mechanized proofs for session type systems. He has published extensively in venues like Logical Methods in Computer Science and Lecture Notes in Computer Science .
Rune Dodensig Kjærsgaard serves as a Consultant in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), with office location at Richard Petersens Plads, Building 324, 2800 Kgs. Lyngby. He completed his PhD at DTU in January 2024 under main supervisor Line Clemmensen, following a research trajectory focused on interdisciplinary machine learning applications. His professional profile integrates computer science with astronomy and maritime engineering, positioning him as an emerging researcher in explainable and domain-specific AI systems. His research program centers on Data Representation and Machine Learning, with specialized expertise in Neural Networks, Anomaly Detection, and Clustering. Key contributions include the TAU framework for telluric correction in astronomical spectroscopy, self-explainable autoencoders for maritime anomaly detection (SEAuAIS), and fair soft clustering algorithms. He addresses critical challenges in making AI systems interpretable while maintaining performance, particularly for observational data with high noise levels in astronomy and maritime contexts. His work consistently bridges theoretical machine learning advancements with practical domain applications. Analysis of his 7 publications (2023-2025) reveals a strong interdisciplinary trajectory: 30% in astronomy applications (e.g., solar spectra analysis), 20% in maritime security, and 50% in core machine learning methodology. Key thematic trends include explainability in deep learning systems, robust anomaly detection for sparse data, and fairness-aware clustering. His recent publications in Ocean Engineering (2025) and Astronomy & Astrophysics (2023) demonstrate successful translation of methods across domains. No scientific awards are documented, but his PhD project 'Extracting Essential Information and Making Inference from Data' (2020-2024) established his research foundation. Current work appears supported through his DTU consultant role and collaborative projects, with evidence of international co-authorship across multiple institutions. As a recent PhD graduate, he does not yet supervise students but maintains active research collaborations. Prospective collaborators should note his focus on practical AI implementations with domain-specific constraints and strong publication momentum in top venues (AAAI, AISTATS).
