Probabilistic Inference Professors and Supervisors in Denmark

Martin BøgstedView profile

Professor

Aalborg UniversityDenmark

Martin Bøgsted is a Professor at Aalborg University, serving as Center Director at Clinda (Center for Clinical Data Science) within the Clinical Institute of the Faculty of Health Sciences. With over 30 years of experience spanning academia, public sector, and industry, he leads significant research initiatives in clinical data science and artificial intelligence applications in healthcare. His research interests span clinical data science, artificial intelligence, applied probability theory, statistics, and machine learning with applications in medicine, agriculture, and telecommunications. His fingerprint analysis reveals specialized expertise in Diffuse Large B-Cell Lymphoma (100%), B Cell research (74%), Multiple Myeloma (41%), Overall Survival analysis (40%), Malignant Neoplasm studies (34%), and Personalized Medicine (23%). His recent publications demonstrate a strong focus on AI applications in healthcare, privacy-preserving synthetic data generation, clinical trial participation, and time-to-event prediction models. His work shows a clear trend toward integrating AI for personalized risk assessment and clinical interventions, with significant emphasis on ethical development of health data technologies. Martin Bøgsted actively supervises master's and PhD students, with records indicating 14 PhD students supervised. He leads major research projects including SE3D (Synthetic health data: ethical development and deployment via deep learning approaches) funded by the Novo Nordisk Foundation (2024-2028) and ARISTOTELES (Integrating AI for Personalized Risk Assessment) (2023-2028). He directs the Center for Clinical Data Science (CLINDA) at Aalborg University, which focuses on developing innovative infrastructure, artificial intelligence, and digital tools for multimodal data fusion and predictive modeling in the healthcare sector. His leadership extends to advisory roles including the Working Group for the National Genome Database and the Advisory Board for Research and Infrastructure at the Danish National Genome Center.

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Yubo SongView profile

Research Fellow

Aalborg UniversityDenmark

Yubo Song is a Postdoctoral Researcher at Aalborg University's Faculty of Engineering and Science, specializing in power electronics, microgrid systems, and reliability engineering. His work focuses on stability validation, control strategies, and the application of neuromorphic computing to power grids. His educational background includes: Ph.D. in Energy Technology from Aalborg University (awarded 2023) M.Sc. in Electrical Engineering from Shanghai Jiao Tong University (awarded 2019) B.Eng. in Electrical Engineering and Automation from Shanghai Jiao Tong University (awarded 2016) Dr. Song's research integrates microgrid engineering, power electronics reliability, and neuromorphic computing to address critical challenges in grid stability. His work pioneers Spike Talk technology for energy-efficient grid communication and develops hybrid modeling approaches for real-time power system simulation. Recent publications demonstrate expertise in damping control of grid-forming inverters, sensitivity analysis for reliability evaluation, and Bayesian risk assessment frameworks. His publication trends reveal a strategic shift toward neuromorphic solutions for power grids, with 2024-2025 works focusing on spike-based communication protocols and event-driven semantic systems. Earlier research centered on stability validation and reliability modeling for microgrids, establishing foundational frameworks still cited in current literature. Dr. Song has secured significant research funding including: Power Sentinel (Villum Foundation, 2024-2025): Developing cyber-physical safeguards for future power grids Reliability and Lifetime Analysis (2022-2025): Creating predictive models for power electronic components Stability and Reliability Validation (PhD project, 2020-2023): Establishing comprehensive microgrid assessment methodologies As Principal Investigator on multiple projects, he mentors early-career researchers while collaborating with industry partners on grid security solutions. His work with the Applied Power Electronic Systems group at AAU Energy focuses on translating theoretical advances into practical grid applications, particularly in converter reliability and neuromorphic grid communication.

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Raúl Pardo JimenezView profile

Associate Professor

IT University of CopenhagenDenmark

Raúl Pardo Jimenez is an Associate Professor in the Department of Software Engineering at the IT University of Copenhagen, affiliated with the Software Quality Research (SQUARE) group. His research focuses on formal methods and privacy engineering, with particular emphasis on probabilistic programming and privacy risk quantification in data analytics. His research spans three interconnected domains: Formal verification of privacy compliance systems like GDPR implementation Probabilistic modeling for quantitative privacy risk assessment Open source sustainability impacts on software quality metrics Recent publications demonstrate a consistent focus on developing rigorous computational methods for privacy preservation, with works appearing in Springer's Lecture Notes in Computer Science and Empirical Software Engineering journals. He leads the REIDENT project (2018-2022), funded by Villum Fonden, which developed Bayesian probabilistic programming approaches for reidentification risk assessment in sensitive datasets.

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Ahmad Salim Al-SibahiView profile

Assistant Professor

IT University of CopenhagenDenmark

Dr. Ahmad Salim Al-Sibahi is an Assistant Professor in the Programming Language and Theory of Computation Section at the University of Copenhagen's Department of Computer Science. He is a core member of the Aleatory Science Team, focusing on probabilistic programming applications in protein folding and inference algorithm optimization. His work combines formal methods with practical machine learning frameworks like Pyro. Educational Background: PhD in Computer Science (2014-2017), IT University of Copenhagen MSc in Computer Science (2014), IT University of Copenhagen Postdoctoral Researcher at University of Copenhagen (2017-2019) Research Interests: His primary focus lies at the intersection of probabilistic programming and computational biology. He develops efficient inference algorithms for probabilistic models, particularly in protein structure prediction and voucher scanning applications. His earlier work includes formal verification of program transformations using inductive refinement types and static analysis techniques. Awards: Best Paper Award at GPCE 2018 Contributions: He contributed to dependently-typed language Idris and co-developed family-based model checking techniques. His research bridges theoretical programming language constructs with applied problems in bioinformatics and software modernization.

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Anders Læsø MadsenView profile

Professor

Aalborg UniversityDenmark

Anders Læsø Madsen is a Professor at the Department of Computer Science , part of The Technical Faculty of IT and Design at Aalborg University . His research focuses on probabilistic graphical models, with a particular emphasis on Bayesian networks and their applications in industrial and environmental domains. Current affiliation: Aalborg University Research areas: Bayesian networks, probabilistic inference, decision support systems, data stream modeling His recent work spans control room engineering , where AI systems aid human operators, and environmental risk assessment using probabilistic models of pharmaceutical impacts. He also contributes to artificial intelligence in power grid monitoring , addressing anomaly detection through Bayesian reasoning. Publications from 2024-2025 demonstrate interdisciplinary applications, including electricity grid data validation , explainable AI frameworks , and pharmaceutical risk modeling . These works integrate probabilistic methods with domain-specific challenges in energy systems, industrial automation, and environmental science.

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Raúl PardoView profile

Associate Professor

IT University of CopenhagenDenmark

Raúl Pardo is an Associate Professor in Computer Science at the IT University of Copenhagen (ITU). He previously held postdoctoral positions at ITU's SQUARE group and Inria's Privatics team. He earned his PhD from Chalmers University of Technology, focusing on formalizing privacy policies for social networks under Gerardo Schneider's supervision. His research integrates formal methods with privacy and security, addressing topics such as privacy risk analysis, probabilistic programming, and GDPR compliance. Key domains include genetic data, IoT, and social networks. Publications highlight contributions to privacy quantification, formal verification of consent mechanisms, and probabilistic systems analysis. He actively participates in conferences like SEFM, SOSYM, and ETAPS, and has organized tracks on verification and learning for autonomous systems. Pardo teaches courses on probabilistic programming, formal methods, and security at ITU, INSA Lyon, and Chalmers. Notable students include Rasmus Carl Rønneberg (SEFM'23) and Ivana Kellyérová (TIME'16). His work has been recognized, including a nomination for the Danish MSc thesis award. He leads the SQUARE research group at ITU, focusing on privacy-preserving software systems and probabilistic modeling.

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Matteo MarsiliView profile

Researcher

University of CopenhagenDenmark

Matteo Marsili is a Senior Research Scientist at the Quantitative Life Sciences Section of the Abdus Salam International Centre for Theoretical Physics (ICTP) in Trieste, Italy. He holds a PhD from SISSA, Trieste (1994) and has held postdoctoral positions at Manchester University, Fribourg University (Switzerland), and SISSA. He joined ICTP in 2002, initially in the Condensed Matter and Statistical Physics (CMSP) Section before transitioning to the Quantitative Life Sciences Section. His research spans interdisciplinary domains, applying statistical physics to complex systems. Key areas include non-equilibrium statistical mechanics, critical phenomena, quantitative finance, statistical inference, machine learning, systems biology, and neuroscience. He investigates how collective behaviors emerge from interactions among simple units—such as particles, neurons, or financial traders—using tools from probability, information theory, and thermodynamics. His recent publications (2020–2025) show a strong focus on information-theoretic approaches to learning, relevance quantification, deep learning, and optimal inference. Themes include Bayesian modeling, minimal complexity, self-organized criticality in neural networks, and thermodynamics of information in financial markets. His work frequently appears in journals like Physical Review E , Journal of Statistical Mechanics , Physics Reports , and PLoS ONE . Matteo Marsili has collaborated extensively with researchers such as Y. Roudi, R.J. Cubero, J. Song, and R. Xie, suggesting active mentorship and team leadership. While no formal awards are listed, his sustained publication record in high-impact journals reflects significant scientific contributions. His lectures at institutions like the Kavli Institute and IHÉS further highlight his academic influence.

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Christoph MathejaView profile

Associate Professor

Technical University of DenmarkDenmark

Christoph Matheja is an Associate Professor at the Technical University of Denmark , affiliated with the Department of Applied Mathematics and Computer Science . His work focuses on formal verification, probabilistic programming, and software systems engineering. Research Highlights : Operational semantics for probabilistic programs, fixed-point characterizations of rewards in MDPs, and data-driven Petri net modeling for process mining. Publications span formal verification techniques, probabilistic programming paradigms, and tools for process mining, emphasizing interdisciplinary applications in computer science and software engineering. Scientific Awards : POPL 2021 Distinguished Paper Advising & Grants : Supervises PhD students in automated reasoning about randomized algorithms, with project funding active from 2023 to 2026. Professional Activities : Serves on committees for ACM SIGPLAN Symposium on Principles of Programming Languages (2022–present), International Conference on Computer Aided Verification (2023–present), and Formal Methods Symposium (2022–present). Organizer of the 2022 Workshop on Verification of Probabilistic Programs.

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Philipp Georg HaselwarterView profile

Assistant Professor

Aarhus UniversityDenmark

Philipp Georg Haselwarter is an Assistant Professor at the Department of Computer Science , Aarhus University . His expertise lies in programming languages , formal verification , and cryptographic proofs . His research interests include Higher-order probabilistic programming Separation logic for program analysis Modular cryptographic proofs in Coq Resource-aware and cost-bounded formal methods Key trends in his recent publications span Formal verification techniques for probabilistic and cryptographic systems Integration of separation logic with cost and error analysis Development of frameworks like SSProve for cryptographic proofs Applications of type theory and higher-order logic in programming languages

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Jens Ledet JensenView profile

Professor

Aarhus UniversityDenmark

Jens Ledet Jensen is a Professor in the Department of Mathematics at Aarhus University, with over 40 years of academic experience. His research focuses on mathematical statistics, asymptotics, saddlepoint approximations, and probabilistic modeling in biological experiments. He has collaborated with institutions like Aarhus University Hospital and researchers in political science, particularly on applications of the NIG distribution. Key research areas: Mathematical Statistics, Asymptotics, Saddlepoint Approximations, Hidden Markov Models, High-Dimensional Data Inference Affiliation: Aarhus University (Faculty of Science, Department of Mathematics) Jensen has contributed to data science education, including developing an English-language bachelor's program and authoring interactive web-based statistics materials. His recent work involves interdisciplinary collaborations, particularly in public administration and biological data analysis. Selected activities include workshops, seminars, and conference participation (e.g., 58th ISI Congress in 2011, workshops on ambit stochastics in 2011, and presentations on topics like multiple regression in 2018).

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Rune Dodensig KjærsgaardView profile

Researcher

Technical University of DenmarkDenmark

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).

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