جست‌وجوی استاد و پژوهشگر

Lars Nørvang Andersenمشاهده پروفایل

دانشیار

• Statistical Learning
• Bioinformatics
• Mathematical Modelling
• +۵ مورد دیگر

Lars Nørvang Andersen is an Associate Professor at the Department of Mathematics, Aarhus University, affiliated with the Stochastics research group. His academic journey includes a Ph.D. in applied probability theory (2009) and a master's degree in statistics from Aarhus University. Research focus: statistical learning, bioinformatics, stochastic modeling Former positions: Postdoctoral researcher at Bioinformatics Research Centre (Aarhus) and Stanford University's Department of Biology Academic service: Assessment committees and Danish Statistical Society treasurer His research spans statistical learning, machine learning, and stochastic modeling in bioinformatics and operations research. Recent publications highlight interdisciplinary work in Gaussian process models, population genetics, and differentially private learning algorithms. Key trends in publications include: Advancements in self-distillation techniques (2021) Applications of phase-type distributions in genetics Stochastic modeling for rare event simulations (2018) Optimization algorithms for logistics and machine learning Teaching activities cover applied statistics, theoretical statistics, and modern statistical learning at bachelor's and master's levels, with industry collaborations in student projects.

Fabrice Baudoinمشاهده پروفایل

استاد

• Stochastic analysis
• Differential geometry
• Dirichlet forms
• +۶ مورد دیگر

Fabrice Baudoin is a Professor at the Department of Mathematics, Aarhus University . His research focuses on stochastic analysis, differential geometry, and Dirichlet forms, with applications to Lie groups, manifolds, and fractal spaces. Research Interests : Stochastic methods in geometric analysis Heat kernel theory and functional inequalities Sub-Riemannian geometry and curvature bounds Probability distributions for Brownian motion functionals Dirichlet forms on fractal spaces Publications Trends : Recent works address sub-Riemannian comparison theorems, hypoelliptic SPDEs, infinite-dimensional diffusions, and stochastic processes on fractals. His research combines geometric insights with probabilistic techniques. Teaching Activities : He has taught diverse undergraduate and graduate courses, with lecture notes available on his blog https://fabricebaudoin.blog .

Jens Ledet Jensenمشاهده پروفایل

استاد

• Mathematical Statistics
• Asymptotics
• Saddlepoint Approximations
• +۵ مورد دیگر

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).

Marco Carboneمشاهده پروفایل

استاد

• Theoretical Computer Science
• Session Types
• Concurrency Theory
• +۷ مورد دیگر

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 .

Mark C. Kellyمشاهده پروفایل

دانشیار

• Atmospheric stability modeling
• Atmospheric turbulence
• Boundary-layer meteorology
• +۷ مورد دیگر

Mark C. Kelly is an Associate Professor in the Resource Assessment and Meteorology Wind Energy Systems Division at the Department of Wind and Energy Systems, Technical University of Denmark (DTU). His research focuses on advancing wind energy modeling and resource assessment through sophisticated atmospheric science approaches. His primary research interests include: Atmospheric stability modeling and representation of buoyancy effects Atmospheric turbulence and subgrid modeling Scale-dependent terrain characterization Mesoscale-microscale interaction Probabilistic meteorological characterization for wind energy applications Air-sea interaction and marine boundary layer processes His recent publications show a strong trend toward uncertainty quantification in wind resource assessment, advanced modeling techniques for wind farm flow simulations, and the development of probabilistic approaches for turbine design. His work bridges fundamental atmospheric science with practical wind energy applications, particularly focusing on how to better model complex atmospheric phenomena for improved wind energy prediction and resource assessment. Dr. Kelly is actively involved in supervising PhD students and research projects. He serves as the Principal Investigator for the WAsP Scientific Development project and is a key participant in several major initiatives including AWETRAIN (Airborne Wind Energy Training) and IEA Wind Tasks 41 and 54. His editorial work includes serving as an editor for Theoretical and Applied Climatology and as a reviewer for multiple prestigious wind energy and renewable energy journals. His research group collaborates extensively with international partners across multiple countries, focusing on advancing wind energy technologies and their integration into sustainable energy systems. The team works closely with industry partners to translate research findings into practical applications for the wind energy sector.

Rune Dodensig Kjærsgaardمشاهده پروفایل

پژوهشگر

• Data Representation
• Machine Learning
• Neural Networks
• +۵ مورد دیگر

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).

• Mathematics
• Applied Mathematics
• Statistics
• +۱ مورد دیگر

Anna Kathrine Skjøth serves as an Instructor (mapped to Lecturer rank) in the Department of Mathematical Sciences at the University of Copenhagen's Faculty of Science. Her institutional affiliation is centered at Universitetsparken 5, 2100 København Ø, with primary contact via aks@math.ku.dk. Her research profile is derived from departmental context, emphasizing core mathematical disciplines including foundational Mathematics, computational Applied Mathematics, probabilistic Statistics, and algorithmic Theoretical Computer Science. These interconnected fields form the intellectual framework for quantitative analysis and modeling within the department's academic mission. No scientific awards or honors were documented in the provided institutional records. Student advisement activities and research grant portfolios were not referenced in the available administrative materials, indicating either non-disclosure or non-applicability to her current instructional role.

• Forensic Genetics
• DNA Profiling
• Mitochondrial DNA Analysis
• +۳ مورد دیگر

Hans Jakob Larsen is a Forensic Geneticist at the Department of Forensic Genetics within the Institute of Forensic Medicine, University of Copenhagen's Faculty of Health Sciences. With over 25 years of continuous service since 1999, he conducts forensic DNA analysis, manages crime case investigations, and provides expert testimony in Danish courts. His educational background includes: Master of Science (cand. scient.) from University of Aarhus (1992) Ph.D. in Molecular Biology from University of Aarhus (1994) Specialized training in Forensic Statistics (2005) and DNA analysis techniques Larsen's research centers on forensic DNA methodologies, specializing in mitochondrial DNA sequencing, STR analysis, and complex DNA mixture interpretation. His work bridges molecular biology and criminalistics, developing protocols for biological trace evidence analysis and statistical evaluation of DNA evidence. Recent publications demonstrate increasing focus on international collaborative frameworks for standardizing DNA mixture interpretation and software validation in forensic laboratories. His publication trends since 2015 show concentrated efforts in probabilistic genotyping systems (STRmix™) and expert software development (EDNA), addressing critical challenges in low-template DNA and mixture deconvolution. The 2025 ReAct project represents current work establishing cross-laboratory standards for DNA recovery analysis under activity-level propositions. Larsen has managed forensic laboratories (5-16 technicians) and temporarily led the Crime Case Section (20 academics, 40 technicians). His teaching portfolio includes specialized courses for Danish police on biological trace analysis and seminars for legal professionals on DNA evidence interpretation. He contributed to the 2005 Thai Tsunami Victim Identification operation and maintains active casework involvement.

• Evolutionary Biology
• Computational Genomics
• Bioinformatics
• +۴ مورد دیگر

Jakob Skou Pedersen is an external researcher affiliated with the Faculty of Health and Medical Sciences at the University of Copenhagen. His work spans evolutionary biology, genomics, and bioinformatics, with significant contributions to understanding human evolutionary constraints, regulatory RNA structures, and ancient human genomes. His research interests focus on computational approaches to evolutionary genomics, including the identification of functional elements in genomes through comparative analysis across multiple species. He has made notable contributions to understanding protein-coding sequences under selection for overlapping functions, regulatory RNA structures, and the evolutionary constraints across mammalian genomes. Analysis of his publication record reveals a strong emphasis on interdisciplinary research combining computational biology with evolutionary genomics. His work frequently appears in high-impact journals including Nature, Cell, and Genome Research, demonstrating expertise in both theoretical modeling and empirical genomic analysis. A significant portion of his research involves large-scale comparative genomic studies across multiple species. His collaborative network is extensive, with co-authorship on major genomic projects involving international research teams. His work on the ancient Palaeo-Eskimo genome represents a notable contribution to paleogenetics and human evolutionary studies.

• Atmospheric Science
• Environmental Radioactivity
• Risk Assessment
• +۲ مورد دیگر

Jens Sørensen is a Lecturer in the Department of Cellular and Molecular Medicine at the University of Copenhagen, affiliated with the ICMM Teaching unit. His departmental role suggests involvement in biomedical education, though his research publications focus on atmospheric science and environmental risk modeling. His research interests include atmospheric dispersion, radionuclide transport, and environmental risk assessment. Publications demonstrate expertise in predictive modeling for industrial pollutants and nuclear contaminants, emphasizing emergency response frameworks and climate impact analysis.