Tom Engsted is a full-time Professor at the Department of Economics and Business Economics, Aarhus University, Denmark. He has been continuously active in research and academic activities since 1991, with recent outputs and engagements extending into 2025. Research Interests: Engsted’s work spans econometrics, financial economics, and statistical methodology, focusing on topics like stock and housing market valuations, risk aversion, and hypothesis testing frameworks. His research explores the likelihood principle, false discovery rates, and habit-persistence hypotheses in economic models. Recent Publications: His recent articles analyze methodological challenges in statistical inference, including confidence interval misinterpretations, significance thresholds dependent on sample size, and false discovery rates in empirical research. Themes emphasize improving rigor in econometric analysis and addressing reproducibility issues. Scientific Awards: Den Gyldne Pegepind (2014) Den Gyldne Pegepind (2016) Den Gyldne Pegepind (2018)
Jesper N. Wulff is a Professor at the Department of Economics and Business Economics, Aarhus University. His research bridges statistical methodology with business analytics to advance causal inference in organizational science. Education: PhD in Economics and Business Economics (2015), MA in Data Analysis (2015), MA in Business Administration (2011) Jesper's research focuses on statistical innovation , including sample size-dependent alpha levels, Bayesian-frequentist hybrid methods, and sensitivity analysis via the Impact Threshold for Confounding Variables (ITCV). His work spans corporate finance (CEO personality traits and firm dynamics), social epidemiology , and public administration . Recent articles address methodological gaps in confounding variable comparisons and error rate optimization. Scientific awards include the 2022 Award for Responsible Research in Management, 2018 Aarhus BSS Lecturer of the Year, and multiple best paper nominations. He serves as Associate Editor for The Leadership Quarterly and Methods Consultant for Journal of Management .
Ka Lok Lo is a Postdoctoral Fellow at the Niels Bohr Institute, University of Copenhagen, specializing in Theoretical High Energy, Astroparticle and Gravitational Physics. His research primarily focuses on gravitational waves, black hole physics, and gravitational lensing phenomena. Dr. Lo's research interests span multiple areas of theoretical astrophysics with emphasis on gravitational wave detection , gravitational lensing , and black hole physics . His work involves developing and applying advanced mathematical frameworks to analyze gravitational wave signals, particularly those affected by strong gravitational lensing. He has made significant contributions to the identification of lensed gravitational wave events through phase consistency tests and Bayesian statistical frameworks. His publication record shows a strong focus on gravitational wave astronomy with particular attention to lensed signals. Dr. Lo's work bridges theoretical physics with observational astronomy, developing methods to extract cosmological information from gravitational wave data. His recent publications demonstrate expertise in gravitational lens catalogs, waveform analysis for binary coalescence events, and theoretical studies of black hole perturbations. Dr. Lo collaborates extensively with international research teams including the LIGO Scientific Collaboration and works within the vibrant gravitational physics community at the Niels Bohr Institute. His research contributes to advancing our understanding of strong-field gravity and developing new methods for gravitational wave astronomy.
Mahya Mohammadi Kashani is a Researcher in Software Engineering at the IT University of Copenhagen, specializing in risk assessment for underwater robotics. She serves as an Early Stage Researcher in the REMARO (Reliable AI for Marine Robotics) network, a European Commission-funded project advancing trustworthy AI for marine applications. Her academic background includes: PhD in Computer Science (2021-2024), IT University of Copenhagen: Focused on statistical assessment of plans via probabilistic optimization of reliability. MSc in Artificial Intelligence and Robotics (2016-2019), Shahid Rajaee Teacher Training University: Researched search-based image annotation using deep models. BSc in Software Engineering (2010-2014): Developed filter drivers for disk access control at low-level driver hierarchies. Her research develops Bayesian-inference-based methods for risk-averse decision-making in underwater robotics, creating probabilistic models to assess operational risks and select reliable robot plans. Previously, she investigated pattern recognition algorithms, sparse reconstruction/coding techniques, and search space reduction for automatic image annotation systems. Her publications (2022-2025) establish a cohesive framework for risk-aware marine robotics, addressing fault recovery, risk quantification, and plan assessment through probabilistic optimization and belief-based planning models. No scientific awards were documented in the source material. Funded by the REMARO project (2020-2025), she contributes to European Commission research on trustworthy AI, active learning, and image segmentation for underwater robotics as a co-investigator. Her collaborative work spans risk-averse planning methodologies and reliability engineering for autonomous marine systems. Within the REMARO network, she collaborates on advancing reliable AI for marine robotics through active learning techniques and image segmentation solutions for challenging underwater environments.
Peter Norman Sørensen is a Professor of Finance at the Department of Economics, University of Copenhagen, within the Faculty of Social Sciences. He serves as Director of the Finance Research Unit and has held significant administrative roles including Deputy Head of Department (2015-2019) and Director of the Economics PhD Programme (2008-2010). His academic career at the University of Copenhagen spans from Assistant Professor (1998-1999) to Associate Professor (1999-2006) and ultimately to his current Professor position since 2006. His primary research interests focus on Asymmetric Information in Finance and Economics, Herding Behavior, Prediction Markets, and Microstructure of Financial Markets. Sørensen's work develops and applies economic theory with asymmetric information to markets and institutions, particularly examining herding behavior of large groups, reputation-building for financial experts, and asset price responses to information. His current research includes studying taxation of transactions in imperfect financial markets, participating in the ERC-funded EVALIDEA project, and membership in research centers including the Center for Financial Frictions (FRIC), the Center for Information and Bubble Studies (CIBS), and the Center for Blockchains and Electronic Markets. Sørensen's publication record shows consistent output in top economics journals, with research trends indicating a strong focus on information economics, market microstructure, and behavioral finance. His work often examines how information asymmetries affect market outcomes, with particular attention to herding behavior, prediction markets, and financial market regulation. Scientific Awards 2012 Elite Forsk researcher prize from the Danish Ministry of Science, Innovation and Higher Education 2011 Finance researcher prize from the Nykredit Foundation 2009 Excellence in Refereeing Award from the American Economic Review Economic Journal referee prize 2021 Sørensen has supervised numerous PhD students and theses in the intersections of Microeconomics, Game Theory and Finance. He has received research grants from prestigious sources including the Danish Social Science Research Council and the European Research Council. His editorial roles include positions at the Journal of Mathematical Economics, Scandinavian Journal of Economics, Economica, and Macroeconomic Dynamics. Professor Sørensen is actively involved with several research centers including the Center for Financial Frictions (FRIC), the Center for Information and Bubble Studies (CIBS), and the Center for Blockchains and Electronic Markets, where he collaborates on interdisciplinary research projects examining market dynamics, information flows, and financial regulation.
Tommy Sonne Alstrøm is an Associate Professor at the Department of Applied Mathematics and Computer Science, DTU Compute, Danmarks Tekniske Universitet (DTU). His research focuses on machine learning, signal processing, and nanotechnology-based sensor systems. Key research areas include: Explainable AI for time series and spectroscopy Diffusion models for speech enhancement Federated learning optimization Probabilistic modeling of biosignals His recent publications demonstrate expertise in neural network geometry, federated learning privacy, and spectroscopic data analysis. Current work involves self-supervised learning for variable-channel time series and road condition modeling using LiRA-CD dataset. Contact: tsal@dtu.dk
Professor Jesper Wiborg Schneider is affiliated with the Department of Political Science and the Danish Centre for Studies in Research and Research Policy at Aarhus University, part of Aarhus BSS. His research focuses on quantitative science studies, research evaluation, science policy, research integrity, and inferential statistics, with a critical emphasis on methodological practices in the soft sciences. Primary Affiliation: Department of Political Science, Aarhus University Secondary Affiliation: Danish Centre for Studies in Research and Research Policy His work examines social and reward structures in science, scientific norms, citation practices, research evaluation methodologies, and the ritualistic use of null hypothesis significance testing. He advocates for Bayesian approaches and has been colloquially labeled a "methodological terrorist" for his critiques of traditional statistical methods. Research projects include: TRUSTparency (2025-2028): Enhancing reproducibility through transparent interventions. SOPs4RI (2019-2022): Standard Operating Procedures for Research Integrity. PRINT (2017-2019): Investigating research integrity practices.
Marleen de Bruijne is Professor of AI in Medical Image Analysis jointly appointed at the University of Copenhagen, Denmark and Erasmus MC – University Medical Center Rotterdam, The Netherlands. Within the Department of Computer Science at Copenhagen she belongs to the Image Analysis, Computational Modelling and Geometry section, where she leads research at the intersection of machine learning and medical imaging. Education MSc in Physics, Utrecht University, 1997 PhD in Medical Imaging, Utrecht University, 2003 Research Interests Her work centers on developing and validating machine-learning algorithms for quantitative analysis of medical images. Key themes include: Transfer learning and domain adaptation across imaging centers Deep learning architectures for segmentation and classification of pulmonary, cardiovascular and neuro images Probabilistic graphical models and Bayesian approaches for robust airway and vessel extraction Computer-aided diagnosis systems for emphysema, bronchiectasis, COPD and calcification Her group translates these techniques into clinical workflows to improve early diagnosis and patient management. Scientific Awards NWO-VENI (Netherlands Organisation for Scientific Research) NWO-VIDI NWO-VICI DFF-YDUN (Danish Council for Independent Research) Advising & Grants She has (co-)supervised 30 PhD students to completion and served as principal investigator on several large personal grants. Her funding record demonstrates sustained support from both Dutch and Danish national science foundations. Labs & Teams She is actively involved in the SCIENCE AI Centre at the University of Copenhagen and maintains strong collaborative links with Erasmus MC Radiology and Pulmonology departments, fostering cross-institutional datasets and multicenter clinical validation studies.
Thomas Wim Hamelryck is a Professor at the Department of Computer Science (Programming Languages and Theory of Computation) and the Department of Biology (Computational and RNA Biology) at the University of Copenhagen . With a PhD in Protein Crystallography from the Free University of Brussels (VUB), he specializes in Bayesian modeling , probabilistic machine learning , and statistical structural bioinformatics , focusing on protein structure prediction, evolution, and non-Euclidean data representation.
Nicklas Boserup serves as an Instructor at the Department of Computer Science (DIKU) at the University of Copenhagen, located at Universitetsparken 1 in Copenhagen Ø. His academic appointment places him within the institution's research and teaching structure, contributing to computer science education and research activities. Boserup's research interests center on machine learning applications in medical imaging, with particular focus on self-supervised learning techniques for histopathology image analysis. His work bridges computer vision and healthcare applications, developing algorithms that can process and segment medical images without requiring extensive labeled training data. This research direction addresses critical challenges in digital pathology where annotation by medical experts is time-consuming and costly. Analysis of Boserup's publication record shows a consistent focus on contrastive learning approaches for medical image segmentation, with his 2022-2023 work establishing foundational techniques in patch-based contrastive learning for histopathology. His 2024 publication represents an expansion into parameter inference methods using differentiable diffusion models, indicating a broadening research scope into statistical computing and probabilistic modeling. The trajectory suggests increasing sophistication in handling complex medical imaging data through advanced machine learning techniques. Boserup maintains an active research profile with publications appearing in both preprint repositories and conference proceedings, demonstrating engagement with the academic community. His email contact (nibos@di.ku.dk) provides a direct channel for academic collaboration and professional communication within the university framework.
Axel Højmark serves as a Lecturer in the Department of Computer Science at the University of Copenhagen, actively contributing to the Human-Centred Computing section. This research group focuses on advancing the relationship between computational technology and human users through supporting well-being, designing novel interaction paradigms, and analyzing technology's impact on human capabilities within a collaborative, supportive academic environment. His research centers on Human-Computer Interaction and Artificial Intelligence evaluation, with particular emphasis on language model agent capabilities and AI-generated content systems. Recent publications reveal a methodological focus on probabilistic evaluation frameworks and forecasting techniques for frontier AI systems, bridging technical AI development with human-centered application contexts in social media and virtual environments. Analysis of his 2023-2025 publications shows consistent investigation into evaluation rigor for emerging AI technologies, with increasing sophistication in capability forecasting methods. The work spans natural language processing, social computing, and cognitive systems, maintaining strong connections to real-world deployment challenges in social media platforms and interactive systems. The Human-Centred Computing section provides state-of-the-art laboratories with specialized equipment for interaction research, fostering interdisciplinary collaboration across the department's eight research sections including Machine Learning, Natural Language Processing, and Pioneer AI initiatives.
Claus Thorn Ekstrøm is a Professor at the University of Copenhagen , affiliated with the Faculty of Health and Medical Sciences and the Section of Biostatistics . His work integrates advanced statistical methods with biomedical and public health research. Contact details include ekstrom@sund.ku.dk and phone numbers +4535327597, +4535327629. The Section of Biostatistics is located at Øster Farimagsgade 5 opg. B, 1014 København K. Research Focus : Bioinformatics, statistical genetics, genetic epidemiology Teaching : Biostatistics, statistics, and bioinformatic methods Ekstrøm’s recent research trends emphasize causal inference , longitudinal data modeling , and integrated analysis of genetic and metabolic data . His publications span topics like biomarker development in psychiatric disorders , statistical frameworks for complex family data , and computational efficiency in Gaussian process regression , reflecting interdisciplinary applications in public health, genetics, and epidemiology.
Ana Alina Tudoran is an Associate Professor at Aarhus University, specializing in quantitative methods within business intelligence. Her academic work bridges machine learning, data mining, and consumer behavior analysis. Primary affiliation: Department of Economics Research focus areas: Artificial Intelligence, Customer Lifetime Value modeling, and Hybrid Intelligence systems Notable projects include pandemic-era consumer behavior studies and IoT privacy reviews Her recent publications span supply chain optimization, organizational text analytics, and responsible AI deployment. Tudoran actively contributes to methodological advancements through hybrid PLS-SEM/machine learning frameworks and is a frequent speaker at management conferences.
Natalia Khorunzhina is an Associate Professor at Copenhagen Business School's Department of Economics. Her research examines household economic behavior through structural modeling approaches, focusing on consumption patterns, housing choices, and intertemporal decision-making. Research Focus: Primary areas include: Household consumption/savings behavior with habit formation Housing investment decisions correlated with fertility/work choices Risk attitude measurement in consumption models Mortgage design impacts on consumption growth Lifecycle asset allocation between risky/riskless assets Publication Trends: Recent articles focus on housing market dynamics during economic cycles, mortgage reform effects, and wealth inequality patterns. Methodologically, work emphasizes structural econometric modeling of household decisions using mixture approximations for complex density kernels. Earlier publications established foundations in stock market participation costs and microfinance impacts.
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.
