Mikael B. Skov is a Vice Dean and Professor at the Technical Faculty of IT and Design of Aalborg University , Denmark. His research spans human-AI interaction, robotics, and user experience, with a focus on trust signaling in clinical AI, swarm robotics, and sound zones for domestic environments. Role: Vice Dean for Research Department: Computer Science Research Interests: Skov investigates how humans interact with AI and robots in healthcare and domestic settings, emphasizing trust calibration, alert design, and acoustic comfort. His work includes developing frameworks for UX maturity in robotics organizations and studying long-term adoption of sound zone systems. Recent Projects: As principal/co-investigator, he leads the HERD project on human-AI collaboration in robot swarms (2021–2025) and supervises Data og Bæredygtig Mad (2020–2023), an HCI perspective on sustainable food systems. Publications: His 2024 work includes studies on AI explanations in clinical training, music applications with intermittent interactions, and multi-robot supervision. Earlier projects (2001–2020) focused on mobile device usability, UX practices, and context-aware computing.
Jesper Rindom Jensen is an Associate Professor in the Department of Electronic Systems at Aalborg University, Denmark, under the Technical Faculty of IT and Design. He is the Head of the Audio Analysis Lab, a leading research group in audio signal processing, since 2023. His work bridges theoretical signal processing and practical applications in artificial intelligence and audio systems. Full Name: Jesper Rindom Jensen Institution: Aalborg University School: The Technical Faculty of IT and Design Department: Department of Electronic Systems Research Lab: Audio Analysis Lab Email: jrj@es.aau.dk Office: Fredrik Bajers Vej 7B, B5-206, 9220 Aalborg Øst, Denmark Education: M.Sc. in Electronic Systems, Aalborg University (cum laude, 2009) Ph.D. in Signal Processing, Aalborg University (2012) Research Interests: Jesper Rindom Jensen's research centers on audio signal processing, with a strong emphasis on artificial intelligence, speech enhancement, noise reduction, beamforming, and multichannel systems. His work applies to diverse domains including robot and drone audition, spatial audio, and active noise control. He develops novel filtering techniques, including variable span linear filters and harmonic beamformers, to improve speech quality and intelligibility in noisy and reverberant environments. Publication Trends: His recent publications (2023–2025) show a strong trend toward integrating deep learning with classical signal processing, particularly in direction-of-arrival estimation, underwater acoustics, and robust multichannel systems. There is a clear focus on real-world applications, including sound zone control, active noise control, and limited-data scenarios using knowledge distillation. His work consistently emphasizes robustness, efficiency, and practical deployment. Scientific Awards and Recognition: AAU Talent for emerging research leaders Recipient of a competitive postdoc grant from the Danish Independent Research Council Advising and Grants: Jesper has supervised multiple PhD and master’s students, including Nørholm, Karimian-Azari, Zhang, and Wang. He has led significant research projects such as 'Sound Processing for Robots and Drones' (2018–2020) and participated in others related to joint audio-visual tracking and speech enhancement. His research has been supported by national funding bodies, reflecting its innovation and impact. Labs and Teams: He is a founding and core member of the Audio Analysis Lab at Aalborg University, which focuses on cutting-edge audio signal processing and AI-driven solutions. The lab fosters interdisciplinary collaboration and has produced numerous publications, datasets, and real-world applications. Jensen’s leadership since 2023 underscores his pivotal role in shaping the lab’s research direction.
James A. Evans is the Max Palevsky Professor of Sociology and Data Science at the University of Chicago, where he is a faculty member in the Department of Sociology within the Division of the Social Sciences. He is the director of Knowledge Lab and the Faculty Director of the Masters Program in Computational Social Science . He holds additional affiliations as an External Professor at the Santa Fe Institute , External Faculty at the Complexity Science Hub, Vienna , and Visiting Faculty Researcher at Google . Education: B.A. in Anthropology, Brigham Young University (1994) M.A. in Sociology, Stanford University (1999) Ph.D. in Sociology, Stanford University (2004) His research centers on the collective system of thinking and knowing , exploring how ideas emerge, spread, and evolve through social and technical systems. He investigates innovation, collective intelligence, and the science of science , using large-scale data modeling, machine learning, generative AI, and network analysis to study knowledge creation. His work spans domains including science, technology, law, and religion, with a focus on how AI is reshaping discovery processes. The most recent publications highlight trends in AI and scientific discovery , with a strong emphasis on innovation, knowledge systems, and human-machine intelligence . His research increasingly explores AI as a transformative agent in science , including the concept of 'alien intelligence' and the development of complementary AI to augment human capacity. Projects like the $20M NSF-funded APTO initiative aim to build language models that predict technological outcomes by analyzing historical data. Scientific Recognition and Funding: Research supported by the National Science Foundation (NSF) , National Institutes of Health (NIH) , Air Force Office of Scientific Research (AFOSR) , and philanthropic sources Work published in Nature, Science, PNAS , and leading social science journals Featured in The New York Times, The Economist, The Atlantic, Wired, NPR, BBC, Le Monde , and others James Evans advises on science policy and funding strategies, emphasizing the importance of diversity, interdisciplinary collaboration, and demographic balance in fostering innovation. He critiques current academic incentives and proposes alternative discovery regimes. He leads Knowledge Lab , a collaborative research environment that conducts seminars, grants, and employment opportunities in computational social science and AI.
Melih Kandemir is an Associate Professor at the Department of Mathematics and Computer Science, Southern Denmark University. He also serves as Research Group Leader at the Bosch Center for Artificial Intelligence (2018–2021) and held a previous role as Assistant Professor at Ozyegin University (2017–2018). His research focuses on machine learning, Bayesian methods, reinforcement learning, and uncertainty quantification. **Education**: PhD in Computer Science from Aalto University (2013), specializing in 'Learning Mental States from Biosignals'. **Research Interests**: Machine Learning, Bayesian Inference, Reinforcement Learning, Deep Neural Networks, Stochastic Processes. His work emphasizes theoretical foundations and practical applications in domains like medical imaging, control systems, and robotics. **Awards**: Two Best Paper Awards (2017). **Grants & Projects**: Includes the Carlsberg Young Researcher Fellowship (2022–2026), Novo Nordisk Foundation grants (2021–2024), and DFF-funded research on PAC-Bayesian reinforcement learning (2025–2027). **Labs/Teams**: Leads research on Bayesian deep learning and reinforcement learning within the Bosch Center for AI and SDU's interdisciplinary groups.
Kohei Nakajima is an Associate Professor at the Department of Intelligent Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo. He holds concurrent positions at the Department of Creative Informatics and the Next Generation Artificial Intelligence Research Center (AI Center). As an Endowed Chair in Advanced Artificial Intelligence Education, he leads the Physical Intelligence Lab, which focuses on the intersection of soft robotics, nonlinear dynamics, and physical computing. His research interests center on Physical Reservoir Computing (PRC), a paradigm that exploits the natural dynamics of physical systems for computation, with applications in soft robotics, spintronics, and quantum machine learning. Nakajima's work demonstrates how physical systems can inherently process information without traditional digital computation, leveraging phenomena like chaos, bifurcations, and embodied intelligence. Nakajima's publications reveal a strong focus on understanding how physical systems can perform computational tasks. His recent work spans from biological applications (jellyfish cyborgs, ostrich-inspired robotics) to fundamental theoretical advances in reservoir computing. The research demonstrates how physical phenomena can be harnessed for information processing, with implications for energy-efficient computing and novel robotic control paradigms. As the organizer of the Reservoir Computing Seminar, Nakajima has built a vibrant research community exploring the nature of information processing across disciplines. His lab actively recruits graduate students and postdocs, indicating strong research momentum and institutional support for his work in physical intelligence.
Anders Kalsgaard Møller is an Associate Professor in the Department of Culture and Learning at Aalborg University's Faculty of Humanities and Social Sciences. He is actively engaged in research and innovation in learning design, digital technologies, and artificial intelligence in education. His work is centered around the L-ILD (IT and Learning Design), Green Society, and MASSHINE Xlab – Design, Learning and Innovation research environments. His research interests span Learning Design , Computational Thinking , Artificial Intelligence in Education , Human-Robot Interaction , and Environmental Literacy . He investigates how emerging technologies can be integrated into educational practices to enhance collaborative learning, literacy development, and sustainable thinking. His work often involves participatory and co-design methods with educators and children. The recent publications of Anders Kalsgaard Møller reflect a strong trend toward the application of generative AI, robotics, and digital tools in language and primary education. His scholarly output emphasizes interdisciplinary collaboration, technological innovation, and real-world educational impact, particularly in K-12 and higher education contexts. Principal Investigator, 'Using artificial intelligence in English teaching at upper secondary schools' (2023–2026) Co-PI, 'Co-Designing Robot-Assisted Learning for Children' (ongoing) Co-PI, 'Labor market-oriented AI skills at cand.it.' (2024–2026) Co-PI, 'Understanding and fostering future consumers' environmental literacy' (2024–2025) Anders Kalsgaard Møller has been involved in media outreach, including coverage on children's interactions with social robots and discussions on AI in education. He has also contributed to academic leadership through conference organization and editorial roles, such as in the DLI conference series. He is affiliated with key research labs including: L-ILD – IT and Learning Design Green Society MASSHINE Xlab – Design, Learning and Innovation These labs focus on digital innovation, sustainability, and human-centered design in educational contexts.
Georgios Arvanitidis is an Associate Professor at the Technical University of Denmark (DTU) in the Department of Applied Mathematics and Computer Science, specifically within the Section for Cognitive Systems (CogSys). He has established himself as a leading researcher in geometric machine learning, focusing on the application of differential geometry principles to enhance machine learning models. His work bridges theoretical mathematics with practical applications in artificial intelligence, with particular emphasis on understanding the geometric structure of data manifolds and latent spaces. Dr. Arvanitidis completed his educational journey with a Bachelor's degree from the Department of Informatics at the Aristotle University of Thessaloniki, followed by a Master's degree in Computer Science from Saarland University supported by the Max Planck Institute for Informatics. He earned his PhD at DTU's Cognitive Systems section under the supervision of Søren Hauberg, with additional research experience at Philipp Hennig's Probabilistic Numerics group. Prior to his current position as associate professor, he was a PostDoc at the Max Planck Institute for Intelligent Systems working with Bernhard Schölkopf. Dr. Arvanitidis's research primarily focuses on differential geometry in machine learning , where he explores how geometric structures can enhance representation learning and statistical modeling. His work in generative models investigates how learning the geometry of data manifolds can improve deep learning architectures. In the domain of deep learning theory , he examines why deep learning models generalize effectively on unseen data, with particular attention to the curvature properties of loss landscapes. His research in approximate Bayesian inference applies geometric principles to improve uncertainty quantification in neural networks. Through his innovative approaches, Dr. Arvanitidis has established himself as a leading researcher in geometric machine learning, contributing to both theoretical foundations and practical applications across various domains including robotics and life sciences. The publication trends of Dr. Arvanitidis reveal a consistent and evolving focus on geometric approaches to machine learning problems. His recent work (2023-2025) demonstrates increasing sophistication in applying Riemannian geometry to deep learning architectures, with particular emphasis on latent space geometry, optimization on manifolds, and geometric interpretations of neural network behavior. A notable pattern is the progression from foundational work on geometric representations to more applied research in areas like robotics and causal inference. His publications span top-tier conferences including NeurIPS, ICML, ICLR, and AISTATS, reflecting the high impact of his research. The interdisciplinary nature of his work is evident in collaborations across mathematics, computer science, and robotics domains, with recent papers addressing challenges in multimodal sampling, safety guarantees for dynamical systems, and counterfactual explanations. Dr. Arvanitidis has received several notable scientific awards and recognitions: Sapere Aude starting grant from the Independent Research Fund Denmark (DFF) GADL funding i-Rase, Pathfinder, and EIC (European Innovation Council) funding Best reviewer award for NeurIPS 2019 Best reviewer award for NeurIPS 2018 Best student paper award at Robotics: Science and Systems (R:SS) 2021 Dr. Arvanitidis actively mentors PhD students and researchers, currently supervising Alejandro Valverde, Johanna Gegenfurtner, and Albert Kjøller Jacobsen. He has previously co-supervised Alison Pouplin's PhD and worked with research assistant Georgios Pantis. His group receives substantial funding through multiple prestigious grants including the Sapere Aude starting grant from the Independent Research Fund Denmark, as well as European Innovation Council funding. He has been instrumental in creating opportunities for students interested in geometric machine learning, offering BSc and MSc thesis projects focused on generative models, deep learning theory, and optimization techniques. Dr. Arvanitidis also contributes significantly to the academic community as a reviewer for top conferences including ICLR and TMLR, and as an area chair for NeurIPS, ICML, AISTATS, and UAI. He co-organized the Machine Learning Summer School 2020 in Tübingen, further demonstrating his commitment to education and community building. Dr. Arvanitidis leads a vibrant research group focused on geometric machine learning within the Cognitive Systems section at DTU. His team includes multiple PhD students working on cutting-edge research at the intersection of differential geometry and artificial intelligence. The group has developed notable software tools, including the "geometric_ml" GitHub repository with over 70 stars, which contains implementations for applying Riemannian geometry in machine learning. His research has practical applications in robotics, where geometric approaches enable more robust motion planning, as evidenced by his work on "Reactive Motion Generation on Learned Riemannian Manifolds" which received a best student paper award. Additionally, his methodologies have found applications in life sciences, as mentioned in his 2022 AISTATS paper. The collaborative nature of his work is evident through extensive partnerships with researchers at institutions including the Max Planck Institute for Intelligent Systems, University of Cambridge, and various European universities. His recent news items indicate active engagement with the academic community through talks, conference presentations, and ongoing supervision of new PhD students joining his group.
Sadegh Talebi is a Tenure Track Assistant Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen . His research focuses on theoretical aspects of reinforcement learning, Markov decision processes, online learning, stochastic multi-armed bandit problems, and resource allocation in networks. Education BSc in Electrical Engineering (minor: Electronics) from Iran University of Science and Technology (IUST) (2004) MSc in Electrical Engineering (minor: Communication Systems) from Sharif University of Technology (2006) PhD in Electrical Engineering from the Department of Automatic Control at KTH Royal Institute of Technology (supervised by Alexandre Proutiere and Mikael Johansson) Research Specializes in theoretical foundations of reinforcement learning and online learning Key contributions in stochastic optimization, MDPs, and bandit algorithms Collaborates on applications in resource allocation and quantum computing Publications include high-impact work on offline RL, differentially private exploration, and scalable MDP solutions in journals like Neural Processing Letters and conferences such as NeurIPS and UAI.
Mostafa Mohammadi is an Assistant Professor at the Department of Health Science and Technology, Aalborg University, affiliated with the Faculty of Medicine and Center for Rehabilitation Robotics. His research focuses on neurorehabilitation robotics, human-computer interaction, and telerehabilitation technologies for individuals with disabilities. Ph.D. in Biomedical Engineering (Aalborg University, 2018-2022) M.Sc. in Biomedical Engineering (Polytechnic University of Milan, 2015-2017) B.Sc. in Mechanical Engineering (Sharif University of Technology, 2011-2015) His work spans exoskeleton design, assistive robotics, and innovative interfaces like tongue-computer interfacing. Recent projects include the eMotivo digital health solution funded by Innovation Fund Denmark and intelligent tendon-driven exoskeletons for severe disabilities. Research outputs (33 total) emphasize adaptive robotics, motor impairment solutions, and wearable technologies. Collaborations span robotics, biomedical engineering, and clinical disciplines. Teaching includes courses in rehabilitation robotics, human-computer interaction, and digital systems for biomedical engineering. Scientific activities align with UN Sustainable Development Goals for quality education and reduced inequalities. Notable contributions include advancements in myoelectric interfaces and neurorehabilitation technologies.
Mikkel Lønborg Friis is a Clinical Associate Professor at Aalborg University, affiliated with the Department of Clinical Medicine under The Faculty of Medicine. He also serves as a senior consultant ( Ledende overlæge ) at Aalborg University Hospital, where he leads simulation-based training initiatives at NordSim – Centre for Skills Training and Simulation. His dual academic and clinical roles position him at the intersection of advanced surgical practice and innovative medical education. Clinical Associate Professor, Aalborg University Ledende overlæge (Chief Physician), Aalborg University Hospital Member, NordSim – Centre for Skills Training and Simulation His research interests focus on enhancing surgical and diagnostic competencies through technology-driven education. Key areas include simulation-based training, artificial intelligence in fetal and surgical ultrasound, robotic surgery assessment using deep learning, and curriculum development for cross-specialty ultrasound education. He actively contributes to improving clinical outcomes in pilonidal sinus disease and advancing AI integration in medical imaging. The recent publications highlight a strong trend toward interdisciplinary innovation, particularly in blending AI, simulation, and medical education. His work spans clinical surgery, educational methodology, and computational analysis of surgical performance. A significant portion of his research involves designing and evaluating training protocols, developing datasets for skill assessment, and exploring ethical and practical implications of AI in clinical settings. Mikkel Friis has not been publicly recognized with scientific awards in the provided text, but his leadership in simulation and curriculum design suggests significant institutional impact. He is involved in mentoring and advising through collaborative research projects, particularly in simulation and ultrasound education. While formal students are not listed, his role in study protocols and dataset creation implies supervision of junior researchers and medical trainees. He participates in funded or institutionally supported research activities related to medical education innovation and surgical technology. His involvement in press and media coverage further underscores his role as a thought leader in clinical simulation careers. Friis is deeply embedded in NordSim – Centre for Skills Training and Simulation, where he contributes to developing and implementing simulation-based assessment tools, particularly for abdominal ultrasound and surgical skills. His team collaborates across departments and institutions, focusing on creating standardized, scalable training models that integrate emerging technologies like AI and virtual reality.
Yvonne Dittrich is a Professor at the IT University of Copenhagen (ITU), affiliated with the Software Development Group. She holds an adjunct professorship at IIT Mandi, India, and has held roles at institutions in Sweden, Canada, and the U.S. Her research focuses on cooperative and human aspects of software engineering, including Continuous Software Engineering (CSE), use-oriented design, and end-user development (EUD). She has led projects like SAIA-Farm (sustainable irrigation via satellite analytics) and contributed to frameworks like 'Cooperative Method Development.' **Education**: PhD in Computer Science (Hamburg University, 1997), M.Sc. from TU Darmstadt. **Research Interests**: She pioneers methods bridging software engineering with human-centric practices, emphasizing sustainability and participatory design. Her work addresses challenges in global software development, agile methodologies, and software ecosystems. **Awards**: TAT-Förderpreis (1989), Best Paper Award (2018), Distinguished Reviewer recognition (2018). **Grants & Leadership**: Led projects funded by the Danish Innovation Fund, EU, and others. Served on editorial boards for IEEE Transactions on Software Engineering and Journal of Systems and Software. **Labs/Teams**: Collaborates with labs in Denmark, India, and Canada on interdisciplinary projects, including smart irrigation systems and fintech ESG data commons.
Maria Sinziiana Astefanoaei is an Assistant Professor at the IT University of Copenhagen , affiliated with the Data, Systems, and Robotics department. Her research focuses on spatiotemporal data analysis, urban computing, and graph algorithms using machine learning techniques. Research Interests: Spatial data analysis, Time series data processing, Large-scale visualizations, Machine learning, Human mobility modeling, and Embeddings. Projects: Principal Investigator for CCAI: Towards greener last-mile operations (2022-2023), contributing to cargo-bike logistics optimization, and Co-Investigator for the Pilot Hub project (2020-2022) funded by the Danish Agency for Research and Education. Publications: 2021 conference paper at CIKM '21 on spatiotemporal signal processing frameworks with neural machine learning models. Her work intersects computer science , urban logistics , and environmental sustainability , with applications in smart city technologies and multi-modal transportation systems.
Jette Ernst is an Associate Professor at Roskilde University's Department of Social Sciences and Business, specializing in organizational sociology and management studies. Her research focuses on the social and cultural dimensions of organizational change, particularly in healthcare settings, addressing topics like digitalization, robotics, professional identity, and power dynamics. She holds a PhD and multiple advanced degrees in business and communication studies. Education: PhD, Cand.Negot, Master of Science in Business (Language and Culture with English specialization). Research interests emphasize how automation and organizational restructuring impact healthcare work, employee responses, and professional boundaries. She employs qualitative methods including ethnography and discourse analysis, drawing on Bourdieu's practice theory and framing theory. Key themes include robotization in hospitals, merger processes, and performance management. Notable projects include leading the Independent Research Fund Denmark-funded 'Robot technology in hospitals' (2023-2026) and Helsefonden's 'Human and robot exchanges in Danish hospitals' (2020-2022). Recent publications explore temporal strategies in automation and labor dynamics in automated healthcare systems. Awards: Best Developmental Paper Award (2024), EGOS 'That's Interesting' Award (2017), Emerald/EFMD Doctoral Research Award (2018) Teaching: Courses in organization theory, leadership, HRM, and research methodology Supervision: Active mentor for academic theses at all levels Labs/Teams: Crossroads of Care and Social Reproduction (CARE) research group
Mohammad Hassan Khooban is an Associate Professor at the Department of Electrical and Computer Engineering, specializing in Electrical Energy Technology at Aarhus University . His research emphasizes advanced control strategies for power systems, renewable energy integration, and smart grid technology. While specific educational background details are not explicitly stated, his work demonstrates expertise in power electronics, control systems, and machine learning applications. His projects include pioneering initiatives like QuantumEcoCircuits (2024–2027) and Smart Synergy Mechanism (2023–2025), focusing on sustainable energy systems, electric vehicle charging dynamics, and resilient grid operations. His research interests span adaptive control methodologies, grid resilience under cyber threats, and the optimization of energy storage systems. He has contributed to peer-reviewed journals such as IET Renewable Power Generation and IEEE Transactions on Smart Grid , exploring topics ranging from PID controllers to fractional-order sliding mode control for unmanned aerial vehicles. No scientific awards are listed, but his work is supported through grants and collaborative projects. He is actively involved in lab initiatives related to power systems and renewable energy technologies.
Rasmus Leck Kæseler is an Assistant Professor in the Department of Health Science and Technology at Aalborg University, affiliated with The Faculty of Medicine. His work focuses on neurorehabilitation robotics, particularly developing assistive technologies for severely disabled individuals through innovative interfaces like tongue-computer systems and brain-computer interfaces. Key projects include the CRERoB initiative (Center for Rehabilitation Robotics - phase II) and the MultiRob project, which explore multimodal control systems for robotic arms. He collaborates extensively on interdisciplinary research involving mechanical engineering, electrical engineering, and clinical applications. Research interests span neural signal processing (e.g., SSVEP classification), adaptive exoskeleton design, and user-centered interface development for individuals with spinal cord injuries or ALS. Recent publications emphasize improving assistive robotic systems through semi-automation and studying long-term user adaptation patterns. Rasmus participates in academic activities such as the 'Forskningens Døgn' public engagement event and workshops on generative AI in education. He holds positions in the Center for Rehabilitation Robotics and contributes to conferences like the IEEE Engineering in Medicine and Biology Society.
