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Ole Winther is Professor in High dimensional biological data analysis/Machine learning at the Department of Biology, University of Copenhagen and Professor in Data science and complexity at DTU Compute, Technical University of Denmark. He serves as CRO and co-founder of raffle.ai, CTO and co-founder of FindZebra, Head of ELLIS Unit Copenhagen, and co-PI of the Machine Learning for Life Science Center. His research spans Bioinformatics , Machine Learning , and AI for Science , focusing on applying deep learning to biological sequence analysis, latent variable models, and medical NLP. Winther's work develops predictive and generative models for bioinformatics, with significant contributions to protein localization tools (SignalP, DeepLoc, DeepTMHMM), single-cell genomics, and novel deep learning architectures like variational autoencoders and diffusion models. Analysis of Winther's recent publications (2023-2025) reveals a strong trend toward integrating protein language models with traditional bioinformatics approaches and applying diffusion models to scientific problems. His work bridges theoretical machine learning advancements with practical applications in biology and medicine, particularly in protein sequence analysis, medical search engines, and scientific simulation acceleration. Winther currently supervises a diverse research group including Panagiotis Antoniadis, Rachael M. DeVries, Jun Wang, Beatrix M. G. Nielsen, Felix G. Teufel, Irene R. Rodriguez, Anders Christensen, and Christopher Heje Grønbech. His former students have established successful careers at institutions including Google, Apple, and various startups, with notable alumni like Casper Sønderby (Google Brain) and Søren Sønderby (Apple). He leads significant research initiatives including the ELLIS Unit Copenhagen and the Machine Learning for Life Science Center, while maintaining active industry partnerships through his co-founded companies raffle.ai (enterprise search using NLP) and FindZebra (search engine for rare diseases). His teaching includes Deep Learning courses at both DTU (02456) and University of Copenhagen (NDAK24002U).

• Bioinformatics
• Machine Learning
• Deep Learning
• +5 more

Olga Saukh is an Associate Professor at the Institute of Technical Informatics, Graz University of Technology (TU Graz), and a Faculty member at the Complexity Science Hub Vienna (CSH). She leads the Embedded Learning and Sensing Systems research group, which operates across both institutions, focusing on the design and deployment of efficient AI-based systems on edge and mobile platforms. Her work bridges deep learning and embedded systems, with applications in environmental monitoring, precision agriculture, and digital health. Ph.D. in Computer Science, University of Bonn (2009) Habilitation in Embedded Systems, TU Graz (2020) Postdoctoral Training, ETH Zurich (2010–2016) B.Sc. in Applied Mathematics, Taras Shevchenko National University of Kyiv (2002) M.Sc. in Applied Computer Science, University of Freiburg (2004) Her research centers on efficient machine learning, particularly model optimization, neural network pruning, and contrastive learning for resource-constrained devices. She is deeply engaged in solving real-world challenges in IoT, sensor networks, and cyber-physical systems. Her work emphasizes data privacy, sustainability, and practical deployment of AI at the edge. The 15 most recent publications highlight a strong trend in efficient deep learning, including model compression, pruning, and transfer learning, applied to diverse domains such as environmental sensing (air quality, pollution tracking), digital agriculture (cattle farming), and embedded AI (sensor calibration, on-demand sensing). Her work frequently appears in top-tier venues like NeurIPS, ICLR, and IEEE/ACM IPSN, reflecting her leadership at the intersection of machine learning and embedded systems. Scientific awards include: CONET Ph.D. Academic Award (2010) Multiple Best Paper Awards at IEEE PerCom, ACM/IEEE IPSN, IEEE ICPADS, IEEE SECON, and UrbCom Spotlight and Oral presentations at ICML and CoLLAs workshops Ph.D. scholarship from IPVS, University of Stuttgart (2004–2005) Prizes in Ukrainian national mathematics competitions (1996–1998) Olga Saukh actively serves on program committees of leading international conferences in machine learning and embedded systems. She has advised multiple students and leads a collaborative research group spanning TU Graz and CSH Vienna. Her group develops practical AI systems for real-world deployment, with a focus on sustainability and privacy. She co-organizes the public EfficientML reading group and has secured recognition through numerous grants and awards. Her future work continues to explore the theoretical and practical challenges of deploying efficient, trustworthy AI in mobile and embedded environments. Her research group, Embedded Learning and Sensing Systems, operates jointly between TU Graz and CSH Vienna, fostering interdisciplinary collaboration across institutions. The team develops AI solutions for edge computing, sensor networks, and cyber-physical systems, with a strong emphasis on environmental sustainability and data privacy. Members work on joint challenges using advanced collaboration tools, reflecting the distributed nature of modern academic research.

• Efficient Machine Learning
• Deep Learning
• Embedded Systems
• +12 more

Kevin Crowston is a Distinguished Professor of Information Science at Syracuse University's School of Information Studies (iSchool), where he examines how information technology enables new organizational forms through empirical studies, theoretical modeling, and system design. His work focuses on coordination-intensive processes in virtual settings, with significant contributions to citizen science, data science teamwork, and journalism transformation. Education A.B. in Applied Mathematics (Computer Science), Harvard University, 1984 Ph.D. in Information Technologies, MIT Sloan School of Management, 1991 Research Focus : Crowston investigates coordination mechanisms in human-AI collaboration, particularly through projects like Gravity Spy (combining citizen scientists with machine learning for gravitational wave analysis) and journalism innovation (e.g., ReelFramer for AI-assisted news-to-video translation). His framework addresses how intelligent systems reshape work design, knowledge production, and team dynamics in scientific and media contexts. Publication Trends : Recent articles (2024-2025) reveal three dominant threads: (1) Human-AI co-creation in journalism (deskilling/upskilling dynamics, creative tool adoption), (2) Citizen science evolution with AI (co-learning systems, lexical entrainment), and (3) Socio-technical governance of intelligent machines (control-accountability alignment, project archetypes). These reflect his central inquiry into how technology reconfigures work structures. Scientific Recognition ACM Distinguished Speaker Research Leadership : Crowston currently directs two major NSF initiatives: (1) HCC grant 21-06865 on intelligent support for non-expert information navigation, and (2) FW-HTF grant 21-29047 exploring human-technology collaboration in journalism. He spearheaded a Research Coordination Network establishing socio-technical frameworks for work in the age of intelligent machines, culminating in a special issue of Information, Technology & People . Collaborative Infrastructure : He co-leads the Gravity Spy citizen science ecosystem (integrating LIGO physicists, machine learning systems, and volunteers) and serves as co-editor-in-chief of Information, Technology and People , previously editing ACM Transactions on Social Computing . His MIDST platform research advances stigmergic coordination for data science teams.

• Information Systems
• Human-Computer Interaction
• Virtual Organizations
• +5 more

Desmond Elliott is an Associate Professor in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen (UCPH). His research focuses on multimodal and multilingual models with specific emphasis on vision-language integration and tokenization-free NLP approaches. He teaches Bachelor and Master's level courses including Advanced Topics in Natural Language Processing (since 2019), Grundlæggende Data Science (since 2023), and previously Data Science (2021-2023). His research interests center on building and understanding multimodal and multilingual models , particularly exploring vision and language interactions through billion-parameter systems. Current work investigates cultural representation disparities in vision-language models, parameter-efficient captioning, and multimodal distributional semantics across diverse domains including food culture and medical imaging. His methodology emphasizes real-world applicability in non-English contexts and ethical considerations in multimodal systems. Elliott's recent publications (2025) demonstrate leadership in multimodal NLP, with significant contributions to vision-language pretraining, multilingual evaluation frameworks, and clinical NLP applications. His work spans theoretical advancements in model architectures and practical implementations addressing challenges in low-resource languages and domain adaptation. Best Long Paper Award at EMNLP 2021 Best Poster Award at COLING 2019 As an active educator, Elliott contributes to courses on Fair and Transparent Machine Learning and previously taught Information Retrieval. His research collaborations span international institutions with particular focus on European and non-English language contexts, reflecting UCPH's recognition as Europe's #1 institution for HCI research over the past decade.

• Multimodal Learning
• Multilingual NLP
• Vision-Language Models
• +4 more

Pedro Monteiro is an Assistant Professor (tenure track) in the Department of Organization at Copenhagen Business School. His research focuses on organizational expertise development, bureaucratic systems, and qualitative methods, with fieldwork in engineering firms and the public sector. Academy of Management Annals Best Article Award Louis Pondy Award recipient Max Boisot Award winner Poets and Quants Top 50 Undergraduate Business Professors 2024 Monteiro's work spans organizational theory, cybersecurity coordination, and migrant expertise challenges. He serves as Communications Editor for Administrative Science Quarterly and contributes to qualitative research communities through co-founding the Talking About Organizations Podcast and Ethnography Atelier. His research projects include a Gothenburg University collaboration funded by Otto Mønsteds Fond (2023). Current teaching includes Organizational Behavior at BSc level and Strategy, Organization & Leadership at MSc level.

• Organization Theory
• Bureaucracy and Organizational Forms
• Collaboration, Coordination, and Cooperation in Organizations
• +7 more

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.

• Audio Signal Processing
• Artificial Intelligence
• Speech Enhancement
• +12 more

Remus Teodorescu is a Professor at AAU Energy , Aalborg University , specializing in Power Electronics System Integration and Materials . His work bridges Lithium-Ion Batteries , Modular Multilevel Converters , and Smart Battery Systems . Education : Not explicitly mentioned in the text. Research Interests focus on Battery Management Systems , AI-Driven Energy Optimization , and Power Electronics for renewable energy integration. Key projects include Digital Twin for Lithium-Ion Batteries and BMS-DC for Data Centers . Recent Publications (2025) emphasize Finite Set MPC , Gradient Descent Optimization , and AI in Battery Parameter Estimation . His 2024 work explores Physics-Informed Neural Networks and Fault-Tolerant Converters . Scientific Awards : Villum Foundation Grant (313 million kroner, 2021) Named world's best in electrical engineering (2023) Advising includes supervising PhD projects on AI-Accelerated Battery Twins and Data-Driven SOH Estimation . Collaborations span Energy Cluster Denmark and Villum Fonden .

• Battery (Electrochemical Energy Engineering)
• Modular Multilevel Converter
• Lithium-Ion Batteries
• +6 more

Francisco Camara Pereira is a Professor and Head of Section at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on Intelligent Transportation Systems, Machine Learning, and Data-Driven Decision-Making in transportation contexts. He actively contributes to advancing transportation science through interdisciplinary approaches combining simulation, optimization, and AI techniques. His work addresses challenges in public transport analysis, charging infrastructure planning, and multimodal demand prediction. Recent projects include developing graph-based optimization methods for electric vehicle networks and causal discovery frameworks for transportation systems. He supervises multiple PhD students in areas like federated learning for cyclist safety, causal graph neural networks, and socially aware AI models. Key contributions include publications on smart card data analysis for travel surveys, stochastic infrastructure expansion models, and transfer learning for bike-share systems. His research aligns with UN Sustainable Development Goals related to sustainable cities and innovation. Dr. Pereira collaborates internationally on transportation policy and infrastructure projects. His lab focuses on translating theoretical advancements into practical solutions for urban mobility challenges.

• Intelligent Transportation Systems
• Machine Learning
• Active Learning
• +3 more

Bent Flyvbjerg is a Professor at the IT University of Copenhagen , affiliated with the Software Engineering Research Center for Public IT and the Danish Institute for IT Program Management . His research focuses on megaprojects, cost overruns, behavioral biases, and digitalization’s intersection with climate action. Research Interests : Megaproject governance, cost-benefit analysis, behavioral economics, climate mitigation through digitalization. Projects : Leads the Villum Kann Rasmussen Professorship (2022–2027) on IT project management. Media Impact : Frequently cited in press/media (203 mentions), including Harvard Business Review and Project Management Journal.

• Megaprojects
• Cost Overrun
• Project Management
• +5 more

Birger Lindberg Møller is a Professor in Plant Biochemistry at the Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen. He also serves as Director of the Carlsberg Laboratory (since 2014) and Head of the VILLUM Research Center for Plant Plasticity (since 2013). His research spans plant biochemistry, synthetic biology, and natural products chemistry with applications in sustainable production systems. His educational background includes: M.Sc. (1972) Ph.D. (1975) D.Sc. (1984) D.Sc. h.c. (honoris causa) Professor Møller's research focuses on bio-active natural products , particularly the biosynthesis, transport, storage and degradation of cyanogenic glucosides . His work elucidates how plants communicate with their environment and defend themselves against herbivores, pests and abiotic stresses. A promising application of his lab's research involves engineering microalgae, mosses and plant cells into production units for high value natural products . His engagement in synthetic biology represents a logical extension of his plant research activities, with work targeted towards forming new combinations of membrane proteins for harvesting sunlight energy to synthesize valuable natural products or degrade toxic compounds. Analysis of Professor Møller's recent publications reveals a strong focus on plant specialized metabolism, particularly cyanogenic glucosides and related compounds. His work spans genomics, transcriptomics, metabolomics, and synthetic biology approaches to understand plant defense mechanisms and engineer plants for improved traits. Key themes include sorghum and cassava metabolism, diterpenoid biosynthesis, and the application of synthetic biology for sustainable production of high-value compounds through photosynthetic systems. His notable scientific awards include: Villum Kann Rasmussen Research Prize (2007) - Denmark's largest research award (2.5 mil DKK or 350,000 €) The Academy of Future Research's "The Future Prize" (2004) Societas Physiologia Plantarum Scandinavica "Popularization Prize" (2003) Knight of the Dannebrog (1995) Carlsberg Research Prize Award (1994) Hans Gram Medal from the Royal Danish Academy of Sciences and Letters (1985) Appointed Honorary Doctor at the University of Umeå, Sweden (2009) Professor Møller has served as the main advisor for 43 PhD students (with 8 ongoing in Denmark and 3 in Australia) and numerous BSc and MSc students. He has secured significant research funding throughout his career, including an ERC Advanced Grant (2.5 million €), Villum Foundation grants, and participation in major research centers with multi-million euro budgets. His current funding includes projects related to synthetic biology, plant plasticity, and cyanogenic glucoside metabolism. He leads the Plant Biochemistry Laboratory and is deeply involved with the Center for Synthetic Biology at the University of Copenhagen. His laboratory focuses on understanding plant specialized metabolism and applying this knowledge through synthetic biology approaches to engineer plants and microorganisms for sustainable production of high-value compounds. His team collaborates extensively with international partners across Europe, Australia, and beyond, and he actively promotes cross-disciplinary collaboration to address complex global challenges in energy, sustainability, and medicine.

• Plant Biochemistry
• Natural Products Chemistry
• Cyanogenic Glucosides
• +5 more

Alfred Taudes is a Full Professor at the Department of Information Systems and Operations, Institute for Production Management, Vienna University of Economics and Business (WU Vienna). He holds a doctoral degree and a Habilitation from WU Vienna in Business Administration and Management Information Systems, and a Magister degree from Vienna University. He has held assistant professorships at WU and visiting professorships at Augsburg, Münster, Essen, and Tsukuba University, Japan. He joined WU permanently in 1993 and served as head of the Department of Information Systems and Operations from 2010 to 2016. His research spans Operations and Supply Chain Management , Marketing Engineering , Knowledge Management , and the impact of Big Data and Blockchain on production systems. Using Complexity Science and Cryptoeconomics , he investigates digital production, integrated value chains, and market designs. He teaches undergraduate and graduate courses including Operations Strategy, Data Science, and IT seminars in WU’s International Supply Chain Master program, and has also taught at Japanese universities. His recent publications focus on blockchain privacy (e.g., CoinJoin analysis), CBDCs, MiCAR regulation, decentralized federated learning, and digital custody, reflecting a strong trend toward cryptoeconomics and blockchain-based systems in operations and finance. These works appear in top journals and conferences in information systems, security, and operations research. Cooperation Officer of the Year 2013/14 Distinguished Paper Award WI 2009 VHB Best Paper Award Nomination VHB Best Paper Award Dr. Wolfgang Houska - Recognition Award Alfred Taudes has coordinated major research projects such as the WWTF-project “Integrated Demand and Supply Chain Management” and the Special Research Area Adaptive Models in Economics and Management Science. He currently leads the research group on Cryptoeconomics at WU and chairs the scientific board of the Austrian Internet Offensive . His leadership extends to project management in initiatives like the Austrian Blockchain Center and research on decentralized finance and digital assets. He is actively involved in academic service, including organizing conferences like DEXA 2022, serving on editorial boards, and advising on research policy. His lab and research group focus on blockchain applications, digital transformation in operations, and the societal implications of big data.

• Operations and Supply Chain Management
• Cryptoeconomics
• Blockchain Technology
• +7 more

Ole Winther is a Professor at the Department of Biology, University of Copenhagen, specializing in Computational and RNA Biology. He also holds a joint appointment as Professor at DTU Compute, Technical University of Denmark. His research bridges machine learning, bioinformatics, and natural language processing with applications in biological sequence analysis, transcriptomics, and health informatics. Education: 1998: PhD in Physics, University of Copenhagen 1994: Master of Science in Physics, University of Copenhagen Winther's research focuses on developing advanced machine learning methodologies for biological applications. He has pioneered protein language models for sequence analysis (DeepLoc, SignalP, DeepTMHMM), interpretable deep learning for RNA subcellular localization, and benchmarking frameworks for DNA language models. His work spans latent variable models, variational inference, diffusion models, and novel architectures for deep generative modeling, with increasing emphasis on practical healthcare applications including rare disease diagnosis through findzebra.com and medical question answering with large language models. Scientific Recognition: ELLIS Fellow (2021) Head of ELLIS Copenhagen Unit H-index of 61 (Google Scholar, May 2023) 19,700+ citations (Google Scholar, May 2023) Winther has supervised 25+ PhD students to completion with 7 currently in progress, along with over 100 master's projects. He frequently serves as PhD opponent and committee chairman across European institutions. His research is supported by substantial funding including multiple Novo Nordisk Foundation grants totaling over 60 million DKK for the Center for Basic Machine Learning Research in Life Science and CAZAI projects, plus significant funding from the Danish Independent Research Fund. He leads an active research group developing cutting-edge machine learning approaches for bioinformatics and NLP challenges. Winther co-founded two spin-out companies: findzebra.com (2014, 2018), a search engine for rare diseases, and raffle.ai, an NLP startup for enterprise search. He initiated DTU's popular BSc in AI and Data program and teaches the highly enrolled MSc course in Deep Learning (450+ students) and PhD course in Bayesian Data Analysis.

• Machine learning methods development
• Bioinformatics
• Biological sequence analysis
• +8 more

Abdulkadir Çelikkanat is an Assistant Professor in the Department of Computer Science at Aalborg University, Denmark, where he is part of the DKW (Data Science and Knowledge) research group. His research focuses on genome representation learning, graph representation learning, and machine learning applications in bioinformatics and network science. Research Interests: His work lies at the intersection of artificial intelligence and biological data analysis, with a strong emphasis on scalable methods for genome and metagenome representation using k-mer profiles, as well as modeling dynamic and complex networks. He develops novel machine learning models to capture the structure and evolution of graphs over time. Recent Research Trends: His recent publications, appearing in top-tier venues like NeurIPS, AAAI, and AISTATS, demonstrate a consistent focus on improving scalability and effectiveness in representation learning. Key themes include revisiting traditional k-mer methods for modern deep learning, modeling citation dynamics, and developing continuous-time node embedding techniques. His work bridges theoretical advances with practical applications in genomics and network analysis. Scientific Awards: Best Paper Award, TGL Workshop @ NeurIPS 2023 Top Reviewer, LoG 2024 Conference Advising and Grants: While current advisees are not listed, he is actively leading research projects as evidenced by his recent publications and project organization (e.g., Nordic ProbAI summer school). His work is supported through institutional affiliations and likely competitive research funding, given the high-impact venues of his publications. Labs and Teams: He is affiliated with the DKW group at Aalborg University. Previously, he was part of the Inria OPIS team and the Centre for Visual Computing during his Ph.D., and worked in the Section for Cognitive Systems at DTU Compute as a postdoctoral researcher.

• Genome Representation Learning
• Graph Representation Learning
• Machine Learning
• +5 more

Rasmus Koss Hartmann serves as an Associate Professor in the Department of Management, Society and Communication at Copenhagen Business School, Denmark. His scholarly work critically examines innovation and entrepreneurship through interdisciplinary lenses, contributing to United Nations Sustainable Development Goals related to responsible consumption, reduced inequalities, and sustainable economic growth. His research interests center on critical innovation management and entrepreneurship studies , with particular focus on deconstructing mainstream narratives around entrepreneurial culture. Key themes include organizational reflexivity in university incubators, the political economy of 'tech theater' in Silicon Valley, and Veblenian critiques of contemporary entrepreneurialism. Hartmann's work consistently challenges assumptions about acceleration in startup culture while exploring alternatives like slow management and untrepreneurial economic models. Analysis of his 39 publications reveals evolving scholarly trajectories: early work examined user innovation in government settings (2013-2018), followed by critical CMS contributions (2014-2016), and recent intense focus (2021-2025) on deconstructing entrepreneurial mythology through books like Innovation Management: Foundations and Futures (2025) and articles analyzing 'wantrepreneurs' and the 'speed ethic'. Hartmann has supervised three research projects including one PhD thesis, demonstrating commitment to mentoring next-generation critical scholars. His work shows significant interdisciplinary reach with publications spanning management journals ( Scandinavian Journal of Management ), innovation outlets ( Innovation: Organization & Management ), and engineering management forums ( IEEE Engineering Management Review ).

• Innovation Management
• Critical Entrepreneurship Studies
• Organizational Reflexivity
• +3 more

Christian Koch is a Professor and Head of Section at the University of Southern Denmark (SDU) Civil and Architectural Engineering, Department of Technology and Innovation, where he leads research on construction industry dynamics, climate change mitigation, and digital transformation. His work bridges institutional theory with practical challenges in sustainable development and organizational innovation. SDU Climate Cluster EU SAND Project Participant Creative Construction Conference Chair Research interests include circular economy implementation, blockchain in construction logistics, lean construction methodologies, and AI applications for safety analysis. His studies focus on institutional entrepreneurship, interorganizational networks, and policy impacts on construction practices, particularly in Denmark and Sweden. Recent article trends analyze machine learning for accident report analysis, blockchain-enabled resource marketization, and climate-resilient infrastructure. Notable awards include the Taylor and Francis Best Theoretical Paper (2025), SCC Fast Track Award (2024), and CME Best Paper on Societal Challenges (2022). Scientific awards include: Taylor and Francis Best Theoretical Paper (2025) SCC Fast Track November 2024 CME Best Paper Transformative Impact (2022) Best Paper Creative Construction Conference (2025) He actively participates in public discourse through media engagements on construction safety, climate adaptation, and sustainable sand extraction for green transitions.

• Construction Industry
• Climate Change
• Engineering
• +7 more