Amartya Sanyal is a Tenure Track Assistant Professor at the Department of Computer Science (DIKU), University of Copenhagen, specializing in Machine Learning. He also serves as an Adjunct Professor at the Indian Institute of Technology Kanpur (2023–2025). His research focuses on critical areas of AI safety, data privacy, and robust learning. University: University of Copenhagen Department: Department of Computer Science Academic Rank: Assistant Professor Adjunct Role: IIT Kanpur (2023–2025) His work addresses challenges like differential privacy , data poisoning attacks , machine unlearning , and robust mixture learning . Recent publications analyze privacy-preserving techniques for large language models, fairness in collective action algorithms, and certified data release mechanisms. Amartya has received the Villum Young Investigator Award (2025). His research outputs emphasize online learning , adversarial robustness , and privacy-utility tradeoffs through rigorous theoretical frameworks and practical implementations. Scientific Award: Villum Young Investigator Award His collaborations span institutions like IIT Kanpur and involve interdisciplinary projects with industry partners. Current activities include talks on privacy with correlated data and machine unlearning advancements.
Manex Aguirrezabal Zabaleta is an Associate Professor in the Department of Nordic Studies and Linguistics at the University of Copenhagen. He previously held positions as a Postdoc (2017-2019) and Assistant Professor at the same institution. His educational background includes: PhD in Natural Language Processing from the University of the Basque Country (UPV/EHU), conducted at the IXA NLP group. Master's degree in Natural Language Processing from UPV/EHU. Bachelor's degree in Computer Science (5 years) from UPV/EHU. Dr. Aguirrezabal's research focuses on the computational analysis of poetry , particularly stress patterns in English. He explores whether computers can effectively analyze poetic structures, a field with roots in the 1980s but revitalized by modern techniques. Additionally, he investigates language generation , computational morphology and phonology , and finite-state methods . His work bridges traditional linguistic inquiry with cutting-edge natural language processing. Recent publications (2023-2024) demonstrate a diverse engagement with computational linguistics, including poetry generation, multimodal corpus development, clickbait analysis, and fact-checking. His research often employs zero-shot learning and language models, reflecting current trends in AI-driven linguistic analysis. He has contributed to international collaborations such as ParlaMint (multilingual parliamentary corpora) and the GEHM Zoom corpus. While specific grant details are not provided, his active publication record indicates ongoing research support. Dr. Aguirrezabal maintains a strong connection to his Basque heritage, having pursued his early education in the Basque language.
Qiongxiu Li is a Tenure-Track Assistant Professor in the Cyber Security group at Aalborg University's Copenhagen campus, part of the Technical Faculty of IT and Design. Her research focuses on cybersecurity, distributed optimization, privacy/security, and federated learning. She has authored/co-authored 38 papers in top-tier venues including IEEE Transactions on Information Forensics and Security, ICLR, and EUSIPCO. Education: PhD in Privacy and Security from Aalborg University (2018-2021). Notable achievements include winning the EUSIPCO 2020 3MT Contest and co-delivering a tutorial on privacy-preserving distributed optimization at EUSIPCO 2024. She actively reviews for conferences like NeurIPS, ICLR, and journals such as TPAMI and TIFS. Research Themes: Privacy-preserving distributed algorithms, federated learning security, differential privacy, and adversarial machine learning. Recent Trends: Focus on securing AI systems (e.g., LLM vulnerabilities, federated clustering privacy), quantization for privacy, and theoretical bounds in decentralized learning. Awards: 2020 EUSIPCO 3MT Winner (outstanding finalist in EURASIP's annual doctoral research competition). Grants/Projects: Co-PI of the AI:SECURITY project (2025-2029) addressing AI security threats like phishing and malicious actors. Labs/Teams: Leads the Cyber Security group at Aalborg's Copenhagen campus, focusing on theoretical and applied research in secure distributed systems.
Tom Brughmans serves as Associate Professor in Classical Archaeology at Aarhus University's School of Culture and Society, where he pioneers the application of network science and computational modeling to archaeological questions. His work bridges theoretical archaeology with complexity science, focusing on long-term economic dynamics in the Roman Empire through quantitative analysis of material culture distribution. His research centers on developing methodological frameworks for archaeological network analysis, with specific expertise in Roman economic integration, amphorae trade networks, and agent-based simulation of ancient economies. Brughmans advocates for computational reproducibility and open-science practices, creating accessible tools that transform complex archaeological data into analyzable network structures while challenging traditional interpretations of Roman market systems. Brughmans' publication trajectory reveals three dominant trends: advancing theoretical foundations of archaeological network science through handbooks and methodological guides; empirical investigations into Roman economic complexity using big-data approaches to amphorae distributions; and development of public-facing simulation platforms that translate academic research into interactive experiences. His work consistently integrates computational techniques with archaeological evidence to model socio-economic processes across centuries. His scientific recognition includes prestigious competitive fellowships: Leverhulme Early Career Fellowship (2017-2019) for the MERCURY project Marie-Curie Individual Fellowship (2019-2020) for SIMREC Brughmans directs multiple major research initiatives including the Past Social Networks Project (an open repository for ancient network data), NEFLARA (a Marie-Curie project developing landscape archaeology frameworks), and MINERVA (focused on Roman economic functioning). He has secured substantial funding from the Leverhulme Trust, Marie-Curie Actions, and ERASMUS+ for projects advancing computational archaeology, while actively promoting collaborative research through platforms like FORVM that make economic modeling accessible to broader audiences. As a core member of Aarhus University's Centre for Urban Network Evolutions (UrbNet), he contributes to interdisciplinary investigations of ancient urban connectivity. His leadership extends to developing international research networks through the Oxford Handbook of Archaeological Network Research and creating open educational resources that democratize access to network analysis methodologies in archaeology.
Andrea Burattin is an Associate Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark. His work bridges formal methods and practical process analysis, focusing on process mining, business process management, and hybrid modeling techniques. He actively contributes to research in healthcare process optimization, streaming data analysis, and system verification through Petri nets and CCS transformations. UN Sustainable Development Goals: Poverty eradication, environmental protection, and prosperity for all (via process optimization) Active projects: Immersive Process Mining (2024-2027), Usability and Understandability of Hybrid Process Models (2018-2021) His research explores large language model integration with process mining, proposing frameworks like Tiramisù for multi-faceted process visualization and PN2CCS for formal model translation. Recent work emphasizes real-time monitoring, conformance checking, and IoT-driven process analytics. Key trends in his publications include: 1) Streaming process mining pipelines (2022-2025); 2) LLM-plan generation frameworks (2024); 3) Formal verification techniques (Petri nets, CCS); 4) Healthcare process modeling (2019-2023); 5) Behavioral pattern analysis in process compliance. Scientific Awards Best Demo Award (2022, 2016) Best Process Mining Dissertation Award (2014) Best Workshop Paper (EDBA and PODS4H, 2023) As advisor, he supervises PhD projects on process mining and hybrid modeling. His editorial roles include Information Systems reviewer (2024-2025) and past editor for Engineering Applications of AI (2022-2023). Collaborations span Denmark, Italy, and the Netherlands.
Yang Cheng is an Associate Professor at the Department of Materials and Production, Aalborg University, Denmark. He holds a PhD in Mechanical Engineering from the same institution (2011), focusing on manufacturing strategy and network dynamics. His research spans supply chain management, sustainability, and global operations, with a focus on integrating technology and environmental policies into manufacturing systems. He leads or participates in high-impact projects like MAASive (2024–2026) and the Sino-Danish Center Research Project (2011–present), addressing resilience in value networks and global operations innovation. Research Interests: Supply Chain Management & Integration Sustainability & Green Technologies Manufacturing Strategy & Networks Technology Policy & Digitalization Global Operations & Cross-Border Collaboration Recent Work Trends: Prof. Cheng's 2025 articles emphasize blockchain in sustainable supply chains, green technology investments under carbon policies, and digitalization's ethical implications. His 2024 research explores smart factories, EU battery regulations, and robotization in manufacturing. These studies blend quantitative models with case-based analysis to address real-world challenges. Awards: 2024 Emerald Literati Awards – Outstanding Reviewer Advising & Grants: As PI for multiple Global Operations Management PhD programs (2019–2025), he guides research on digital transformation and university-industry collaboration. His projects receive funding from Danish and international grants, focusing on innovation and resilience in manufacturing networks. Labs/Teams: Collaborates with the Center for Industrial Production at Aalborg University and engages in international partnerships through the Sino-Danish Center. Active editorial roles include Production Planning & Control and Journal of Manufacturing Technology Management .
Sneha Das is an Assistant Professor at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), specializing in Speech and Language Technology, Machine Learning, and Privacy-Preserving AI. Her research bridges technical innovation with applications in mental health and physiological signal analysis. Her work focuses on Speech Emotion Recognition , Distributed Speech Processing , and Explainable AI , with recent publications exploring model interpretability, speaker anonymization, and physiological data analysis for emotion detection. She actively supervises PhD students in projects involving AI for mental health and hydroacoustic modeling of fish behavior. Key Research Areas: Speech Emotion Recognition (SER) Privacy and Fairness in Speech Processing Transfer Learning with Physiological Time Series AI Applications in Health and Aquaculture Notable achievements include earning a DSc (Tech) degree for her thesis on robust distributed speech processing. She also contributes to educational activities, including teaching applied statistics and R programming to PhD students.
Anne Elisabeth Haxthausen is an Associate Professor at the Software Systems Engineering section within DTU Compute , Technical University of Denmark . Her work focuses on formal methods, railway control systems, and safety-critical software engineering. Founder and leader of the DTU Railway Verification Group Member of European Technical Working Group on Formal Methods in Railway Control Editorial board member for Springer Formal Aspects of Computing Journal Active in the Overture Language Board Her research emphasizes formal verification of railway interlocking systems, particularly through compositional approaches and automated tools. She has contributed to projects like RobustRailS, Overture, and RAISE, focusing on model-based development and verification. She serves as a tutor for bachelor students and contributes to the advisory committee for DTU's Computer Science and Engineering MSc program. Her recent publications explore challenges in verifying autonomous and AI-driven railway technologies.
Isabelle Augenstein is a Professor at the University of Copenhagen, Department of Computer Science (DIKU), where she heads the Copenhagen Natural Language Understanding (CopeNLU) research group and the Natural Language Processing section. She is also a co-lead of the Danish Pioneer Centre for Artificial Intelligence, Denmark's largest research center initiated by the Danish Ministry of Higher Education and Science. In October 2022, she became Denmark's youngest ever female full professor. Dr. Augenstein earned her undergraduate degree in Computational Linguistics and Psychology from Heidelberg University, followed by a Master's in Computational Linguistics. She completed her PhD in Computer Science at the University of Sheffield under the supervision of Dr. Diana Maynard and Prof. Fabio Ciravegna. In 2021, she earned a Habilitation at the University of Copenhagen in Explainable Fact-checking. Professor Augenstein's primary research focuses on fair and accountable Natural Language Processing, with particular emphasis on explainability, factuality, and bias detection. Her work spans multiple subfields including automated fact-checking, stance detection, gender bias analysis, and cultural bias in language models. She has pioneered research in explainable fact-checking, developing methods that not only predict claim veracity but also provide meaningful explanations of the decision-making process. Her research group has produced numerous influential papers on measuring model fragility, quantifying gender biases, and developing robust fact-checking systems that account for distribution shifts. Her significant contributions have been recognized with several prestigious awards: ERC Starting Grant on 'Explainable and Robust Automatic Fact Checking' DFF Sapere Aude Research Leader fellowship on 'Learning to Explain Attitudes on Social Media' Karen Spärck Jones Award from the British Computing Society and Bloomberg Hartmann Diploma Prize from the Hartmann Foundation Member of the Royal Danish Academy of Sciences and Letters since 2024 Professor Augenstein has secured significant research funding including her ERC Starting Grant supporting five years of blue-sky research. She actively mentors PhD students and postdoctoral researchers through her 'ExplainYourself' project. She served as President of SIGDAT (which organizes the EMNLP conference series), having previously held leadership roles as Vice President and Vice President-Elect. She is a co-founder of Widening NLP (WiNLP), an initiative to increase diversity in the NLP community, and maintains the BIG Directory of underrepresented groups in NLP. She leads the Copenhagen Natural Language Understanding (CopeNLU) research group, which relocated to the historic Østervold Observatory in Copenhagen's Botanical Gardens in 2023. The group focuses on developing methods for explainable and robust natural language understanding, with applications in fact-checking, bias detection, and social media analysis. Professor Augenstein also co-leads the Speech and Language collaboratory at the Pioneer Centre for Artificial Intelligence, where her team investigates how language models can better serve diverse populations while maintaining accountability and transparency.
Hanne Leth Andersen is the Rector of Roskilde University and a Professor of University Pedagogy. She holds a PhD and has extensive experience in higher education leadership, including roles as director of the Centre for Teaching Development at Aarhus University and director of the Learning Lab at Copenhagen Business School. Her research focuses on foreign language didactics, university pedagogy, educational quality, and innovative teaching methods. Education: PhD in University Pedagogy. Previous academic positions include Professor of University Pedagogy at Aarhus University and Copenhagen Business School. Research interests emphasize exam form innovations, teaching development, language learning methodologies, and the role of foreign languages in education. She advocates for educational quality and pedagogical strategies to enhance student learning environments. Key awards include Chevalier de l'Ordre de la Légion d'Honneur (France), Commandant of the Ordre des Palmes Académiques (France), and Dannebrog Order (Denmark). Notable contributions include developing teacher training programs and advising on educational policies in Norway, Sweden, Finland, and France. Advising and grants: Pioneered collegial supervision methods for teacher competence development at Aarhus University, contributed to Norway’s university quality systems evaluations, and advised on French bachelor’s program reforms. Engaged in strategic board roles within research, education, and cultural institutions. Labs/teams: Active in Roskilde University’s Rectorate leadership, previously directed Learning Lab at CBS, and collaborates internationally on educational strategy initiatives.
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.
Johannes Bjerva is a Full Professor at Aalborg University's Department of Computer Science (Campus Copenhagen), leading the Copenhagen branch and conducting interdisciplinary NLP research integrating linguistic typology. His work focuses on low-resource languages, language model security, and societal AI impact. PhD (University of Groningen, 2017): Thesis on multitask/multilingual lexical modeling M.A. & B.A. in Computational Linguistics (Stockholm University) Research interests span linguistically-informed NLP , language model security , and low-resource language technology . Current projects include the DFF Sapere Aude grant (2025) for language model detection security and the LM2-SEC project (2025–2030). His 2024 ACL paper on embedding inversion security and 2024 EMNLP paper on typological diversity exemplify recent work. Scientific awards include: 2021: Teacher of the Year (AAU Computer Science) 2019: Google Cloud research credits 2022: Carlsberg Semper Ardens (5M DKK) 2024: Novo Nordisk Data Science grant (~10M DKK) Supervision includes 8 PhD students across projects like CreoleVal and HiFi-KPI . He serves on the Industrial Researcher Committee at Innovation Fund Denmark and is a member of Det Unge Akademi (2023–2028).
Arijit Khan is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark. He leads the Data Engineering, Science and Systems group and is affiliated with the Technical Faculty of IT and Design. His research focuses on Graph Neural Networks , Blockchain , Data Management , and AI interpretability . He is the Principal Investigator (PI) of a major project on Data Management, Fundamental Algorithms, and Machine Learning for Emerging Problems in Large Networks (2022–2027). Research Interests : Graph Data Management & Machine Learning Blockchain Transaction Analysis Large Language Model + Knowledge Graph Synergies Healthcare AI (e.g., ICU glucose prediction) Explainable AI for Graph Neural Networks Research Trends : His publications emphasize neuro-symbolic systems , uncertain graph analysis , and AI-driven blockchain insights . Recent work bridges large language models with knowledge graphs and explores GPU performance optimization via shader code analysis. Awards & Grants : No explicit awards listed, but his active research grants include a 5-year project on large network analysis with interdisciplinary applications in life and health sciences. Funding emphasizes algorithmic innovation and data science integration. Labs/Teams : Head of the Data Engineering, Science and Systems research group, focusing on AI for societal impact ('AI for the People') and scalable graph data systems. Collaborations span blockchain analytics, healthcare informatics, and GPU architecture design.
Hjalmar Alexander Bang Carlsen is an Associate Professor at the Copenhagen Center for Social Data Science (SODAS), Faculty of Social Sciences, University of Copenhagen. He specializes in mixed digital methods and is deeply engaged in research and teaching related to digital data analysis, particularly in the context of political and civic participation on social media. He is a key contributor to the Social Data Science master's degree program. University: University of Copenhagen School: Faculty of Social Sciences Department: Copenhagen Center for Social Data Science (SODAS) Position: Associate Professor in Mixed Digital Methods Email: hc@soc.ku.dk ORCID: https://orcid.org/0000-0002-2638-0932 His research centers on mixed methods strategies for digital data, with a substantive focus on civic and political engagement via social media. Key areas include informal volunteering during crises, gender inequality in online political participation, and the use of large language models (LLMs) for qualitative interviewing. He leads three major projects: SoMeVolunteer (on crisis volunteering), public participation on Facebook, and AInterviewer (an LLM-based interviewing tool). The recent publications reflect a strong trend in digital sociology, crisis response, and methodological innovation. His work combines large-scale social media data with surveys, interviews, and textual analysis, emphasizing ethical and epistemological considerations in computational social science. Topics span from refugee solidarity and pandemic volunteering to framing contests among climate NGOs and gender disparities in digital political engagement. While no formal scientific awards are listed in the provided text, Carlsen is actively funded by the Velux Foundation and UCPH Data+, and his work is widely disseminated through media and academic outlets. He collaborates closely with researchers like Jonas Toubøl and Snorre Ralund, and his projects often involve interdisciplinary teams. He has secured seed funding for innovative methodological development, indicating strong grant-writing capacity. He is involved in public engagement, with multiple media appearances discussing Danish civic response during the pandemic and refugee crises. His research outputs include journal articles, book chapters, and a co-authored textbook on mixed methods. He also participates in workshops and public lectures, contributing to both academic and public discourse on digital society. Carlsen is affiliated with SODAS and the Social Sciences Datalab, indicating active involvement in data-intensive research infrastructure. His work on AInterviewer suggests leadership in emerging AI-driven qualitative methods, positioning him at the forefront of digital social research innovation.
Irena Vodenska is Professor of Finance and Director of Finance Programs at Boston University’s Metropolitan College, Department of Administrative Sciences. She holds a PhD in statistical finance and an MA in economics from Boston University, an MBA from Vanderbilt University, and a BS in computer information systems from the University of Belgrade. She is also a Chartered Financial Analyst (CFA) charter holder. Her research is at the intersection of finance, complexity science, and artificial intelligence, focusing on systemic risk modeling, ESG investments, and financial network dynamics. She has led major interdisciplinary research projects funded by the National Science Foundation, the European Commission, and the U.S. Army Research Office. PhD, Statistical Finance – Boston University MA, Economics – Boston University MBA – Owen Graduate School of Management, Vanderbilt University BS, Computer Information Systems – University of Belgrade Dr. Vodenska’s research interests include network theory in finance, systemic risk propagation, AI-powered ESG analysis, cryptocurrency price forecasting, and financial regulation. She employs big data, machine learning, and natural language processing to analyze financial news, market dynamics, and corporate sustainability. Her work investigates how climate disinformation spreads via social networks and influences public policy and governance. The recent articles highlight a consistent focus on modeling financial and economic systems using network science and AI. Trends include systemic stress testing, sentiment analysis in financial markets, cascading failures, and the interplay between macroeconomic indicators and financial networks. Her work spans econophysics, behavioral finance, public health economics, and ethical AI in fintech. National Science Foundation (NSF) research grant (2023) NSF EAGER Award (2014–2015) European Commission FET Open Grant (2012–2014) U.S. Army Research Office (ARO) Grant (2020–2021) MEXT Post-K Computer Grant, Japan (2016–2019) Alexander Hamilton Fulbright Fellowship (1994) Owen Graduate School Fellowship (1995–1996) Dr. Vodenska teaches core finance courses such as Investment Analysis and Portfolio Management, Derivatives Securities, and Financial Regulation and Ethics. She co-developed the MET AD 678 course with Professor Tamar Frankel from BU Law, emphasizing real-world case studies and ethical decision-making. Her research grants have supported innovative work in systemic risk modeling, AI for ESG, and financial network stability. She is actively involved in mentoring, conference organization, and editorial roles in leading journals. She is a key organizer of the International School and Conference on Network Science (NetSci) and the Big Data in Economics, Science, and Technology (BEST) Conference. Her lab and research team focus on complexity in financial systems, bringing together economists, physicists, computer scientists, and data analysts to study global financial stability and sustainability.
