Find Academic Supervisors

• Design Science Research
• Conversational Agents
• Generative AI
• +3 more

Daniel Bierschwale is a Researcher at the Institute for Business Administration and Information Systems at the University of Hildesheim, specializing in the Department of Information Systems and Enterprise Modeling within the Faculty of Mathematics, Natural Sciences, Economics and Computer Science. He has been working as a Scientific Employee since April 2023, following the completion of his Master's degree in Business Informatics at the University of Hildesheim (2020-2023) and a Bachelor's degree at Hochschule Hannover (2016-2020). His research focuses on Design Science Research methodology, conversational agents, generative AI applications, and the concept of unlearning in organizational contexts. Bierschwale's work bridges theoretical frameworks with practical applications, particularly in developing tools to support novice researchers and enhancing user acceptance of AI technologies through anthropomorphic design elements. His research has significant implications for knowledge management, digital transformation in public administration, and improving human-AI interaction across various domains including healthcare, education, and public services. Bierschwale's publication record demonstrates a strong trajectory in high-impact conferences including HCI International, ECIS, and WI, with a particular emphasis on the intersection of design science and generative AI. His work often explores how to make complex technologies more accessible to non-expert users and how to support organizational adaptation to new digital paradigms through structured unlearning processes. Best Research Paper Award at RVI23 (2023) for work on unlearning competencies in public administration Bierschwale actively supervises student theses on topics related to generative AI applications in business processes, taxonomy development for AI implementation in organizations, and sustainable mobility solutions. His collaborative approach is evident in his frequent co-authorship with colleagues from the University of Hildesheim and other institutions, reflecting the interdisciplinary nature of his research at the intersection of information systems, human-computer interaction, and organizational behavior.

Özlem Yaşar UğurluView profile

Associate Professor

• Strategic Management
• Organizational Behavior
• Knowledge Management
• +4 more

Associate Professor Özlem Yaşar Uğurlu is affiliated with Gaziantep University , serving in the Faculty of Economics and Administration under the Department of Business Administration . Her academic journey includes a Doctorate in Business Administration and Organization from Afyon Kocatepe University (2011), a Master's in Business Administration from Gaziantep University (2005), and a Licence in Public Administration from Hacettepe University (2002). She has held administrative roles such as Department Head (2016-2017) and Deputy Department Head (2011-2014). Education PhD: Business Administration and Organization, Afyon Kocatepe University (2011) MSc: Business Administration, Gaziantep University (2005) BSc: Public Administration, Hacettepe University (2002) Dr. Yaşar Uğurlu’s research focuses on Strategic Management , Organizational Behavior , and Entrepreneurship , with recent work examining topics like green human resource management , digital transformation , and psychological capital . Her article trends highlight the intersection of organizational learning , national culture , and innovation strategies , often applied to sectors such as banking, logistics, and healthcare. Scientific Awards 2018 - Third Best Paper Award, Gazi Osmanpaşa University Graduate Symposium 2017 - Second Place, Uludağ University Bilim-Sanat Awards (Management and Organization) 2017 - Third Place, Uludağ University Bilim-Sanat Awards (Management and Organization) 2016 - Second Place, Gaziantep University Graduate Symposium (Management and Organization) She has supervised numerous theses and contributed to projects like the National Investigation of Psychological Contract Breach and Organizational Silence (2018). Her work spans collaborations with researchers like Duygu Kızıldağ and Cenk Tufan, reflecting a multidisciplinary approach to organizational effectiveness and societal challenges.

• Data Analysis
• Recommendation System
• Responsible AI
• +6 more

Dr. Angela Huo is an Associate Professor at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). She holds multiple leadership roles including Deputy Director of Transnational Education for the Faculty of Engineering and IT, Responsible Academic Officer for Teaching and Learning of the School of Computer Science, Course Director for the Bachelor of Computing Science (Honours) program, and site coordinator for the South Pacific Programming Contests region of the International Collegiate Programming Contest (ICPC). Dr. Huo's research spans several key areas in computer science, with a primary focus on Data Analysis, Recommendation Systems, and Responsible AI. She specializes in utilizing AI and data analysis techniques to improve the efficiency and security of modern software systems. Her recent research interests include recommendation systems, anomaly detection, and privacy-preserving through data mining and deep learning. She has made significant contributions across multiple domains including multimodal learning, federated learning, causal inference, and human behavior recognition. Dr. Huo's publication record demonstrates a strong trajectory in cutting-edge AI research, with recent work focusing on conditional diffusion models for trajectory prediction, hybrid recommendation systems, causal learning to mitigate echo chambers, and adaptive waste detection. Her research shows a consistent pattern of addressing real-world challenges through innovative AI approaches, often combining multiple techniques to achieve superior results. Dr. Huo has secured research funding for projects including "Churn prediction scoping study" and "Army sovereign Software-Defined Battlefield Network." She has been actively involved in teaching and curriculum development, having created Industrial sub-majors for Google, AWS and iOS, Information Security sub-major, Data Analytics for Cybersecurity Microcredentials, and Cybersecurity and Privacy major since 2019. She has taught programming and cybersecurity management courses at the undergraduate level and served as HDR Coordinator of the School of Computer Science during 2021-2022.

• Data Mining
• Knowledge Graphs
• Multimodality Foundation Models
• +5 more

Carl Yang is an Assistant Professor in the Department of Computer Science at Emory University since 2020. He holds courtesy appointments as Assistant Professor in the Center for Data Science (Nell Hodgson Woodruff School of Nursing) and the Department of Biostatistics and Bioinformatics (Rollins School of Public Health). His research focuses on data mining, knowledge graphs, and trustworthy AI with applications in healthcare, neuroscience, and biomedicine. He has received prestigious awards including the NSF CAREER Award (2025), NIH K25 Career Award (2023), and the Best Paper Award at ICDM 2020. Yang earned his Ph.D. from the University of Illinois, Urbana-Champaign under Prof. Jiawei Han, and his B.Eng. from Zhejiang University under Prof. Xiaofei He. Yang’s work spans federated learning for graph data, brain network analysis, and healthcare informatics. He leads initiatives like the FedKDD workshop series and co-organized FedGraph conferences. His research is funded by NSF, NIH, Microsoft, and OpenAI. Notable contributions include FedSage (federated graph learning), BrainGB (fMRI analysis benchmark), and KG-LLM co-learning frameworks. He advises multiple Ph.D. students whose work has been recognized with awards such as the MCBIOS Young Scientist Excellence Award and SDM Doctoral Forum honors. Yang’s current projects include NSF-funded research on diabetes heterogeneity and brain graph mining, NIH grants for health informatics, and collaborations with Stanford, Oxford, and NTU. He also serves as a visiting faculty at Google Research/DeepMind (part-time since 2024) and has held visiting roles at Oxford and Zhejiang University.

Tom KochView profile

Adjunct Professor

• Medical Geography
• Disease Mapping
• Bioethics
• +12 more

Tom Koch is an Adjunct Professor of Geography at the University of British Columbia's Department of Geography within the Faculty of Arts. With a multidisciplinary PhD spanning geography, ethics/philosophy, and medicine, he maintains an active research profile across medical geography, disease mapping, and bioethics. His work bridges historical and contemporary approaches to understanding epidemics spatially through mapping, while also addressing pressing ethical questions in healthcare, particularly around assisted dying and pandemic response. Dr. Koch's research interests span three interconnected domains: medical geography and disease mapping, medical ethics and bioethics, and journalism. His pioneering work in medical geography has generated influential books including Disease Maps: Epidemics on the Ground and Cartographies of Disease , exploring how spatial representation shapes our understanding of disease. In bioethics, he has published extensively on euthanasia, organ transplantation policy, and more recently, Canada's Medical Assistance in Dying (MAiD) legislation, with over 50 papers and books including Ethics in Everyday Places: Mapping Moral Stress, Distress, and Injury . His journalism background informs his public engagement, having conducted over 203 media interviews on the Covid-19 pandemic since January 2020. Analysis of his recent scholarly output reveals a clear trajectory connecting geography, ethics, and pandemic response. His publications demonstrate how spatial thinking informs ethical decision-making during health crises, with particular attention to how mapping technologies have transformed from woodblock prints to Web 3.0 platforms. This interdisciplinary approach reveals how disease mapping serves not just as a technical tool but as a framework for understanding moral dimensions of public health crises, particularly visible in his Covid-19 related work examining both spatial-temporal data democratization and the ethical failures of pandemic response. As an active public intellectual, Dr. Koch has engaged extensively with media and policy discussions, particularly regarding Canada's MAiD legislation and pandemic response. His background as both a journalist and researcher enables him to bridge academic scholarship with public discourse, making complex ethical and geographical concepts accessible to broader audiences while maintaining scholarly rigor.

Yue NingView profile

Assistant Professor

• Machine Learning
• Graph Neural Networks
• Deep Learning
• +7 more

Dr. Yue Ning is an Assistant Professor in the Department of Computer Science at Stevens Institute of Technology, where she conducts cutting-edge research at the intersection of machine learning, graph mining, and data analytics. Her work focuses on social informatics, healthcare, and financial technologies, with an emphasis on developing predictive and generative methods that capture spatio-temporal, dynamic, and interpretable patterns in large-scale datasets. Dr. Ning is affiliated with the Stevens Institute for Artificial Intelligence (SIAI), Center for Research Toward Advancing Financial Technologies (CRAFT), and Semcer Center for Healthcare Innovation (CHI). PhD in Computer Science (2018), Virginia Tech Dr. Ning's research spans several interconnected domains in artificial intelligence. Her work in Graph Neural Networks focuses on designing new architectures for dynamic and heterogeneous graph data, with applications in continual learning and knowledge graph reasoning. In healthcare AI, she develops methods for personalized care, epidemic forecasting, and medical representation learning. Her work on socially responsible AI addresses fairness in finance and medicine, fake news detection, and hate speech identification. She also explores transfer learning and federated learning frameworks for domain adaptation and bias mitigation in imbalanced data scenarios. Analysis of Dr. Ning's recent publications reveals a strong focus on bridging graph neural networks with causal inference for robust event prediction. Her work increasingly integrates large language models with structured graph data for healthcare applications, particularly in medical coding and clinical record analysis. A notable trend is her growing emphasis on fairness and privacy in machine learning systems, reflected in multiple publications on graph fairness, certified unlearning, and bias mitigation in recommender systems. Scientific Awards: NSF CAREER Award (2021-2026) for deep interpretable predictions for multi-scope temporal events NSF CRII Award (2020-2024) for learning dynamic graph-based precursors for event modeling Early Career Award for Research Excellence from Stevens Institute of Technology Research fellowship from NIH-sponsored AIM-AHEAD program Dr. Ning actively mentors PhD and undergraduate students, with several successful PhD graduates including Songgaojun Deng and Chang. Her research is supported by multiple significant grants, including three NSF awards totaling over $1.5 million, NVIDIA GPU grants, and institutional support from Stevens Institute of Technology. She has served as Principal Investigator on projects exploring dynamic graph learning for event prediction, interpretable temporal event modeling, and domain-informed generative frameworks for medical knowledge learning. Dr. Ning leads research within the Stevens Institute for Artificial Intelligence, where she contributes to initiatives in healthcare AI and socially responsible machine learning. Her lab focuses on developing frameworks that integrate domain knowledge with deep learning for applications in healthcare, social science, and finance. Current projects include deep graph learning for dynamic and heterogeneous data, transfer learning for domain adaptation, and machine learning methods for healthcare applications including personalized care and epidemic forecasting.

• Cyber-security
• Internet of Things
• Machine Learning
• +3 more

Professor Amir Hussain is a faculty member at Edinburgh Napier University's School of Computing Engineering and the Built Environment. His expertise spans Cyber-security, Internet of Things (IoT), Machine Learning, and Medical Informatics. He is associated with the Centre for Cybersecurity, IoT and Cyberphysical Systems and has led projects such as a stretchable piezoionic sensor system for electromechanical sensing. His research integrates AI-driven solutions for healthcare, IoT security, and network optimization. Key projects include developing secure edge computing frameworks and post-quantum cryptographic systems. Collaborations involve interdisciplinary work on biomedical sensor networks and federated learning applications. His publications address challenges in face forgery detection, sepsis transcriptomics, and energy-efficient wireless networks. He contributes to both computational and medical domains, emphasizing privacy preservation and secure AI implementations.

Timur HammondView profile

Assistant Professor

• Cultural geography
• Urban geography
• Geographies of memory
• +5 more

Timur Hammond is an Assistant Professor in the Department of Geography and the Environment at Syracuse University's College of Arts & Sciences, with affiliations in the Maxwell School of Citizenship and Public Affairs and the Middle Eastern Studies program. His work bridges cultural and urban geography with a focus on Turkey and the Middle East. Education: Ph.D. in Geography, University of California, Los Angeles (2016) M.A., University of California, Los Angeles (2010) B.A., University of North Carolina at Chapel Hill (2005) Hammond's research explores the complex relationships between space, religion, and memory in urban Turkey, particularly Istanbul. His work examines how religious practices and identities are shaped by and shape urban environments, with special attention to the historical transformations of sacred spaces and practices. He investigates how urban landscapes become imbued with religious meaning through storytelling, architectural practices, and everyday interactions. His scholarship contributes to broader conversations about secularism, political Islam, and urban change in the Middle East, drawing on interdisciplinary approaches from cultural geography, anthropology, and religious studies. Hammond's recent publications demonstrate a consistent focus on the geographies of Islam in urban Turkey, with particular attention to Istanbul's transformation throughout the twentieth century. His work spans historical analysis of Ottoman and Republican periods, contemporary studies of religious practice, and theoretical contributions to understanding space, religion, and memory. A recurring theme is how religious meaning is produced through spatial practices and narratives, rather than being inherent to particular places. Scientific Awards: Laura J. and L. Douglas Meredith Teaching Recognition Award for Early Career Performance, Syracuse University (2022) Maxwell Faculty Fellowship, Humanities Center, Syracuse University (2020) Hammond has secured multiple research grants supporting his work on Istanbul's urban environments, including "Following the Margins: Creative Experiments in Documenting Natures of Contemporary Istanbul" (2023-2024) and "Unlearning the Urban: cities, systems, and inequities" (2021-2023). His service includes Faculty Coordinator for the Future Professoriate Program, membership on the Syracuse University Humanities Council, and editorial roles for academic journals. He has also been active in professional organizations, serving as Awards Director for the Cultural Geography Specialty Group of the American Association of Geographers (2016-2022). Hammond's work connects with broader scholarly communities through his participation in research clusters focused on urban studies, Middle Eastern studies, and religious geography. His book "Placing Islam in Istanbul: Buildings, Stories, and Belonging in a Changing City" (2023) has generated significant discussion in academic circles, as evidenced by interviews such as the Turkey Book Talk podcast (January 2024).

Sasa BatističView profile

Associate Professor

• Human Resource Management
• Social Network Analysis
• Organizational Behavior
• +5 more

Saša Batistič is an Associate Professor at Tilburg University's Tilburg School of Social and Behavioral Sciences, Department of Human Resource Studies. He has been in this position since September 2016, having previously served as a Research Associate at the University of Portsmouth from June 2014 to August 2016. His academic background includes a PhD in Human Resource Management from the University of Reading (2010-2014) and earlier studies in Management at the Faculty of Economics, University of Economicus. Education: PhD in Human Resource Management, University of Reading (2010-2014) Previous Position: Research Associate, University of Portsmouth (2014-2016) Current Position: Associate Professor, Tilburg University (2016-present) Dr. Batistič's research focuses on relational human resource management, social networks, work relationships, socialization of organizational newcomers, employability, and advanced research methods such as bibliometrics. His work has been published in distinguished academic journals including The Leadership Quarterly, Human Resource Management Review, Human Relations, British Journal of Management, International Journal of Project Management, Journal of Business Research and Journal of Knowledge Management. His recent work shows a strong emphasis on bibliometric analyses of HRM topics and the application of artificial intelligence in people management. His most recent publications reveal a clear trend toward examining how artificial intelligence is transforming people management practices, with multiple 2025 publications on AI applications in HRM. He has also conducted significant research on knowledge hiding behaviors, organizational socialization processes, and the relationship between HR systems and organizational performance. His methodological approach frequently combines bibliometric analysis with multilevel research designs. Research Focus Areas: Human Resource Systems, Organizational Socialization, Social Networks Methodological Expertise: Bibliometrics, Multilevel Analysis, Social Network Analysis Recent Trends: AI in HRM, Knowledge Management, Organizational Socialization Dr. Batistič serves on the editorial board of the Journal of Organizational Behavior and has recently taken on editorial activity for Human Resource Development Quarterly. His research has practical applications through projects like the Tilburg L&D Monitor, which provides insights into learning and development trends in organizations. He has established a robust collaborative network, frequently working with researchers such as Rob F. Poell, Matej Černe, and Renata Kenda on multi-authored publications. His work contributes to Sustainable Development Goals related to decent work and economic growth through his research on HR practices and organizational effectiveness.

Jörg SydowView profile

Professor

• Strategic Management
• Network Theory
• Organizational Behavior
• +3 more

Prof. Jörg Sydow is a Professor at the Department of Management , School of Business & Economics, Free University of Berlin. He leads the Chair for Inter-firm Cooperation, focusing on alliances, networks, clusters, and other interorganizational arrangements. His research integrates theoretical and empirical analysis, emphasizing structuration theory, path dependence, and reflexive network development. Current research explores reflexive network development , linking strategic firm conduct to organizational fields like cultural industries and optical technologies. Investigates historical path dependencies and their impact on technological/economic performance. Specializes in interorganizational governance , digital transformation, and creative processes within networks. Recent publications analyze temporary organizations, project-ecosystem dynamics, and the interplay between stability and change in networked environments. His work spans healthcare, energy, and creative sectors, addressing challenges in global production networks and entrepreneurial ecosystems.

• Machine Learning
• Artificial Intelligence
• Medical Imaging
• +5 more

Giovanni Montana is a Professor of Data Science at the University of Warwick, with joint appointments in the Departments of Statistics and Warwick Manufacturing Group (WMG). His research focuses on statistical and machine learning methods, particularly in sequential decision-making, reinforcement learning, and multi-agent systems. He has held academic roles at Imperial College London (2005–2013) and King’s College London (2013–2018), advancing interdisciplinary research in bioinformatics, neuroimaging, and healthcare. Supported by grants from EPSRC, MRC, and Wellcome Trust, his work bridges theoretical advancements and real-world applications. Education: PhD in Computational Statistics (University of Warwick), postdoctoral research at the University of Chicago, and industry experience at Bristol-Myers Squibb. His career spans statistical genetics, medical imaging, and AI-driven healthcare solutions. Research interests include deep reinforcement learning, multimodal biomedical data integration, and AI ethics. Notable achievements include developing biomarker discovery algorithms and AI models for chest X-ray analysis, showcased in media such as BBC and MIT Technology Review. Awards include Chartered Statistician and UKRI Turing AI Acceleration Fellowship. Labs/Teams: Leads the Data Science Research Group at Warwick, fostering collaborative projects in AI, robotics, and healthcare. Active in grant review panels for major funding bodies like EPSRC and the Royal Society.

Daniel HoView profile

Professor

• AI Governance
• Environmental Policy
• Legal Technology
• +3 more

Daniel Ho is the William Benjamin Scott & Luna M. Scott Professor of Law at Stanford University, holding concurrent appointments in Political Science and Computer Science (courtesy). He serves as Director of the Regulation, Evaluation, and Governance Lab (RegLab), and as Senior Fellow at multiple institutes including Stanford HAI and the Institute for Economic Policy Research. His roles include advising the White House on AI policy, serving on the National AI Advisory Commission, and advising the U.S. Department of Labor on responsible AI implementation. Ho holds a J.D. from Yale Law School and a Ph.D. from Harvard University, with prior judicial clerkship experience at the U.S. Court of Appeals. His research focuses on AI governance, legal technology, environmental policy, and algorithmic fairness, integrating computational methods with public administration challenges. Key research interests include AI's societal impacts, legal reasoning automation, regulatory innovation, and equity in algorithmic systems. His work bridges technical and legal domains, addressing challenges in cybersecurity, environmental protection, tax auditing, and racial disparity analysis. Recent projects involve developing benchmarks for AI legal reasoning and frameworks for auditing generative models. Ho leads interdisciplinary teams advancing AI policy implementation, including tools for mapping historical racial covenants and assessing environmental enforcement through satellite imagery. His work emphasizes practical solutions for government agencies, combining technical rigor with legal expertise to shape equitable outcomes.

• Epidemiology of Ageing
• Public Health Modelling
• Dementia Research
• +5 more

Dr. Sara Ahmadi Abhari is a Lecturer in the Epidemiology of Ageing at the School of Public Health, Faculty of Medicine, Imperial College London. She holds an MD from Tehran University (2008) and a PhD in Epidemiology from the University of Cambridge (2014), supported by the Gates Cambridge Scholarship and Krishnan-Ang Scholarship. Her research focuses on dementia biomarkers, public health modeling for disability/dementia burden forecasting, and evaluating interventions' impacts on aging populations. She has prior experience in the Iranian National Malaria Control Program (UNDP) and UCL epidemiology research (2014–2018). Key research areas include Alzheimer’s biomarkers, cardiovascular-dementia linkages, and pandemic effects on older adults. She has published extensively on epidemiological modeling, including studies on hypertension trends, diabetes-dementia comorbidities, and social restrictions' impact on physical activity. Her work integrates clinical, demographic, and AI-driven methods to address public health challenges. Awards: Gates Cambridge Scholarship, Trinity College Krishnan-Ang Scholarship Education: PhD Epidemiology (Cambridge), MD (Tehran University) Her grants and collaborations involve forecasting dementia/disability burdens, analyzing large cohort data (e.g., Whitehall II Study), and evaluating health policy implications. She contributes to global initiatives like the May Measurement Month blood pressure screening campaigns.

• Digital Twins
• Edge Computing
• Federated Learning
• +6 more

Paolo Bellavista is a Professor at the University of Bologna, Department of Electrical, Electronic, and Information Engineering, specializing in Computer Science and Engineering. His research focuses on cutting-edge technologies bridging the physical and digital worlds, with particular emphasis on Digital Twins, Edge Computing, Federated Learning, and IoT systems across various application domains. His research interests span Digital Twins implementation in industrial settings, Edge Computing architectures, Federated Learning techniques, Internet of Things applications, 5G/6G networking, Cloud Computing paradigms, and Smart Manufacturing systems. His work demonstrates a strong focus on practical implementations of theoretical concepts, particularly in the context of distributed systems where latency, reliability, and security are critical concerns. Analysis of his recent publications reveals a strong trend toward integrating Digital Twin technology with edge computing infrastructure, developing privacy-preserving federated learning techniques, and creating efficient communication protocols for industrial IoT applications. His work spans both theoretical contributions and practical implementations across smart factories, transportation systems, aviation, and urban infrastructure. While specific awards are not mentioned in the available data, his extensive publication record in top-tier venues demonstrates significant recognition within the academic community. His collaborations span numerous institutions and researchers across Europe and beyond. Professor Bellavista actively supervises research in distributed systems, with numerous PhD students and postdoctoral researchers contributing to his projects. His research has been supported by various grants focused on next-generation networking, edge computing, and industrial digitalization initiatives. His laboratory work centers around the IoTwins platform for implementing distributed digital twins in industrial manufacturing and facility management settings, with applications spanning smart factories, urban environments, and transportation systems. Current research directions include entanglement-aware middleware for digital twins, federated unlearning techniques, and adaptive resource management in cloud-to-edge continuum environments.

Dadong WangView profile

Adjunct Professor

• Image Analysis
• Computer Vision
• Artificial Intelligence
• +3 more

Dr. Dadong Wang is a Senior Principal Research Scientist and Group Leader of the Imaging and Computer Vision (ICV) Group at CSIRO Data61. He holds academic roles as a Conjoint Professor at UNSW and an Adjunct Professor at UTS. His expertise spans image analysis, computer vision, artificial intelligence, and medical imaging, with a focus on end-to-end solutions for industrial and scientific applications. Education: PhD in Engineering (2002) Doctor of Engineering (1997) Master of Engineering (1993) Bachelor of Engineering (1990) Research Interests: Dr. Wang’s work emphasizes AI-driven solutions for multi-dimensional image data, including applications in agriculture, healthcare, and environmental monitoring. His research bridges theoretical advancements with practical industrial deployment. Awards & Recognition: Winner of APICTA Research & Development Project of the Year (2023) CSIRO Research Achievement Award (2012, 2009) IEEE Senior Member (2010) Keynote speaker at major AI conferences (e.g., Australasia Joint Conference on AI, 2023) Advising & Grants: His team actively seeks PhD candidates in areas like deep learning and augmented reality. Projects include pneumoconiosis diagnosis, fisheries monitoring, and smart agriculture systems. Collaborations span industry and academia, with grants supporting innovation in imaging technologies. Labs & Teams: Leads the ICV Group, which develops cutting-edge computer vision tools and collaborates with industry partners to commercialize solutions. Recent projects include AI for meat quality control and environmental compliance systems.