جستجوی استاد راهنمای دانشگاهی

Sascha van Schendelمشاهده پروفایل

استادیار

• Data Protection Law
• Privacy Law
• Cybersecurity
• +۱۲ مورد دیگر

Sascha van Schendel is an Assistant Professor in Data Protection & Cybersecurity at the Department of Law & Markets, Erasmus School of Law, Erasmus University Rotterdam. She is affiliated with the Erasmus Centre of Law and Digitalization and contributes to the Sectorplan SSH-Breed research initiative on the influence of digitalization on work, prosperity, and entrepreneurship. Her research lies at the intersection of law, technology, and fundamental rights, with a focus on data protection, privacy, algorithmic governance, and the regulation of emerging technologies in public and environmental domains. She has published widely on topics including GDPR compliance, privacy by design, smart energy systems, AI in criminal justice, and the legal implications of predictive policing. The recent publications reflect a strong trend toward the regulation of data in public infrastructure (e.g., energy grids), algorithmic accountability in law enforcement, and the integration of legal and technical perspectives in privacy-preserving systems. Her work spans environmental law, criminal procedure, and digital constitutionalism, demonstrating interdisciplinary reach. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While no formal PhD or Master’s students are listed, Dr. van Schendel has been involved in significant research projects such as the NWO-funded Megamind project on smart energy grids, indicating experience with competitive grant funding. She actively collaborates with legal scholars and contributes to public policy debates, including responses to national legislation like the Dutch Cybersecurity Bill. Labs and Teams: She is a core member of the Erasmus Centre of Law and Digitalization, a research hub focusing on the legal challenges of digital transformation. Her work is embedded in interdisciplinary collaborations involving law, technology, and public policy experts.

• Process Mining
• Healthcare Informatics
• Data Security
• +۳ مورد دیگر

Dr. Jeewanie Jayasinghe Arachchige is a Lecturer in the Department of Computer Science at Vrije Universiteit Amsterdam, Faculty of Science. She teaches undergraduate courses including Bachelor Project Computer Science, Professional Development, and Software Engineering Processes for the academic year 2024–2025. Her research focuses on process mining , healthcare informatics , and data security . She applies process mining to analyze healthcare pathways and subpopulation treatment variations, develops explainable AI frameworks for predictive analytics, and examines data governance in emerging architectures like Data Lakehouses. Her work intersects legal informatics, particularly formalizing Sri Lankan civil court processes using ontology engineering. Recent publications highlight trends in balancing simplicity and complexity in process modeling, Industry 4.0 healthcare applications, and cybersecurity in model-driven web development. She has contributed to over 20 peer-reviewed articles since 2006, spanning topics from service-oriented architectures to value network analysis. Her teaching and research emphasize practical applications of IT in healthcare, legal systems, and enterprise environments. No ancillary activities are currently recorded.

Jakob Schoefferمشاهده پروفایل

استادیار

• Responsible AI
• Explainable AI
• Human-Centered AI
• +۶ مورد دیگر

Jakob Schoeffer is a tenure-track Assistant Professor in the Artificial Intelligence department at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen (Netherlands). His work focuses on the intersection of human decision-making and artificial intelligence, particularly in high-stakes contexts where fairness, transparency, and appropriate human-AI collaboration are critical. Dr. Schoeffer's research interests center on responsible and explainable AI, with specific focus areas including: Human-AI collaboration dynamics in decision-making processes Fairness perceptions and interventions in AI systems Appropriate reliance on AI recommendations Explainable AI techniques for high-stakes domains Transparency mechanisms that improve human-AI team performance Label indeterminacy issues in medical AI applications His recent publications (2023-2025) reveal a strong trend toward applying AI research in critical domains like healthcare (particularly neurological recovery prediction), while maintaining a rigorous focus on the human aspects of AI deployment. His work spans both theoretical foundations of human-AI interaction and practical implementations, often employing mixed-methods approaches that combine technical AI development with behavioral studies. Dr. Schoeffer actively collaborates with researchers across institutions including the University of Texas at Austin and has made significant contributions to top conferences in AI ethics, fairness, and human-computer interaction. His research has been featured in multiple news outlets and policy discussions, indicating real-world impact of his work on responsible AI development. Prior to his current appointment, Dr. Schoeffer was a Postdoctoral Research Fellow at the University of Texas at Austin. He received his PhD from the Karlsruhe Institute of Technology (KIT) in Germany with a dissertation titled "On the Interplay of Transparency and Fairness in AI-Informed Decision-Making." He also holds a master's degree in Operations Research from Georgia Tech and industry experience as a Senior Data Scientist at IBM.

• Advertising Law
• Consumer Law
• E-Commerce
• +۶ مورد دیگر

Bram Duivenvoorde is an Associate Professor at the Molengraaff Institute for Private Law , Utrecht University , specializing in advertising law , consumer law , and e-commerce . He holds a PhD from the University of Amsterdam (2014) and combines academic research with practical legal experience, including roles as an attorney-at-law at Hoogenraad & Haak (2014-2020) and interim legal counsel at Heineken International (2018). His work focuses on digital society , personalized marketing , and AI regulation in consumer contexts. Education : PhD (UvA, 2014), LLM (Utrecht, 2009), LLB (Utrecht, 2007) Key Research Areas : Advertising Law, Consumer Law, Digital Regulation, EU Law, Personalized Marketing Recent publications analyze generative AI , deepfakes , and DSA compliance , emphasizing consumer protection in evolving digital landscapes. He supervises PhD candidates Thijs Kelder and Jorn Torenbosch and leads the LLM Honours Programme . Duivenvoorde contributes to European consumer law through RENFORCE initiatives and serves as Editor for journals like Intellectuele Eigendom en Reclamerecht .

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Mohammad Aliannejadi is an Assistant Professor at the IRLab (formerly ILPS) within the Informatics Institute at the University of Amsterdam. His research focuses on Information Retrieval (IR), machine learning, natural language processing (NLP), and conversational systems, particularly in modeling user information needs on mobile devices and conversational search systems. He holds a Ph.D. in Informatics from Università della Svizzera italiana (USI), Lugano, Switzerland, and a M.Sc. in Computer Engineering from Tehran Polytechnic. During his Ph.D., he visited the CIIR Lab at the University of Massachusetts Amherst, USA. His research interests include conversational search systems, recommender systems, unified search frameworks, and user-centric evaluation methodologies. Notable contributions include work on clarifying questions in open-domain dialogues, contextual suggestion systems, and cross-market recommendation. Aliannejadi has organized major shared tasks and workshops, including the IGLU Contest (NeurIPS 2021) and XMRec Workshop (RecSys 2021). He serves on program committees of top IR conferences like SIGIR, CIKM, and ECIR, and has authored over 50 peer-reviewed publications in these areas. His work has received recognition, including top performance in TREC Contextual Suggestion tracks (2015, 2016). He actively contributes to the IR community through teaching, including courses on Information Retrieval and Human-in-the-Loop Machine Learning at the University of Amsterdam.

Madalina Busuiocمشاهده پروفایل

استاد

• Public Governance
• Artificial Intelligence in Governance
• Algorithmic Decision-Making
• +۷ مورد دیگر

Prof. Dr. Madalina Busuioc is a Full Professor of Public Governance at the Department of Political Science and Public Administration, Vrije Universiteit Amsterdam. She serves as Director of the Graduate School of Social Sciences and co-Director of the R&I Lab on Artificial Intelligence and Digital Governance. Her ERC-funded research explores public accountability in the AI era, with a focus on algorithmic governance and cognitive biases in administrative decision-making. She holds a PhD cum laude from Utrecht University (2010). Her research interests center on public power dynamics, algorithmic governance, and institutional accountability. Notable contributions include work on AI's impact on citizen-state interactions, reputational authority in bureaucracy, and regulatory oversight mechanisms. Her book European Agencies: Law and Practices of Accountability (Oxford UP) and peer-reviewed articles in Public Administration Review , Journal of Public Administration Research and Theory , and Governance highlight her scholarly impact. Teaching contributions include designing the MSc Public Administration: Artificial Intelligence and Governance program, which integrates technical and governance perspectives. Awards include the Haldane Prize (2016) and Fernand Braudel Fellowship (2021). She advises on AI policy through ancillary roles like membership in the Meijers Commission on international law. Her research projects address AI ethics, algorithmic accountability, and regulatory innovation. Recent work explores behavioral dimensions of human-AI collaboration in public services and the societal implications of AI adoption in administrative systems.

• User-Generated Content Analysis
• Generative AI in Healthcare
• Online Platforms and Decision Making
• +۳ مورد دیگر

Dr. Dicle Yagmur Ozdemir is an Assistant Professor of Business Information Management at the Rotterdam School of Management (RSM), Erasmus University. She joined RSM in September 2023 after earning a PhD in Management Science (Information Systems concentration) from the University of Texas at Dallas, a Master's in Industrial Engineering from Sabanci University, and a Bachelor's in Industrial Engineering from Istanbul Technical University. Her research focuses on user-generated content dynamics in online platforms and the application of generative AI in healthcare. She employs econometric modeling and natural language processing to study how novel information in reviews influences stakeholders' decisions, and designs algorithms to mitigate information overload. In healthcare AI, she experiments with generative AI's impact on patient-provider interactions. Her work has been presented at top conferences including CIST, WITS, ICIS, WCBA, and INFORMS. Key themes in her research include algorithmic fairness in content selection, the psychological effects of AI-driven health advice, and the mediation effects of review novelty on consumer behavior. She has collaborated internationally, with research outputs including 6 works since 2023. Notable contributions address the moderating role of review dissimilarity in credibility assessments and the paradoxical effects of positive/negative review valence in decision contexts.

Sanne Kruikemeierمشاهده پروفایل

استاد

• Digital Media
• Political Communication
• Social Media and Polarization
• +۴ مورد دیگر

Prof. Sanne Kruikemeier is a Professor of Digital Media and Society at Wageningen University & Research (WUR). She leads the ERC Starting Grant (HUNTING) and a NORFACE consortium grant, coordinating research across four European universities (2020–2024). As director of the Digital Data and Democracy Lab and member of the AlgoSoc Gravitation Program, she explores digitalization's societal and democratic impacts. Her research focuses on data-driven political targeting, social media's role in polarization, privacy behavior, and digital journalism. She holds roles on the editorial boards of journals like Human Communication Research , International Journal of Press/Politics , and Digital Journalism . Awards include the KNAW Early Career Award and ICA Herbert S. Dordick Dissertation Award. Her work bridges digital media, political communication, and societal change, addressing challenges like misinformation and democratic engagement. Kruikemeier advises PhD candidates on projects like data-driven campaigning's impact on democracy and polarization strategies. She engages in public discourse through media commentary on social media's role in politics and news consumption trends. Her interdisciplinary approach integrates communication science with computational methods to analyze modern political and social dynamics.

• Latent Variable Modeling
• Machine Learning
• Measurement Equivalence
• +۳ مورد دیگر

Dr. David Goretzko is an Assistant Professor in the Department of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences. He leads the Measurement and Machine Learning Lab and specializes in the integration of data science techniques with psychometric theory. His academic journey includes: Ph.D. in Psychological Methods from LMU Munich (2020) M.Sc. in Statistics from LMU Munich (2018) M.Sc. in Psychology from LMU Munich (2016) B.Sc. in Physics from LMU Munich (2015) B.Sc. in Psychology from LMU Munich (2014) Dr. Goretzko's research primarily focuses on the intersection of machine learning and psychometrics. His work addresses critical challenges in factor analysis, measurement invariance, and model fit assessment. He develops innovative methods that combine traditional psychometric approaches with modern data science techniques, particularly in the areas of exploratory factor analysis trees, regularized factor analysis, and cost-sensitive machine learning applications in psychological assessment. His research has significant implications for improving the validity and reliability of psychological measurements across diverse populations. His recent publications reveal a strong trend toward integrating machine learning methodologies with traditional psychometric approaches. A significant portion of his work focuses on factor analysis techniques, particularly addressing the challenge of determining the appropriate number of factors. His research also extensively covers measurement invariance testing across multiple covariates using tree-based approaches, and he has made notable contributions to evaluating model fit in confirmatory factor analysis. The interdisciplinary nature of his work spans psychology, statistics, and computer science. Dr. Goretzko serves as an Associate Editor for the European Journal of Psychological Assessment and is an active reviewer for numerous prestigious journals including Psychological Methods, Behavior Research Methods, and Structural Equation Modeling. He also reviews grant proposals for major funding agencies such as the German Research Foundation (DFG), National Science Foundation (NSF), and Dutch Research Council (NWO). He currently holds a Project Grant from the German Research Foundation (DFG GO 3499/1-1) since 2021. His research program focuses on developing and validating new methodologies for psychological assessment that incorporate machine learning techniques while maintaining psychometric rigor. His work has practical applications in educational measurement, clinical psychology, and organizational assessment. Dr. Goretzko leads the Measurement and Machine Learning Lab at Utrecht University, which focuses on developing innovative methodologies that bridge the gap between traditional psychometrics and modern data science. The lab's research has particular relevance for improving measurement practices in cross-cultural research, educational assessment, and clinical psychology settings where measurement invariance and factor structure validation are critical concerns.

Kate Crawfordمشاهده پروفایل

استاد پژوهش

• Artificial Intelligence
• Machine Learning
• Politics of Technology
• +۴ مورد دیگر

Kate Crawford is a Research Professor at the Annenberg School for Communication and Journalism, University of Southern California; Senior Principal Researcher at Microsoft Research New York City; and Honorary Professor at the University of Sydney. She serves as the inaugural Visiting Chair for AI and Justice at École Normale Supérieure in Paris, co-leading the international working group on Foundations of Machine Learning. Her research examines artificial intelligence through interdisciplinary lenses including politics, labor, environmental impact, and historical context. She investigates how large-scale data systems shape societal structures while emphasizing ethical implications and power dynamics in algorithmic systems. Her work bridges technical AI development with critical social theory. Dr. Crawford co-founded three major research initiatives: FATE (Fairness, Accountability, Transparency, Ethics) at Microsoft Research; AI Now Institute at New York University; and Knowing Machines at USC. She has advised policy bodies including the United Nations, Federal Trade Commission, European Parliament, Australian Human Rights Commission, and White House on AI governance. Her scientific recognition includes: Miegunyah Distinguished Visiting Fellowship (University of Melbourne, 2021)

Mykola Pechenizkiyمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Predictive Analytics
• +۵ مورد دیگر

Mykola Pechenizkiy is a Full Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), holding the Data Mining Chair. He also serves as an Adjunct Professor in Data Mining for Industrial Applications at the University of Jyväskylä. His research focuses on predictive analytics, data mining, and responsible AI, addressing real-world challenges in industry, healthcare, and education. He leads the Customer Journey research program at the Data Science Center Eindhoven, emphasizing ethical and transparent analytics. Academically, he holds a PhD from the University of Jyväskylä (2005) and has held visiting researcher positions at institutions like Columbia University and NYU. He has co-authored over 300 peer-reviewed publications and serves on editorial boards and committees for leading conferences (e.g., AAAI, IJCAI). He is the President of the International Educational Data Mining Society (IEDMS). His research interests include concept drift adaptation, sparsity techniques in neural networks, and fairness-aware AI. He has led projects such as the TKI PPS KPN Smart Two initiative and collaborates with industries like ASML, Philips, and Rabobank. His work contributes to UN SDGs, particularly in sustainable development through AI-driven solutions. Awards: Best Demo Paper Award (IEEE ICDE 2023), Best Paper Awards (ALA 2022, LoG 2022), and SensorKDD 2009 recognition. Grants/Projects: Active projects include TKI PPS KPN Smart Two (2019–2025) and Smart One W&I TKI KPN Flagship (2018–2022). Labs/Teams: Affiliated with EAISI Health, SIKS Scientific Board, and the University of Waikato’s AI Institute.

Konrad Kollnigمشاهده پروفایل

استادیار

• Digital Privacy
• AI Ethics
• Regulatory Technology (RegTech)
• +۴ مورد دیگر

Konrad Kollnig is an Assistant Professor at Maastricht University’s Faculty of Law, specializing in the intersection of law and technology. He leads the RegTech4AI project, which combines legal and technical methods to address challenges in the AI and digital platforms sector. His academic background includes a PhD and MSc from the University of Oxford and a BSc from RWTH Aachen, with his PhD thesis winning the prestigious Stefano Rodotà Award 2024. Research focuses on market power analysis in digital platforms, ethical AI governance, and privacy-preserving technologies. He developed the TrackerControl app (200,000+ downloads) to expose app tracking practices. His work has influenced EU, OECD, US FTC, and other regulatory bodies, and been featured in Forbes, Wired, and New Scientist. Key achievements include winning the United Nations Privacy Competition 2022 and the Best Student Privacy Paper Award 2022. He holds a five-year RegTech4AI project grant (€2.1M) funding six researchers. Talks and collaborations span institutions like Georgetown University, CNIL, and the Council of Europe. His interdisciplinary approach bridges computer science, law, and policy to address systemic risks in digital ecosystems.

Vedran Dunjkoمشاهده پروفایل

استاد

• Quantum Computing
• Quantum Machine Learning
• Quantum Artificial Intelligence
• +۷ مورد دیگر

Professor Vedran Dunjko is a faculty member at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, with affiliations to the Leiden Institute of Physics (LION). He leads the Applied Quantum Algorithms group and co-founded the Quantum@LIACS initiative, focusing on the intersection of quantum computing, machine learning, and artificial intelligence. His research interests include quantum machine learning, quantum-enhanced reinforcement learning, quantum heuristics, and the application of AI to quantum computing challenges. Dunjko's work bridges theoretical foundations with experimental implementations on near-term quantum devices, exploring both quantum advantages in learning and the use of classical AI for quantum system design. The recent publications show a strong trend toward proving quantum advantages in learning tasks, optimization, and topological data analysis, with publications in Nature , Nature Communications , and NeurIPS . Key themes include quantum policy gradients, quantum TDA, and reinforcement learning for quantum circuit optimization. ERC Consolidator Grant (2024) PNAS Cozzarelli Prize (2018) Editor’s Suggestion in Physical Review Letters (2014, 2018) Featured in Physics (American Physical Society) (2014, 2018) Dunjko advises several PhD candidates and postdocs, including Rahul Bandyopadhyay, Sofiene Jerbi, and Lea Trenkwalder. He has received competitive grants, most notably the ERC Consolidator Grant in 2024. His group fosters international collaborations with institutions across Europe and industry partners. The Applied Quantum Algorithms group and the Quantum@LIACS team combine theoretical investigations with practical implementations on quantum hardware, focusing on scalable quantum algorithms and AI-driven quantum discovery.

Antonia Krefeld-Schwalbمشاهده پروفایل

استادیار

• Cognitive Science
• Consumer Behavior
• Sustainable Marketing
• +۵ مورد دیگر

Antonia Krefeld-Schwalb is an Assistant Professor at the Department of Marketing Management, Rotterdam School of Management, Erasmus University. With a background in cognitive science and management, her research bridges computational modeling, eye-tracking, and consumer decision-making to address sustainability challenges. Current Affiliation: Assistant Professor, Rotterdam School of Management Research Focus: Sustainable consumer behavior, decision-making processes, and methodological improvements Key Collaborations: Columbia University, University of Geneva, Erasmus Sustainability Program Her cognitive science training informs methodological approaches like mouse/eye tracking and computational modeling applied to marketing problems. She investigates structural parameter interdependencies, external validity threats in surveys, and climate risk communication effectiveness. Recent research trends include climate adaptation strategies, sustainable behavior interventions, and meta-scientific analyses of statistical practices in consumer research. She advocates for heterogeneous population sampling and preregistration to enhance validity. Scientific Honors: Veni Grant (NWO) She develops targeted sustainability interventions through collaborations like the Erasmus Sustainability Program. Her work appears in journals such as PNAS, Journal of Marketing Research, and Psychological Review.

Halefom Abrahaمشاهده پروفایل

استادیار

• Artificial Intelligence and Law
• Privacy and Data Protection
• European and International Law
• +۵ مورد دیگر

Dr. Halefom Abraha is an Assistant Professor at the International and European Law (IER) Department of Utrecht University's School of Law. He specializes in Digital Human Rights, focusing on privacy, data protection, AI regulation, and algorithmic management in labor markets. His research addresses cross-border data access for law enforcement and digital sovereignty. Abraha holds a joint PhD from the University of Malta and Edith Cowan University (Australia) for his thesis on 'Cross-Border Data Access for Law Enforcement Purposes,' which earned the 2021 Dean's Award for Academic Excellence. He completed postdoctoral research at the Bonavero Institute of Human Rights, University of Oxford (2021-2024), and has advised the European Parliament’s LIBE Committee. His expertise spans EU and international law, with notable fellowships including Marie Sklodowska-Curie ITN (2017-2020) and AFRINIC (2017). Abraha is affiliated with the Netherlands Institute of Human Rights (SIM) and RENFORCE, focusing on governance challenges in the digital society. His research areas include the Future of Work under digital transformation, Security in Open Societies, and Contesting Governance paradigms. Key publications address workplace data protection frameworks, algorithmic management regulation, and cross-border data governance. Abraha advocates for contextualized legal solutions balancing innovation with human rights protections.