جستجوی استاد راهنمای دانشگاهی

Laura Stateمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• AI Ethics
• Explainable AI
• +۳ مورد دیگر

Laura State serves as a Research Fellow at the Humboldt Institute for Internet and Society (HIIG) in Berlin, Germany, where she contributes to the AI & Society Lab's Impact AI project. This initiative develops transdisciplinary auditing methodologies to evaluate artificial intelligence systems' contributions to societal transformation and ecological sustainability through rigorous impact assessment frameworks. Her academic credentials include: PhD in Data Science from Scuola Normale Superiore, Pisa, Italy (Advised by Salvatore Ruggieri and Franco Turini) MSc in Neural Information Processing from the University of Tübingen BSc in Physics from the University of Rostock State's research synthesizes hard sciences with social perspectives to investigate AI's societal and planetary implications. She specializes in transparency and accountability mechanisms for non-interpretable machine learning models, developing assessment methodologies to determine how AI can foster sustainable futures. Her interdisciplinary approach integrates technical AI development with regulatory frameworks and ecological impact analysis, emphasizing real-world applicability through industry-academia collaboration. Her 2023-2025 publications reveal a cohesive research trajectory centered on explainable AI and regulatory compliance, particularly regarding GDPR requirements. Key themes include legal-technical alignment for explanation systems, bias/fairness policy frameworks, and innovative evaluation tools like REASONX. The work consistently bridges machine learning theory with societal accountability, demonstrating methodological rigor in translating technical capabilities into public-interest applications. As a core member of HIIG's AI & Society Lab, State collaborates on transdisciplinary teams examining AI's role in sustainability transitions. The Impact AI project coordinates researchers from computer science, law, and social sciences to develop evaluation frameworks that measure AI's contribution to UN Sustainable Development Goals, with active engagement in policy dialogues and public science initiatives like Lange Nacht der Wissenschaften.

Stefan Schulteمشاهده پروفایل

استاد

• Data Engineering
• Blockchain Technologies
• Internet of Things (IoT)
• +۴ مورد دیگر

Prof. Dr.-Ing. Stefan Schulte is a Full Professor at Hamburg University of Technology, leading the Institute for Data Engineering and the Christian Doppler Laboratory Blockchain Technologies for the Internet of Things (CDL-BOT). He holds a diploma in Economics and a Bachelor's in Computer Science from the University of Oldenburg, followed by a Master's in Information Technology (with Merit) from the University of Newcastle. After completing his PhD at TU Darmstadt in 2010, he held roles as Postdoctoral Researcher at TU Wien, Assistant Professor (tenure-track), and eventually Associate Professor before joining TU Hamburg in 2021. His research focuses on data engineering, blockchain technologies applied to IoT, elastic computing, and quality-of-service (QoS) aspects in smart systems. Notable contributions include work on fog computing, federated learning, and cross-blockchain interoperability. He has published over 140 papers in top-tier venues like IEEE Transactions on Services Computing and ACM Computing Surveys. Key awards include Best Paper Awards at the IEEE International Conference on Blockchain (2020) and the European Conference on Service-Oriented and Cloud Computing (2023). Prof. Schulte chairs major conferences such as the IEEE International Conference on Fog and Edge Computing (ICFEC 2025) and serves on editorial boards for journals like IEEE Transactions on Services Computing. He leads CDL-BOT, a lab exploring blockchain applications in IoT and manufacturing. His industrial collaborations include projects like SIMPLI-CITY (smart mobility) and CREMA (cloud-based manufacturing). Current research emphasizes blockchain interoperability, federated learning frameworks, and edge-AI systems. He actively reviews proposals for the German Research Foundation, EU programs, and industry initiatives.

Koushik Senمشاهده پروفایل

استاد

• Software Engineering
• Programming Languages
• Formal Methods

Koushik Sen is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research focuses on developing software tools and methodologies to enhance programmer productivity and software quality, with expertise in Software Engineering, Programming Languages, and Formal Methods. Education: B.Tech from Indian Institute of Technology, Kanpur M.S. and Ph.D. in Computer Science from University of Illinois at Urbana-Champaign Research Focus: Professor Sen pioneers automated testing techniques including concolic testing and DART (Directed Automated Random Testing). His work bridges formal methods with practical software development, emphasizing bug detection, program synthesis, and AI-driven software analysis tools. Recent innovations include machine learning approaches for code recommendation and fuzzing. Publication Trends: His recent publications (2019-2023) demonstrate strong emphasis on fuzzing techniques, program synthesis, and AI/ML applications in software engineering. Notable domains include smart contract security, automated testing, and developer tooling, with frequent collaborations in top-tier conferences. Awards and Honors: NSF CAREER Award (2008) Sloan Foundation Fellowship (2011) IFIP TC2 Manfred Paul Award (2010) Okawa Foundation Research Grant (2015) Multiple ACM SIGSOFT Distinguished Paper Awards UIUC Distinguished Alumni Educator Award (2014) Leadership: Active program committee member for premier conferences (PLDI, ICSE, ISSTA) and keynote speaker. His research is supported by NSF, Okawa Foundation, and Sloan Foundation.

• Blockchain Technology
• Distributed Systems
• Consensus Protocols
• +۶ مورد دیگر

Mohammad Sadoghi is a Professor at the University of California, Davis, with former affiliations at Purdue University, IBM T.J. Watson Research Center, and the University of Toronto. His research focuses on distributed systems, blockchain technologies, consensus protocols, and fault-tolerant computing. He has contributed extensively to transaction processing, stream processing architectures, and the integration of edge-cloud systems with blockchain frameworks. Current Affiliation: University of California, Davis Former Affiliations: Purdue University, IBM, University of Toronto Research Interests include consensus algorithms, Byzantine fault tolerance, distributed ledger technologies, and scalable data processing. He has pioneered systems like ResilientDB and ByShard, addressing challenges in global-scale distributed systems and blockchain fabrics. His work bridges theoretical foundations with practical implementations, emphasizing real-world applications in edge computing and hybrid cloud-edge environments. Key publications highlight advancements in consensus protocols, blockchain scalability, and fault-tolerant architectures. Recent trends in his work focus on concurrent consensus mechanisms, DAG-based systems, and secure geo-replication. Contributions span both academic publications and industry-oriented solutions, such as the Bedrock platform for BFT protocol analysis. Grants and advising roles are implied through his extensive research output, though specific grants are not detailed in the provided text. His collaborations include projects on self-curating databases (e.g., L-Store) and systems like SplitJoin for stream processing.

• Business Process Management
• Process Mining
• Process Analytics
• +۴ مورد دیگر

Hajo A. Reijers is a Professor at the University of Utrecht, Netherlands, with a former affiliation at Vrije Universiteit Amsterdam. His research focuses on Business Process Management (BPM), Process Mining, and Robotic Process Automation (RPA), emphasizing practical applications in healthcare, organizational processes, and human-computer interaction. He contributes to developing tools like SWORD for detecting workarounds and DEUCE for auditing electronic health records. His work spans algorithm development for process discovery, predictive analytics, and optimization techniques. Key areas include analyzing event logs, modeling workplace behavior, and enhancing process transparency. Reijers collaborates extensively with industry partners, addressing challenges in process automation, employee acceptance of AI, and ethical monitoring. His contributions to conferences like BPM, CAiSE, and ICIS highlight interdisciplinary approaches, combining computer science with organizational studies. Notable projects include frameworks for task mining, reinforcement learning in care processes, and pattern recognition in government transparency assessments. Research initiatives often involve cross-disciplinary teams, exploring topics like workplace well-being through process mining, decision-making support systems, and overcoming barriers to BPM adoption. His work bridges theoretical advancements with real-world impact, influencing both academic discourse and practical business solutions.

• Artificial Intelligence
• Automated Planning
• Temporal Reasoning
• +۴ مورد دیگر

Alfonso Emilio Gerevini is a prominent researcher in artificial intelligence with over 30 years of continuous academic contributions. His work spans theoretical foundations of automated planning to practical healthcare applications, with recent publications demonstrating significant impact in both traditional AI domains and emerging interdisciplinary areas. His research interests focus on automated planning systems , temporal reasoning , and multi-agent coordination , with recent expansion into healthcare applications using machine learning techniques. Gerevini has made fundamental contributions to planning algorithms, particularly in width-based search, case-based planning, and privacy-preserving multi-agent planning. His work on PDDL (Planning Domain Definition Language) has been influential in standardizing planning representations. Analysis of his 15 most recent publications reveals a strategic evolution from core planning research toward impactful healthcare applications, particularly during the COVID-19 pandemic. While maintaining his expertise in planning algorithms, he has successfully integrated machine learning techniques to address real-world medical challenges including radiology report analysis, prognosis prediction, and lab test interpretation. His work demonstrates exceptional versatility across both theoretical and applied domains of artificial intelligence. Gerevini maintains a robust collaborative network, primarily with Italian researchers including Ivan Serina, Alessandro Saetti, and Luca Putelli. His publications appear consistently in top-tier AI venues including Artificial Intelligence journal, Journal of Artificial Intelligence Research, and AAAI/ICAPS conferences. The collaborative patterns suggest he leads a significant research group focused on advancing planning systems while applying them to critical real-world problems.

• Corporate Management
• Strategic Management
• Organizational Theory
• +۵ مورد دیگر

Anja Tuschke holds the W3 Professorship for Business Administration, especially Corporate Management / Strategic Management, at the Ludwig Maximilian University of Munich within the Faculty of Business Administration . Her work bridges strategic decision-making, organizational dynamics, and corporate governance, with a focus on leadership, boardroom practices, and institutional influences. Teaching: Courses include Strategic Management: Concepts and Cases , Organizational Theory , and Strategy and Leadership . Research: Explores strategic change, corporate misconduct, board networks, executive compensation, and institutional legitimacy. Publications highlight trends in governance, organizational behavior, and strategic adaptation, with recent focus on digital transformation, CEO accountability, and network-driven practices.

Theresa Zügerمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Public Interest AI
• Sustainability
• +۵ مورد دیگر

Dr. Theresa Züger is an interdisciplinary researcher leading the AI & Society Lab at the Alexander von Humboldt Institute for Internet and Society (HIIG). She investigates how AI systems can be designed to serve the common good, focusing on initiatives that promote sustainability, strengthen social inclusion, and enable technology reuse through open systems. Her work addresses societal challenges associated with artificial intelligence at political, social, and cultural levels, contributing to accurate assessment of AI's societal implications. Züger received her Ph.D. in Media Studies from Humboldt University in Berlin in 2017 with her dissertation 'Reload Disobedience,' focusing on digital forms of civil disobedience. Previously, she earned an M.A. in Theatre, Film and Television Studies as well as German and Philosophy from the University of Cologne. She also headed the office responsible for the German government's Third Engagement Report on behalf of the Federal Ministry for Family Affairs, Senior Citizens, Women and Youth (BMFSFJ). Her research centers on Public Interest AI, examining how AI development and deployment can benefit society rather than primarily maximizing profit. Current projects include 'Impact AI' (funded by Volkswagen Foundation), which develops auditing methods to assess AI projects' impact on public interest and sustainability, and 'Human in the Loop,' investigating how automated decision-making processes should be designed for successful human-machine interaction. She previously led the 'Public Interest AI' project, developing a theoretically grounded understanding of public interest AI and creating prototypes like a web accessibility tool and fact-checking application. Analysis of her recent publications reveals a consistent focus on bridging AI technology with societal values. Her work spans technical aspects of AI implementation, ethical considerations, and practical applications that serve public interests. Key trends include human-AI collaboration frameworks, accessibility enhancements, critical examination of AI hype, and theoretical foundations for public interest-oriented AI development. Züger serves as Vice-Chair of the UNESCO Commission on Communication and Information and participates in several juries including the Deep Tech Award, Digital Places – Land of Ideas, and the Civic Innovation Fund. She is also a regular event moderator for organizations like the Berlin-Brandenburg Media Authority, re:publica, and transmediale. As project leader of Impact AI and head of the AI & Society Lab, Züger directs significant research initiatives with funding from organizations like the Volkswagen Foundation. Her work involves extensive collaboration with academic and practical partners across Europe and globally, particularly through projects addressing women in tech and international AI governance. The AI & Society Lab functions as an interdisciplinary interface for new research approaches and knowledge transfer in AI, promoting inclusive, human rights-friendly, and sustainable AI strategies in Europe.

Athanasios Andreouمشاهده پروفایل

پژوهشگر

• AI Ethics
• Cybersecurity
• Data Privacy
• +۳ مورد دیگر

Athanasios (Thanos) Andreou is a Postdoctoral Researcher at the NYU Tandon School of Engineering under Damon McCoy's team. He holds a PhD from Sorbonne Université (conducted at EURECOM), supervised by Patrick Loiseau and Oana Goga, and a Diploma (5-year) in Electrical and Computer Engineering from NTUA. His research focuses on AI ethics, cybersecurity, privacy, and e-crime, with a particular emphasis on auditing social media advertising ecosystems and transparency mechanisms. His academic journey includes a MSc in Communications & Computer Security from EURECOM. Key research interests span internet measurements, transparency in digital advertising, and mitigating privacy risks in social platforms. Notable contributions include developing the Facebook Ads Monitor tool and investigating privacy vulnerabilities in data brokers' interfaces. Publications: Over a decade of work, his publications address critical issues such as ad transparency limitations, privacy risks in Facebook targeting, and identity disclosure risks in multi-profile user scenarios. Awards: Winner of the 2020 CNIL-Inria Award, runner-up for the Caspar Bowden PET Award, and recipient of best paper runner-up at ASONAM 2017. His work bridges social media analysis with practical tools like AdAnalyst, aiming to enhance user awareness of targeted advertising practices. Current affiliations include NYU Tandon, with prior involvement at EURECOM and Sorbonne Université.

Timo Speithمشاهده پروفایل

مدرس

• Machine Ethics
• Machine Explainability
• Artificial Intelligence
• +۲ مورد دیگر

Dr. Timo Speith is a fixed-term Lecturer at the University of Bayreuth's Faculty of Cultural Studies, affiliated with the Professorship for Philosophy, Computer Science and Artificial Intelligence. He holds a B.A. in Philosophy and M.Sc. in Computer Science from Saarland University, where he also completed his doctoral studies in Philosophy. His research focuses on machine ethics, machine explainability (XAI), and non-speculative AI systems, alongside interests in ancient philosophy and the philosophy of science. He has contributed to interdisciplinary work on XAI evaluation methods, fairness in AI systems, and explainable hardware (XHW). Speith has organized workshops on explainable systems requirements engineering and co-authored a manifesto outlining XAI 2.0 challenges. His work bridges philosophy, computer science, and ethics to address AI transparency and accountability. Education: B.A. Philosophy, Saarland University M.Sc. Computer Science, Saarland University Ph.D. in Philosophy, Saarland University (2023 dissertation) Research Interests: Machine Ethics, XAI Methodology, AI Transparency, Philosophy of Science, Ancient Philosophy. Grants and Collaborations: Active in international workshops (e.g., RE4ES), IEEE conferences, and collaborations with institutions like Saarland University and the University of Bayreuth's Philosophy Institute. His work addresses practical and theoretical challenges in AI explainability across hardware and software systems. Lab/Team Affiliation: Associated with the Philosophy, Computer Science, and Artificial Intelligence research group at the University of Bayreuth, focusing on interdisciplinary AI ethics and technical implementation.

Önder Askinمشاهده پروفایل

پژوهشگر

• Differential Privacy
• Statistical Inference
• Privacy-Preserving Algorithms
• +۲ مورد دیگر

Önder Askin is a Research Assistant at Ruhr University Bochum's Institute of Statistics, specializing in differential privacy and statistical inference. His research develops frameworks for quantifying and verifying privacy guarantees in data analysis systems. Academic background includes a Master's in Mathematics from Ruhr University Bochum (2019). Research establishes mathematical foundations for privacy preservation, particularly developing methods to evaluate Rényi differential privacy bounds in black-box settings. Publications address statistical quantification of privacy parameters and general-purpose auditing techniques. Current work advances f-Differential Privacy estimation methods for practical algorithm deployment.

• Digital Archiving
• Conventional Archiving
• Digital Preservation
• +۳ مورد دیگر

Christian Keitel is an Honorary Professor for Archival Science and Digital Archiving at the University of Applied Sciences Potsdam. He works at the Landesarchiv Baden-Württemberg, where he has served since 2000, currently as Deputy Head of Department 2 (Specialized Programs and Educational Work). His research focuses on conceptual principles of conventional and digital archiving, DIMAG systems, certification of digital archives, authenticity of digital records, and cooperative learning processes in digital archiving. University of Applied Sciences Potsdam – Honorary Professor (2015–) Landesarchiv Baden-Württemberg – Deputy Head of Department (2011–), Senior Archivist (2000–) Keitel’s scholarly work emphasizes digital preservation, metadata standards, and long-term archive certification. His publications address challenges in trustworthiness of digital repositories, significant properties of electronic records, and collaborative digital archiving frameworks. He co-authored DIN standards 31644 and 31645 for trustworthy digital archives. Recent articles highlight his contributions to digital ingest workflows, metadata extraction, and municipal archive solutions. His work explores intersections between traditional archival principles and modern digital challenges, including authenticity verification, system development, and cross-institutional cooperation.

Muhammad Bilal Zafarمشاهده پروفایل

استاد

• Human-Centric AI/ML
• Fairness in Machine Learning
• Explainable Artificial Intelligence
• +۳ مورد دیگر

Professor Muhammad Bilal Zafar holds a faculty position at Ruhr University Bochum (RUB), where he heads the Chair of AI and Society within the Research Center for Trustworthy Data Science and Security. He is also a Principal Investigator (PI) at the Cluster of Excellence CASA and a member of the Horst Görtz Institute for IT Security. Previously, he worked as a Senior Applied Scientist at Amazon Web Services and a Research Scientist at Bosch Center for Artificial Intelligence. He completed his PhD at the Max Planck Institute for Software Systems (MPI-SWS) and Saarland University, advised by Krishna P. Gummadi and Manuel Gomez Rodriguez. His research focuses on human-centric AI/ML, emphasizing fairness, explainability, and robustness of AI systems. Key contributions include mechanisms for fair classification, bias detection in algorithms, and trustworthy AI applications. He actively engages in mentoring PhD and Master’s students, with current openings for candidates in Computer Science, Mathematics, and related fields. His work bridges theoretical advancements with practical solutions for ethical AI deployment. Notable collaborations include projects with AWS and Bosch, addressing challenges in algorithmic fairness and AI transparency. His publications span top venues like ACL, KDD, NeurIPS, and WWW, highlighting contributions to both foundational and applied aspects of AI ethics.

• Artificial Intelligence
• Information Systems
• Blockchain Technology
• +۳ مورد دیگر

Oliver Hinz is a prominent researcher in the field of information systems and digital technologies, focusing on areas such as artificial intelligence ethics, blockchain applications, cybersecurity, and digital responsibility. His work bridges technical innovation with societal impact, addressing challenges in corporate governance, healthcare informatics, and user-centric technology design. Research interests: AI governance, blockchain tokenization, digital democracy, and ethical technology adoption Co-authored over 160 publications in leading journals like Information Systems Research and MIS Quarterly Key collaborations with institutions like TU Munich and Tilburg University Recent work emphasizes explainable AI frameworks, disaster management through social media analytics, and the socio-economic implications of digital platforms.

Fuyuan Zhangمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Cybersecurity
• Formal Methods
• +۷ مورد دیگر

Fuyuan Zhang is a Postdoctoral Researcher at the Max Planck Institute for Software Systems, specializing in advanced software testing methodologies and formal verification techniques. His research focuses on improving the reliability and security of AI systems, quantum computing frameworks, and concurrent systems through innovative testing criteria, adversarial attacks, and compositional reasoning. Key areas of expertise include: Large Language Model (LLM) testing and validation Quantum program analysis and security Adversarial machine learning and neural network robustness Formal verification of concurrent and cyber-physical systems Automated bug detection in complex software systems His work bridges theoretical foundations with practical applications, addressing critical challenges in AI safety, quantum software reliability, and system-wide security certification.