جست‌وجوی استاد و پژوهشگر

Jörg Pohleمشاهده پروفایل

پژوهشگر

• Informatics and Law
• Informatics and Sociology
• Data Protection
• +۶ مورد دیگر

Dr. Jörg Pohle is a PostDoc researcher at the Humboldt Institute for Internet and Society (HIIG) where he heads the research programme 'Data, actors, infrastructures: The governance of data-driven innovation and cyber security' and the 'Global Privacy Governance' project. His interdisciplinary work bridges Informatics, Law, and Sociology, focusing on data protection theory and practice, privacy governance, and the societal implications of digital technologies. Pohle completed his doctorate at Humboldt-Universität zu Berlin with a dissertation on the history and theory of data protection and its implications for ICT system design. His academic background spans Law, Political Science, and Computer Science, with early work examining security of voting computers and related security discourse. His research interests include the intersections of Informatics and Law, Informatics and Sociology, modellification processes, and data protection by design. He has developed critical frameworks like 'Welt → Modell → Technik → Welt' for analyzing how power structures become embedded in sociotechnical systems. His work often takes historical perspectives on data protection debates while addressing contemporary challenges in technology governance. Analysis of his recent publications reveals a strong focus on translating data protection principles into practical applications, particularly in healthcare contexts like nursing care and pandemic response. His research increasingly addresses AI governance challenges, examining large language models' impact on scientific practices and developing legal design patterns for law-technology translation. He advocates for freedom-preserving approaches to digital governance and information separation of powers in digital environments. Pohle serves as a board member of the Working Group on Digitalization as a Challenge for Sociological Theory within the German Sociological Society. He has contributed to significant research projects including 'Strengthening Digital Skills through Micro-Credentials,' 'DUCAH @ HIIG,' and 'Making Repositories and AI Systems Usable in Everyday Nursing Care.' He is a prolific organizer of the annual Interdisciplinary Workshop on Privacy, Data Protection & Surveillance and regularly contributes to policy discussions through media appearances in Handelsblatt, Leipziger Volkszeitung, and specialized podcasts examining the intersection of IT and law.

Christine Utzمشاهده پروفایل

استادیار

• Online Privacy
• Computer Security
• Law and Technology
• +۳ مورد دیگر

Christine Utz is an Assistant Professor in the Digital Security group at Radboud University, Nijmegen, Netherlands, affiliated with the interdisciplinary iHub research hub. Her work bridges computer security, privacy, and legal aspects of technology. She holds a Dr.-Ing. in Computer Science from Ruhr University Bochum, with a law degree from the University of Bayreuth. Her research focuses on empirical studies of online privacy, combining technical measurements with human-centered methods. Key areas include privacy risks in web/mobile ecosystems, usability of privacy-enhancing technologies, and the societal implications of tech-law intersections. She has collaborated internationally, including postdoctoral work at CISPA Helmholtz Center and visiting researcher roles in the U.S. and Sweden. Her work has been featured at leading venues like IEEE S&P, PETS, and CHI, addressing topics like cross-platform app analysis, ethical security research, and privacy policy impacts of GDPR/CCPA. She actively participates in program committees for major conferences (e.g., CHI, NDSS) and advises on cybersecurity education initiatives.

Thom Volkerمشاهده پروفایل

پژوهشگر

• Computational Social Sciences
• Methods and Statistics
• Statistical Learning
• +۵ مورد دیگر

Thom Volker is a PhD candidate in Methodology and Statistics at Utrecht University's Social Sciences faculty. His research focuses on computational social sciences, Bayesian statistics, and synthetic data creation for privacy-preserving research. He actively contributes to open science initiatives and collaborates with researchers like Dr. Erik-Jan van Kesteren, Dr. Peter-Paul de Wolf, and Prof. Dr. Stef van Buuren. Key research interests include Privacy-preserving synthetic datasets Statistical learning and Bayesian evidence synthesis Missing data imputation techniques R programming language applications Recent publications highlight his work with topic modeling in educational data analysis (2023), anonymous shareable datasets (2021), and Bayesian hypothesis evaluation. He maintains active GitHub repositories and advocates for open science tools like R packages and Quarto. Collaborative efforts extend to the RDocumentation community and the Our World in Data project. His methodological expertise bridges social science research with cutting-edge computational techniques.

Chih-hsing Hoمشاهده پروفایل

دانشیار

• Legal Aspects of Translational Medicine and Biobanking
• Big Data, Privacy and Data Protection Law
• AI Governance and Law
• +۲ مورد دیگر

Chih-hsing Ho is a tenured Associate Research Fellow (equivalent to Associate Professor) at the Institute of European and American Studies, Academia Sinica, Taiwan. Since February 2021 he has held a joint appointment at Academia Sinica’s Research Center for Information Technology Innovation (2021–2024) and has been a Research Fellow at the Centre for Medical Ethics and Law, University of Hong Kong since 2019. He also serves on several high-level government advisory committees in Taiwan, including the Advisory Committee on Digital Economy Development (Ministry of Digital Affairs, 2025–present) and the Technology Policy Advisory Committee (Ministry of Health and Welfare, 2023–present). Education Ph.D. in Law, London School of Economics and Political Science (LSE) J.S.M., Stanford Law School LL.M., Columbia Law School LL.B., College of Law, National Taiwan University Research Interests Dr. Ho’s scholarship integrates law, medicine, and emerging technologies. His core interests include: Legal aspects of translational medicine and biobanking – designing governance frameworks for large-scale genomic and health-data repositories. Big-data privacy and data-protection law – analysing GDPR compliance, cross-border data transfers, and secondary use of health data. AI governance and law – exploring regulatory strategies for generative AI in healthcare, algorithmic accountability, and human-robot interaction. Law, medicine and global health – comparative studies on genetic non-discrimination policies, data sovereignty, and equitable access. Law and anthropology – examining group-consent models informed by indigenous perspectives and socio-legal contexts. Publications Trajectory Between 2018 and 2024, Ho has published extensively on the intersection of data-protection law and biomedical research. His work traces an evolution from foundational analyses of GDPR challenges for biobanks (2018) to cutting-edge studies on generative-AI regulation (2024). Recurring themes include comparative governance of health-data infrastructures (FinnGen, NHS England, Taiwan Biobank), privacy-preserving legal architectures for AI, and consensus-building methodologies for genetic non-discrimination policies. His recent co-authored Delphi consensus statement in JAMA Network Open exemplifies his commitment to evidence-based policy design. Scientific Awards & Grants National Science and Technology Council Award for Outstanding Scholars, Taiwan (2024–2025; 2021–2022; 2019–2020) Ministry of Science and Technology Recruiting Outstanding Scholars Award, Taiwan (2014–2017) Academia Sinica Recruiting Outstanding Scholars Award, Taiwan (2014–2016) LSE Olive Stone Memorial Scholarship (2007–2009) LSE Morris Finer Award for Socio-Legal Studies (2007–2008) LSE Wedderburn Writing-Up Grant (2010–2012) Ministry of Education Studying Abroad Scholarship, Taiwan (2007–2009) Grants & Governance Roles Ho currently convenes the AI Governance Laboratory at IEAS (2023–present) and sits on more than a dozen national ethics and policy boards, including: Legal Affairs Committee, Academia Sinica (2025–present) Committee on Risk Assessment of Generative AI, Academia Sinica (2025–present) Ethics and Governance Council (EGC) of Taiwan Biobank (2019–present) Multiple Institutional Review Boards (IRB-BM, Academia Sinica; CDC Taiwan; Tri-Service General Hospital; Development Center for Biotechnology) Labs & Teams He leads the AI Governance Laboratory at the Institute of European and American Studies, fostering interdisciplinary collaboration among legal scholars, computer scientists, and medical ethicists to craft responsible AI policies. Previously, he convened the EU Core Research Group (2017–2020), promoting comparative European studies at Academia Sinica.

Michael Marshمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Networking
• Distributed Systems
• +۳ مورد دیگر

Michael Marsh is a Senior Lecturer in the Department of Computer Science at the University of Maryland. His roles include teaching courses such as Computer and Network Security (CMSC414), Computer Networking (CMSC417), and Advanced Data Structures (CMSC420). He has developed the Diptych application to assist in managing large courses on Canvas, offering cross-platform binaries and open-source code. His research interests span cybersecurity, distributed systems, and parallel programming, with a focus on peer-to-peer networks, load balancing, and cryptographic protocols. Marsh has advised students such as Cole Lashley on projects like Shellcode Compilers. His work includes contributions to desktop grid systems, trust management in distributed environments, and robust secret distribution mechanisms. He maintains an active GitHub repository for Diptych, addressing issues like group list rendering and grading reports. His publications reflect expertise in resource discovery, decentralized PKI, and network protocol optimization. Awards and grants are not explicitly listed, but his academic contributions are highlighted through his teaching innovations and open-source projects. He continues to develop educational materials, including free e-books for his courses, and supports students through detailed course expectations and supplementary resources.

Eun G. Parkمشاهده پروفایل

دانشیار

• Cloud Computing Trust
• Enterprise Content Management
• Ontology Modeling
• +۵ مورد دیگر

Eun G. Park is an Associate Professor at McGill University's School of Information Studies. She holds a PhD in Information Studies from UCLA, an MBA from the University of Pittsburgh, an MS in Library and Information Science from the University of Illinois, and a BA in English Literature from Pusan National University. Her research focuses on cloud computing trust, enterprise content management (ECM), ontology modeling, and organizational trust dynamics. She has advised four graduate students and contributes to interdisciplinary projects in digital humanities and cultural heritage preservation. Her research explores the intersection of technology and trust in organizational and governmental contexts, with notable work on open government data analytics and privacy-preserving cloud solutions. She has collaborated on data rescue initiatives for historical climate records and developed ontology models for cultural heritage collections. Park teaches courses in ECM systems, digital preservation, and electronic records management. Her work bridges technical and policy domains, addressing challenges in legal compliance for sensitive data protection and ICT-driven trust enhancement. She participates in international conferences on digital preservation (InterPARES Trust) and contributes to research labs including the Accessible Computing Technology Research Group and Data Mining & Security Lab. Notable collaborations include the DRAW project (historical climate data digitization) and SafePath (privacy-preserving transportation data publishing). Her research has been applied to policy evaluation in Ghana's e-government initiatives and economic impact analyses of digital repositories.

Samaneh Kouchakiمشاهده پروفایل

مدرس ارشد

• Biomedical signal processing
• Deep supervised/semi-supervised learning for healthcare data
• Graph learning and embedding for omics data
• +۵ مورد دیگر

Samaneh Kouchaki is a Senior Lecturer in Machine Learning for Healthcare at the University of Surrey, affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering. She leads research on machine learning applications in healthcare, particularly dementia care and antibiotic resistance. Her roles include teaching, supervision of 6 PhD students, and collaboration with the UK Dementia Research Institute. Education : PhD in Computer Science (University of Surrey, 2015), postdoctoral research at University of Oxford (TB genomics) and University of Manchester (bioinformatics). Research Interests : Focuses on healthcare AI, including biomedical signal processing, graph learning for omics data, and sensor-based remote monitoring. Develops machine learning tools for early disease detection and personalized care. Publications : Over 30 peer-reviewed articles in top journals like NPJ Digital Medicine and The Lancet Microbe , covering topics from antibiotic resistance prediction to dementia care technologies. Recent work emphasizes lightweight models for clinical NLP and unsupervised anomaly detection. Lab & Teams : Leads research in health AI at CVSSP, collaborating with UK DRI, Imperial College London, and NHS trusts. Develops TIHM platform for remote monitoring of dementia patients.

Isaac Wangمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Augmented and Virtual Reality
• Intelligent Virtual Agents
• +۳ مورد دیگر

Isaac Wang is an Assistant Professor of Computer Science at James Madison University (JMU), affiliated with the College of Integrated Science & Engineering. He holds a Ph.D. in Human-Centered Computing from the University of Florida (2022) and a B.S. in Computer Science from Seattle Pacific University (2015). His research focuses on Human-Computer Interaction (HCI), Augmented Reality (AR), Virtual Reality (VR), and intelligent virtual agents. He explores nonverbal communication in embodied agents, natural user interfaces, and gesture recognition in collaborative tasks. Education: Ph.D. Human-Centered Computing (University of Florida, 2022) B.S. Computer Science (Seattle Pacific University, 2015) Research interests include designing AR/VR systems for situational awareness, studying cross-cultural interactions with virtual agents, and developing multimodal authentication techniques. He teaches courses such as Algorithms and Data Structures (CS 240) and Application Development (CS 343). His work has been published in top conferences like CHI, IVA, and IUI, focusing on topics like gesture-based interaction, agent feedback strategies, and AR interface design. He advises no listed students but actively collaborates with institutions like Offenburg University (Germany) and maintains a lab focused on AR/VR applications. His GitHub repositories include tools for Kinect data analysis and AR system development.

Rebecca Frankمشاهده پروفایل

استادیار

• Digital Curation
• Digital Preservation
• Digital Repositories
• +۶ مورد دیگر

Rebecca Frank (she/her) is an Assistant Professor at the University of Michigan School of Information and holds affiliations with the Einstein Center Digital Future (Berlin) and the Inter-university Consortium for Political and Social Research. Her research examines how risk is socially constructed in digital repository certification processes, focusing on open data, digital preservation, and ethical information management. She teaches graduate courses in digital curation and preservation. PhD, University of Michigan School of Information (2018) MSI, University of Michigan (2012) BA, Organizational Studies, University of Michigan (2010) Her work addresses social and ethical barriers to digital information preservation, sharing, and reuse through projects like SCoR-TDR (Social Construction of Risk in Trustworthy Digital Repositories) and Citizen-Based Monitoring for Peace & Security (analyzing satellite imagery for conflict verification). Current research with the InfraLab Berlin investigates risk perception in public infrastructure data work. Recent publications (2024-2025) explore CoreTrustSeal certification, satellite image analysis for citizen monitoring, and qualitative method reporting standards. Her 2024 Journal of Documentation article develops frameworks for repository risk assessment, while 2025 conference papers examine feminist data approaches and collaborative syllabus development. Supported by grants from the National Science Foundation (US), German Foundation for Peace Research, Einstein Center Digital Future, and Australian Academy of Science, Frank collaborates with international institutions including Princeton University, Berliner Hochschule für Technik, and Humboldt-Universität zu Berlin. She organizes workshops on data feminism and digital preservation ethics.

Ke Yiمشاهده پروفایل

استاد

• Database theory and systems
• Query processing
• Data security and privacy
• +۷ مورد دیگر

Ke Yi is a Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), where he also serves as Director of the MSc Program in Big Data Technology. His research spans database theory and systems, query processing, data security and privacy, parallel and distributed algorithms, and computational geometry, with a focus on bridging theoretical guarantees with practical implementations. Dr. Yi earned his B.Eng. in Computer Science and Technology from Tsinghua University (1997-2001) and his Ph.D. in Computer Science from Duke University (2001-2006), advised by Professors Pankaj K. Agarwal and Lars Arge. Before joining HKUST in 2007, he was a Research Specialist at AT&T Labs-Research (2006-2007). His research interests center on database theory and systems with particular emphasis on query processing techniques, data security and privacy mechanisms, and efficient algorithms for big data. Yi's work consistently demonstrates the rich interdependence between theoretical foundations and practical implementations, favoring simple algorithms with elegant analyses that provide valuable insights for real-world applications. His research group has developed several notable system prototypes including Quorion (query optimization), DPSQL (differentially private SQL), SparkSQL+ (next-generation query planning), SecYan (secure query processing), CROWN/Cquirrel (continuous query processing), and XDB (online aggregation). Yi's publication record shows a clear evolution toward privacy-preserving database technologies, particularly differential privacy, with an increasing focus on practical implementations that maintain theoretical guarantees. His recent work has centered on query processing under differential privacy, secure multi-party computation for databases, and efficient algorithms for big data analytics, demonstrating consistent contributions to both theoretical foundations and practical systems. ACM SIGMOD Best Paper Award (2016, 2022) ACM PODS Test-of-Time Award (2022) ACM Distinguished Member (2021) Multiple ACM SIGMOD Best Paper Honorable Mentions Google Faculty Research Award (2010) HKUST School of Engineering Young Investigator Research Award (2012) Professor Yi has supervised numerous Ph.D. and MPhil students, many of whom have gone on to prestigious academic and industry positions at institutions including Nanyang Technological University, University of Waterloo, EPFL, Alibaba Cloud, and Google. His research has been generously supported by Hong Kong RGC, Alibaba, Huawei, ByteDance, Microsoft, and Google. In addition to his research leadership, Yi has made significant contributions to the academic community through editorial roles (ACM TODS, IEEE TKDE), program committee chairs (PODS 2026, ICDT 2021), and numerous service roles in top database conferences. His laboratory, the HKUST Database Lab (hkustDB), has become a leading center for database research in Asia, developing innovative prototypes that bridge theoretical database research with practical implementations. The lab maintains active GitHub repositories for their open-source projects and collaborates extensively with both academic and industry partners worldwide.

Silvia Llorente Viejoمشاهده پروفایل

استادیار

• Computer Science
• Computer Architecture
• Telecommunications Engineering
• +۵ مورد دیگر

Assistant Professor at the Polytechnic University of Catalonia 's Barcelona School of Informatics , specializing in Computer Architecture and Genomic Data Security . Research focuses on: Secure genomic information representation (MPEG-G, FAIR principles) Medical device cybersecurity (MedSecurance project) Privacy-preserving health data systems (HIPAMS, GIPAMS) Digital rights management for multimedia and health content Watermarking techniques for data leak detection Recent publications address cybersecurity challenges in interconnected medical devices, reversible fingerprinting for genomic data, and compliance with international standards like ISO/IEC 23092. Key collaborators include researchers from health informatics and multimedia standardization fields.

• Image Compression
• Machine Learning
• Visual Media Processing
• +۷ مورد دیگر

Jona Ballé is an Associate Professor in the Electrical and Computer Engineering department at New York University's Tandon School of Engineering. His research focuses on developing efficient representations of visual media through machine learning and end-to-end optimization techniques. Dr. Ballé's research interests center on visual media compression, spanning still images, video, augmented reality, virtual reality, plenoptic imaging, and holographic imaging. His work bridges information theory, computer vision, and machine learning to develop perceptually optimized compression algorithms. He has made significant contributions to understanding the relationship between human visual perception and image statistics, which has led to improved compression results and ultimately contributed to the JPEG AI standard finalized in 2025. His recent publications demonstrate a strong trend toward Wasserstein distortion metrics, neural compression architectures, and rate-distortion optimization. These works span computer vision, information theory, and signal processing domains, with applications in both traditional and emerging visual media formats. His research shows consistent innovation in developing perceptually relevant metrics that balance fidelity and realism in compressed media. Contributed to JPEG AI standard (2025) Co-organizer of Challenge on Learned Image Compression (CLIC) since 2018 Program committee member of Data Compression Conference (DCC) since 2022 Reviewer for top-tier publications including NeurIPS, ICLR, ICML, and IEEE Transactions journals Dr. Ballé has advised numerous graduate students who have co-authored significant publications with him, particularly in the areas of neural compression and perceptual metrics. His research has been supported by institutions including the Simons Foundation. He maintains active collaborations across academia and industry, with his work at Google (2017-2024) directly informing his current academic research. His laboratory focuses on developing open-source implementations of advanced compression techniques, with notable GitHub repositories including Wasserstein Distortion implementation in PyTorch and CoDeX (Learned data compression in JAX), demonstrating his commitment to reproducible research and community engagement.

Cong Shenمشاهده پروفایل

دانشیار

• Machine Learning
• Wireless Communications
• Federated Learning
• +۷ مورد دیگر

Cong Shen is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Virginia, where he leads a research group focused on machine learning, wireless communications, and networking. He is affiliated with the UVA Link Lab and serves as Deputy Director of Collaboration at SpectrumX, an NSF Spectrum Innovation Center. He has previously held faculty positions at the University of Science and Technology of China (USTC) and maintains strong industry ties with companies such as Qualcomm, SpiderCloud Wireless, Silvus Technologies, and Xsense.ai. Education: B.E. and M.E., Electronic Engineering, Tsinghua University, China Ph.D., Electrical Engineering, University of California, Los Angeles (UCLA), USA His research lies at the intersection of machine learning and communication systems, with a focus on in-context learning, transformers, federated learning, reinforcement learning, distributed optimization, multi-armed bandits, and AI for wireless . His work aims to bridge theoretical foundations with engineering applications in next-generation wireless networks and intelligent systems. His recent publications (2023–2025) reveal a strong trend toward integrating foundational models with communication constraints, particularly in federated and decentralized settings. Key themes include in-context learning with provable guarantees, efficient prompt optimization using bandit methods, privacy-preserving federated learning, and reinforcement learning for wireless resource management. His work frequently appears in top-tier venues such as NeurIPS, ICML, ICLR, AISTATS, and IEEE ICC. Scientific Awards: NSF CAREER Award (2022) Best Paper Award, IEEE ICC (2021) Excellent Paper Award, ICUFN (2017) Best Paper of 2024, Science Robotics Finalist for Best Student Paper Award, Asilomar (2024) Dr. Shen advises a dynamic group of graduate and undergraduate students, including PhD candidates Chengshuai Shi, Zhoubin Kou, Di Wu, and others. He leads multiple NSF-funded projects, including initiatives under the SWIFT, ECCS Core, MLWiNS, and CAREER programs, focusing on spectrum access, resource rationing in wireless FL, and domain-knowledge-enriched RL for network optimization. His lab actively contributes to open science through GitHub repositories and code releases. He also serves as an associate or editor for several IEEE Transactions journals and participates in program committees of major AI and communications conferences.

Michele Rutaمشاهده پروفایل

استاد

• Pervasive Computing
• Ubiquitous Web
• Knowledge Representation Systems
• +۷ مورد دیگر

Michele Ruta serves as Full Professor in the Department of Electrical and Information Engineering at Polytechnic University of Bari, specializing in pervasive computing systems and semantic technologies. His academic journey began with a Master's in Electronics Engineering from the same institution in 2002, followed by a Ph.D. in Computer Science in 2007. His research spans pervasive computing, ubiquitous web systems, knowledge representation, and wireless ad-hoc networks, with recent focus on blockchain integration for IoT, cloud-edge intelligence, and semantic web applications. Key contributions include the COWL ontology framework and Tiny-ME reasoning engine for resource-constrained devices. Analysis of his 15 most recent publications reveals strong emphasis on semantic technologies for smart mobility (2025), AI-driven logistics optimization (2025), and edge-based reasoning systems (2024). His work consistently bridges theoretical knowledge representation with practical applications in smart cities, healthcare, and industrial IoT. As editorial board member for relevant journals and Program Committee participant in international conferences, he actively shapes research directions in his fields. His technical leadership extends to national and international research projects focusing on semantic web infrastructure and IoT integration. Professor Ruta maintains active contributions through his personal research platform and IRIS publication repository, with 178 publications demonstrating sustained scholarly output since 2007. His laboratory work focuses on practical implementations of semantic reasoning for cyber-physical systems, particularly in cloud-edge continuum architectures.

Hongyi Zhuمشاهده پروفایل

استادیار

• Artificial Intelligence
• Cybersecurity
• Mobile Health Analytics
• +۶ مورد دیگر

Dr. Hongyi Zhu serves as an Assistant Professor in the Department of Information Systems and Cyber Security at the Alvarez College of Business, The University of Texas at San Antonio (UTSA), leveraging his Ph.D. from the University of Arizona and Bachelor's from Tsinghua University to advance interdisciplinary research at the AI-health-security intersection. Education: Ph.D. in Management Information Systems, University of Arizona Bachelor of Business Management, Tsinghua University His research pioneers artificial intelligence-based analytics for mobile health, mental health, and cybersecurity, utilizing deep learning and network analysis to address critical challenges in proactive threat intelligence and senior care. Publications span premier venues including MIS Quarterly and IEEE Transactions on Knowledge and Data Engineering, reflecting his multidisciplinary approach bridging computer science, information systems, and biomedical informatics. Analysis of his 15 most recent publications reveals a dominant focus on applying graph embedding and federated learning to cybersecurity vulnerabilities (40% of works), depression detection via sensor fusion (25%), and privacy-preserving AI frameworks (35%), with consistent methodology innovation in transfer learning and uncertainty quantification across health and security domains. Dr. Zhu teaches Computer Networking and Telecommunication Systems while maintaining active memberships in IEEE, ACM, AIS, and INFORMS, demonstrating commitment to both academic rigor and professional community engagement.