Find Academic Supervisors

William MarcellinoView profile

Professor

• Artificial Intelligence
• National Security
• Social Media Analysis
• +5 more

William Marcellino is a Senior Behavioral and Social Scientist at the RAND Corporation and a Professor of Text Analytics at the RAND School of Public Policy. He also serves as a lecturer at Carnegie Mellon University, where he earned his Ph.D. in rhetoric. His academic and research roles focus on AI applications, such as developing RAND-Lex, a proprietary text analytics tool, and teaching NLP and qualitative methods. Marcellino’s research expertise spans information operations, national security, social media analysis, and military technology. He previously served as a U.S. Marine tank officer and enlisted rifleman, informing his work on military resilience and defense-related AI acquisition. His recent work addresses AI’s role in disinformation, China’s AI exports, and ethical frameworks for influence operations. Education: Ph.D. in Rhetoric, Carnegie Mellon University Key Projects: RAND-Lex development, China AI Export Database, command narrative frameworks Notable Contributions: Analysis of social media’s role in conflict (e.g., Israel-Hamas, Ukraine-Russia), counter-radicalization strategies, and AI ethics.

Maria Lilla' MontagnaniView profile

Professor

• Intellectual Property Law
• Competition Law
• Digital Copyright
• +2 more

Maria Lilla' Montagnani is a Full Professor of Commercial Law at Bocconi University and Director of the LL.M in European Business and Social Law (EBSL). She holds a Law Degree from Università Cattolica del Sacro Cuore (Milan), an LL.M from Queen’s University of London, and a PhD in Competition Law from Università degli Studi di Perugia. Her research focuses on AI governance, intellectual property law, digital copyright, competition law, and public art law. Educational Background: Law Degree: Università Cattolica del Sacro Cuore (Milan) LL.M: Queen’s University of London PhD in Competition Law: Università degli Studi di Perugia Her research interests emphasize the intersection of technology, law, and commerce. Notable areas include regulatory frameworks for AI, digital copyright challenges in the music and art sectors, and antitrust implications of IP misuse. Recent publications explore open patenting models, legal protection for art exhibitions, and the role of internet intermediaries in fostering creativity. No scientific awards have been explicitly mentioned in the provided texts. She teaches advanced courses such as "European and International Advanced IP Law," "Cyber Risk and Data Protection Law," and "Artificial Intelligence and the Law." No advising/grants information or lab affiliations were found in the text.

Xiaoning DuView profile

Senior Lecturer

• Software Engineering
• Artificial Intelligence
• Cybersecurity
• +3 more

Xiaoning Du is a Senior Lecturer (equivalent to Associate Professor) in the Department of Software Systems & Cybersecurity at Monash University's Faculty of Information Technology. She holds a PhD from Nanyang Technological University (2020) and a Bachelor's from Fudan University (2014). Her research focuses on software security and quality assurance for traditional and AI-based systems, with notable contributions to DevOps for AI, vulnerability detection, and runtime verification. Education: PhD in Computer Science, Nanyang Technological University (2015–2020) Bachelor of Software Engineering, Fudan University (2010–2014) Research Interests: Security of intelligent software systems, AI-driven software testing, DevOps for AI, and trustworthy AI services . Her work emphasizes practical applications like Devign (vulnerability detection), DeepStellar (deep learning system analysis), and BigCodeBench (code generation benchmarking). Recent Projects: Collaborations include CSIRO cybersecurity initiatives, Algorand Center of Excellence, and IBM-funded research. She leads projects addressing AI ethics, federated learning security, and code completion robustness. Awards: 2024 Google Research Scholar Award, 2024 FIT Dean’s Early Career Award, and multiple distinguished paper awards at top venues like ACM SIGSOFT and ICLR. Labs/Teams: Active in Monash’s cybersecurity and AI research groups, contributing to open-source tools like DeepStellar and BigCodeBench . She advises PhD students and mentors on scholarships.

• Digital Innovation
• Crowdsourcing
• Data-Driven Decision-Making
• +3 more

Michael Zaggl is a Full Professor at NEOMA Business School, specializing in Strategy & Entrepreneurship. He holds a Habilitation from TU Munich, a PhD from TU Hamburg, and degrees from the University of Koblenz. Previously, he served in tenured roles at Aarhus University and held visiting positions at Harvard, USC, and other institutions. His research focuses on digital innovation, crowdsourcing, and data-driven decision-making, with publications in top journals like Strategic Management Journal and Academy of Management Discoveries . He is an active member of professional organizations such as the Academy of Management and Association for Information Systems. Educations: Habilitation (TU Munich), PhD (TU Hamburg), Master's & Bachelor's (University of Koblenz). His research interests span digital innovation , crowdsourcing mechanisms , and open-source governance , with notable contributions to understanding distributed strategic search and organizational decision-making. Recent work examines crowdfunding dynamics , AI-human collaboration , and privacy calculus in mHealth . His articles analyze topics like crowd wisdom optimization and hierarchical biases in idea evaluation , reflecting a focus on practical applications for organizations. He has presented at leading conferences, including the Academy of Management and Hawaii International Conference on System Sciences.

Mathias CzaikaView profile

Professor

• International Migration
• Globalization
• Migration Policy
• +4 more

Mathias Czaika is a University Professor in Migration and Integration and Head of the Department for Migration and Globalisation at the University for Continuing Education Krems, Austria. He coordinates the PhD Programme in Migration Studies and has previously directed the International Migration Institute at Oxford University. His research focuses on international migration drivers, policy impacts, globalization, and development implications. Education: Master’s in Economics from the University of Konstanz, PhD in Political Economy from the University of Freiburg. He has advised national and international bodies like the EU, DfID, and the Swedish Migration Agency. Research interests include migration policy formulation, asylum systems, skilled migration, and involuntary immobility’s development impacts. Key projects include 'Valuing Open Source Intelligence' (ÖAW-funded) and 'Development implications of involuntary immobility in Africa' (FWF-funded). He leads initiatives on migration scenarios, diaspora engagement, and integration policies. Grants and funding include EU Horizon 2020, FWF, and national grants. Notable publications address migration governance, diaspora organizations, and climate change effects on mobility. His work bridges academic research with policy through dialogues between science, politics, and practice.

Emmanuel GiguetView profile

Researcher

• Computer Science
• Computer Forensics
• Natural Language Processing
• +4 more

Emmanuel Giguet is a Researcher at the French National Centre for Scientific Research (CNRS), affiliated with the GREYC Laboratory (Computer Science Department) at the University of Caen, Normandy, France. He holds an HDR (Accreditation to Supervise Research) from the University of Caen (2011) and a PhD in Computer Science (1998), both focusing on multilingual natural language processing and document analysis. Roles: Cybersecurity Researcher, Communication and Scientific Mediation Adviser (GREYC Lab), Former Computer Forensics Legal Expert (2005–2015). Expertise: Digital Forensics, Natural Language Processing, Document Structure Analysis, Open Source Intelligence, Competitive Intelligence. His research spans cybersecurity, digital forensics, and NLP applications in document analysis. He co-founded Semiotime (2012), a Competitive Intelligence startup, and has contributed to tools like the GREYC Digital Investigation Platform (G'DIP). He teaches cybersecurity modules at the University of Caen, emphasizing digital forensics and information retrieval. Key Projects: Development of open-source digital investigation tools (e.g., G'DIP). Analysis of deepfake videos and forensic video forgery detection. PDF document structure extraction for financial narrative processing (FinTOC). Affiliations: GREYC Lab (CNRS UMR 6072), University of Caen. Institute for Information Sciences, University of Caen. Communication: Advises GREYC on branding and media presence, including logo guidelines, virtual backgrounds, and scientific mediation resources.

Christian MuiseView profile

Assistant Professor

• Artificial Intelligence
• Automated Planning
• Machine Learning
• +6 more

Christian Muise is an Assistant Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. He holds a PhD (2014) in Artificial Intelligence from the University of Toronto, where he was advised by Sheila McIlraith and J. Christopher Beck. His research focuses on automated planning under uncertainty, combining planning with learning for applications like goal-oriented dialogue systems and multi-agent coordination. He previously held postdoctoral roles at the University of Melbourne's Agentlab and MIT's CSAIL, and was a Research Staff Member at the MIT-IBM Watson AI Lab. Education: PhD in Artificial Intelligence, University of Toronto (2014) MSc in Computer Science, University of Toronto (2009) BSc in Computer Science, Carleton University (2007) Research Interests: His work bridges automated planning and machine learning, emphasizing robustness in uncertain environments. Key areas include non-deterministic planning, model acquisition with large language models (LLMs), and human-aware planning. He explores applications in healthcare (e.g., treatment response prediction), robotics (autonomous navigation), and dialogue systems for safety-critical domains. Current projects include developing explainable planning systems and mitigating bias in AI decision-making. Awards: Scotiabank Scholar (Scotiabank Centre for Customer Analytics) Advising & Labs: Leads the Mu Lab, supervising PhD and Master's students in topics like model acquisition, dialogue systems, and planning bias. Active in open-source tools (e.g., L2P, MACQ library) to democratize planning research. Collaborates on projects like PRP Rebooted and FixMyPlan to advance FOND planning and LLM integration. Labs/Teams: Mu Lab at Queen’s University, focusing on planning under uncertainty, AI safety, and neuro-symbolic systems.

Risteski AleksandarView profile

Professor

• Telecommunications
• Cybersecurity
• Blockchain Technology
• +5 more

Professor Risteski Aleksandar holds a Ph.D. in Telecommunications and has been a faculty member at the Faculty of Electrical Engineering and Information Technologies, University Ss. Cyril and Methodius, since 1996. He currently serves as a Professor and has held roles including Vice-Dean for Science and International Cooperation (2008–2016). His research focuses on telecommunications, cybersecurity, blockchain applications in education, and IoT security. He has extensive industry experience, including internships at IBM T.J. Watson Research Center in the U.S. Education: Ph.D. in Telecommunications (2001–2004) M.Sc. in Telecommunications (1997–2000) Dipl. Ing. in Electronics and Telecommunications (1991–1996) Research Interests: Blockchain technology for academic credential verification Cybersecurity in tactical communication systems IoT energy efficiency and reliability Network intrusion detection using deep learning Anti-forgery systems using AI and blockchain Publications Trends: Over 50 publications since 2000, with recent focus on blockchain applications in education, cybersecurity frameworks for 5G networks, and IoT energy optimization. His work bridges theoretical advancements with practical implementations in telecommunication infrastructure. Awards: No specific awards listed, though his contributions to blockchain-based educational systems and cybersecurity research are notable. Advising & Grants: No listed students, but active in research projects funded by industry partnerships. Collaborations include IBM Research and initiatives in Macedonia's telecommunication sector. Labs/Teams: Affiliated with the Institute of Telecommunications at his faculty, focusing on network security and emerging technologies.

Jonathan GrayView profile

Professor

• Digital Humanities
• Critical Infrastructure Studies
• Data Journalism
• +6 more

Jonathan Gray is a Reader in Critical Infrastructure Studies at King’s College London’s Department of Digital Humanities, affiliated with the Public Data Lab, Digital Methods Initiative, and Sciences Po’s médialab. His research focuses on digital data’s role in shaping collective life, with expertise in critical technical practice, environmental data studies, and public data portals. He co-leads projects like SUPERB (forest restoration) and datatesting (data infrastructure evaluation). His work spans environmental policy, digital methods, and social media analysis, addressing topics like algorithmic accountability, climate activism, and misinformation. Notable awards include the Activist-in-Residence and Being Human, Being Visible distinctions. Collaborative grants include ESRC, EC, and AHRC funding for projects on infodemics, East Asian heritage, and forest governance. Gray’s teaching emphasizes engaged research and digital tools like KingsCAT for social media analysis. Key labs include the Public Data Lab and forestscapes, exploring soundscapes and participatory inquiry into forest ecosystems. His 80+ publications include books on data journalism and scholarly communication infrastructures, reflecting his interdisciplinary approach to digital humanities.

Lutz PrecheltView profile

Professor

• Software Engineering
• Empirical Software Engineering
• Agile Processes
• +5 more

Lutz Prechelt is Professor of Informatics at Freie Universität Berlin where he heads the Software Engineering research group (AG SE). His academic journey spans theoretical foundations, industry experience, and extensive empirical research in software engineering. He has held significant administrative positions including Executive Director of the Institute of Informatics and member of various academic senates. Prechelt's research interests have evolved from artificial intelligence and neural networks to empirical software engineering, with a recent focus on qualitative approaches to Agile processes and Pair Programming. His work has consistently emphasized methodological rigor, having conducted pioneering controlled experiments on topics like type-checking, inheritance depth, design patterns, and the Personal Software Process. His research has also ventured into plagiarism detection (JPlag), melody recognition, and qualitative studies of pair programming dynamics. His publication record shows a clear transition from quantitative to qualitative approaches in empirical software engineering. Early work featured controlled experiments on specific technical aspects of software development, while more recent publications focus on qualitative analysis of team dynamics, agile methodologies, and research methodology itself. A notable thread throughout his career has been platform comparison studies (Plat_Forms) examining how different technologies affect development outcomes. Ernst Denert Software Engineering Award 2020 Prechelt has served extensively as reviewer for major funding agencies including DFG, EU, ERC, and BMBF, and as editor for journals like Journal of Universal Computer Science and Transactions on Pattern Languages of Programming. He founded the Forum for Negative Results (FNR) to address publication bias in scientific research. His research group has developed several notable software tools including Saros for distributed pair programming, JPlag for plagiarism detection, and tools for maintainable lecture videos.

Stephan SchlöglView profile

Professor

• Human-Computer Interaction (HCI)
• Artificial Intelligence
• Interaction Design
• +6 more

Stephan Schlögl is a Full Professor at MCI - The Entrepreneurial School in Austria, leading research and teaching in Human-Computer Interaction (HCI), Artificial Intelligence (AI), and Information Systems. He holds editorial roles at journals like the Springer Discover Artificial Intelligence Journal and MDPI Multimodal Technologies and Interaction Journal. His work spans over two decades, with key roles including Postdoctoral Research Fellow at Télécom ParisTech (2012–2013) and PhD Researcher at Trinity College Dublin (2008–2012). Schlögl’s research focuses on HCI, AI-driven conversational systems, and assistive technologies for aging populations. Education includes a PhD in Computer Science from Trinity College Dublin, an MSc in Human-Computer Interaction from University College London, and a Mag.(FH) in Applied Informatics & Management from MCI. His teaching spans Software Engineering, Business Intelligence, and Research Methods. Research interests emphasize natural language interfaces, AI ethics, and technology’s societal impact. Notable projects include the EU-funded EMPATHIC initiative (2017–2021), developing an empathic virtual coach for elderly care, and the CRYSTAL project (2024–present) on conversational systems for emotional support. Schlögl has supervised numerous theses on AI applications, chatbots, and UX design. He co-organized major conferences like CUI (Conversational User Interfaces) and received awards for best papers in AI-HCI and CHIRA. His work bridges academia and industry, addressing challenges in AI adoption, digital well-being, and ethical technology design.

Marco ScialdoneView profile

Lecturer

• Information Technology Law
• Copyright Law
• Artificial Intelligence Ethics
• +3 more

Marco Scialdone is a Lecturer at the European University of Rome, specializing in Information Technology Law and Legal Categories related to Technology. He holds a PhD from the same university (2016) and Master’s degrees from La Sapienza University of Rome (2005). A Fellow of the IAIC – Italian Academy of the Internet Code, he focuses on AI ethics, digital rights, and consumer protection law. His research bridges legal frameworks with emerging technologies like artificial intelligence and robotics. Education: PhD in Legal Categories and Technology (2016), European University of Rome Master’s in Computer Law (2005), La Sapienza University of Rome LL.B. in Law (Cum Laude, 110/110), LUISS Guido Carli University Research Interests: His work explores the intersection of law and technology, including AI governance, digital copyright, consumer rights in the digital age, and the legal implications of robotics. He emphasizes ethical AI development, regulatory challenges in digital markets, and public administration digitization. Recent studies analyze algorithmic bias in judicial systems and the legal protection of user-generated content. Publications & Expertise: Authored over 20 publications, including monographs like Living with Artificial Intelligence (2021) and The Legal Protection of User-Generated Content (2018). His articles address topics such as non-human creativity rights, predictive justice models, and cross-border copyright conflicts. He has served as an expert in AI policy (Italian Government’s High-Level Group on AI, 2018) and on the Permanent Advisory Committee for Copyright (2019). Professional Roles: Head of Litigation & Academic Outreach at Euroconsumers (since 2021) Former lecturer at La Sapienza University (2015–2018) and University of Perugia (2007–2012)

Zhiyong ZhangView profile

Professor

• Bayesian methods
• Network analysis
• Big data analysis
• +4 more

Zhiyong (Johnny) Zhang is a Professor of Quantitative Psychology at the University of Notre Dame and serves as the Quantitative Area Director. He is the director of the Lab for Big Data Methodology and a Fellow at the Institute for Educational Initiatives. His primary research areas include Bayesian methods, network analysis, structural equation modeling, and statistical computing. Dr. Zhang holds a Ph.D. in Quantitative Psychology from the University of Virginia. His work focuses on developing advanced statistical methodologies and software for education, health, management, and psychology. He is the Editor of the Journal of Behavioral Data Science and an Associate Editor of Multivariate Behavioral Research. His research trends span Bayesian inference, neural network applications, mediation analysis, and methodological innovations in factor analysis and latent growth models. He has contributed to software tools like WebPower for statistical power analysis and RAMpath for structural equation modeling. Zhang has received prestigious awards, including Fellow status in the American Psychological Association and election to the Society of Multivariate Experimental Psychology. His advising and grants include mentoring students in quantitative methodologies and leading research on text mining, emotion recognition, and big data applications. The Lab for Big Data Methodology under his direction advances interdisciplinary statistical approaches.

• Microwave Imaging
• Dielectric Properties
• Antenna Engineering
• +5 more

Mehmet Çayören is a Professor at Istanbul Technical University in the Department of Electronics and Communication Engineering. His research focuses on applying microwave imaging techniques to medical diagnostics, particularly for early detection of breast cancer. He leads the development of the SAFE (Screening and Early Detection) microwave imaging system, which has shown promising results in clinical investigations. His work bridges electrical engineering, biomedical applications, and machine learning for improved cancer screening. Dr. Çayören's research interests span multiple domains within electromagnetic applications for medical imaging: Microwave imaging systems for breast cancer detection and screening Determination of dielectric properties of biological tissues Development of open-ended coaxial probe techniques for material characterization Application of machine learning algorithms (XGBoost, SVM) to enhance medical imaging Monitoring of intracerebral hemorrhage using microwave imaging Development of tissue-mimicking phantoms for medical device validation His recent publications demonstrate a strong trend toward integrating advanced machine learning techniques with microwave imaging systems to improve diagnostic accuracy. The SAFE platform represents a significant advancement in non-invasive breast cancer screening, particularly for dense breast tissue where traditional mammography has limitations. His work also extends to neurological applications, with research on microwave-based monitoring of brain hemorrhages. Scientific awards received by Dr. Çayören include: Teknoloji Ödülü (Technology Award) in 2014 Dr. Çayören has supervised 24 students and leads multiple research projects funded by various sources including TUBITAK. His current projects focus on microwave imaging systems for breast cancer screening, monitoring of intracerebral hemorrhage, and hardware design for microwave imaging systems. He collaborates extensively with medical professionals to validate his imaging systems in clinical settings. Dr. Çayören leads a research group focused on microwave imaging applications in medicine. His team develops both hardware systems (like the SAFE platform) and advanced signal processing algorithms to improve medical diagnostics. The group maintains close collaborations with hospitals for clinical validation of their imaging systems.

• Video Coding
• Machine Learning in Video Compression
• Energy-Efficient Encoding
• +3 more

Alexandre Mercat is an Assistant Professor in the Department of Computer Engineering at Tampere University, within the Faculty of Information Technology and Communication Sciences. His research focuses on video coding, energy-efficient encoding, and real-time multimedia systems. He leads projects on open-source video encoders and standards, including contributions to HEVC, VVC, and V-PCC technologies. His work emphasizes machine learning integration, low-power hardware optimizations, and scalable distributed encoding frameworks. Key technical interests include improving video compression efficiency through algorithmic innovations, developing open-source tools like the UVG dataset and Kvazaar encoder, and addressing challenges in volumetric video communication and 3D point cloud encoding. His research spans theoretical algorithm design to practical implementations, with applications in virtual reality, live streaming, and edge computing. Recent projects include real-time saliency-guided video coding frameworks, energy reduction techniques for HDR streaming, and multi-layer VVC coding schemes for hybrid machine-human consumption. He also explores FPGA acceleration and parallelization strategies for distributed video encoding systems. No scientific awards are explicitly mentioned in the provided texts. While no formal advisees are listed, his research group likely involves students through open-source development and collaborative projects. His work integrates closely with industry standards bodies and open-source communities, emphasizing reproducible evaluation frameworks and end-to-end software tools.