جستجوی استاد راهنمای دانشگاهی

Peter Fisher is an Assistant Professor of Organizational Behavior and Human Resource Management at Ryerson University's Ted Rogers School of Management. His research specializes in bias reduction in hiring, workplace diversity, and evidence-based recruitment practices. He also serves as webmaster for the Canadian Society for Industrial Organizational Psychology. Research Focus Fisher investigates personnel selection methodologies, cross-cultural personality differences, and HR decision-making. Key areas include: Gender and cultural bias mitigation in employment references Personality assessment validity (e.g., forced-choice testing) Technology's role in modern recruitment Publications His 9 most recent articles (2017–2021) emphasize industrial-organizational psychology, with recurring themes of psychometrics, cross-national comparisons, and HR analytics. Work frequently appears in journals like Personality and Individual Differences and the International Journal of Selection and Assessment . Awards & Grants SSHRC Insight Development Grant (2020–2021): $50,574 for tech startup hiring research Lazaridis Seed Grant (2019–2020): $10,000 2017 Best Master’s Thesis Award (HR Research Institute) 2017 Best Student Poster, Second Prize (Canadian Society for I/O Psychology) Teaching & Service He teaches MHR 623: Recruitment and Selection and contributes to academic societies, enhancing industry-academia collaboration in HR practices.

• Recruitment
• Selection
• Workplace Diversity
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Rebekah Overdorf is a Research Professor and Chair for Security and Trustworthiness of Online Information at Ruhr University Bochum, where she leads research at the intersection of computer security, privacy, and information integrity. She is also affiliated with the Research Center for Trustworthy Data Science and Security. Her research focuses on identifying and mitigating threats to information integrity in online platforms, with particular expertise in detecting disinformation campaigns, analyzing propaganda distribution channels, and developing methods to protect user privacy. Overdorf's work combines data science and machine learning approaches with security expertise to address contemporary challenges in online information ecosystems. Analysis of her recent publications reveals a consistent focus on security and privacy challenges in social media environments, with particular attention to Telegram and Twitter platforms. Her research spans technical analysis of bot networks and propaganda distribution mechanisms, development of detection methodologies for disinformation campaigns, and creation of protective technologies that empower users against algorithmic manipulation. Over the past decade, her work has evolved from foundational research in stylometry and authorship attribution to more applied security challenges in contemporary social media ecosystems. Professor Overdorf has received recognition through publications in top security and social media conferences including USENIX Security, ICWSM, EuroS&P, and FAT*. Her collaborative research approach is evident in her extensive co-authorship network spanning multiple institutions across Europe and North America. Her advising and research activities focus on training the next generation of security researchers while developing practical solutions to pressing information integrity challenges. Through her leadership of the Chair for Security and Trustworthiness of Online Information, she oversees research projects that address real-world security threats while maintaining academic rigor. The Chair for Security and Trustworthiness of Online Information serves as a hub for interdisciplinary research, bringing together computer scientists, social scientists, and policy experts to develop comprehensive approaches to online security challenges. Current projects focus on detecting coordinated inauthentic behavior, analyzing state-sponsored disinformation campaigns, and developing privacy-aware technologies that maintain functionality while protecting user data.

• Computer Security
• Privacy
• Information Integrity
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Dr. Xiao Zhang is an Assistant Professor of Management Information Systems in the Department of Information Systems at Lamar University's College of Business. He teaches courses including MBA ERP, E-Commerce, Bachelors E-Commerce Design and Development, and Principles of Management Information Systems, demonstrating expertise across both business applications and technical aspects of information systems. His educational background includes a Ph.D. from the University of Texas San Antonio and an MBA from Pittsburgh State University. He is also a Microsoft Certified System Engineer, combining academic credentials with practical technical expertise. Dr. Zhang's research focuses on electronic word-of-mouth communication, cybersecurity, and information systems. His work examines consumer skepticism toward online reviews, trust formation in digital environments, and security challenges in information systems. He has developed measurement scales for assessing skepticism toward eWOM and has published extensively on deception detection in online user-generated content. His research spans from 2012 to 2020, with multiple publications in reputable journals including Computers in Human Behavior and IEEE Engineering Management Review. His academic service includes: Reviewer for journals including Information Systems Frontiers (2019-2021), The DATA BASE for Advances in Information Systems (2021), and Journal of Information, Communication & Ethics in Society (2020) Reviewer for conferences like AMCIS (2018) and IEEE ATC (2021) Active participation in departmental and college committees at Lamar University including the Accounting and Information Systems Chair Search Committee (2020) and MIS Faculty Search Committee (2019-2020) Faculty advisor for AIS Student Meeting Dr. Zhang also holds a patent for 'System and Method for Password Verification' (2012), demonstrating his applied research contributions to information security and practical implementation of his scholarly work.

• Electronic Word-of-Mouth
• Cybersecurity
• Information Systems
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Vincent Itier serves as a Lecturer at IMT Nord Europe, where he is affiliated with the CRIStAL research laboratory (UMR CNRS 9189). His office is located in Building ESPRIT, Scientific City, at the Villeneuve d'Ascq campus. He is a member of the SIGMA research team and actively contributes to the academic community through teaching, research supervision, and scholarly publications. Dr. Itier's research interests span across multimedia security, digital forensics, and machine learning, with particular emphasis on detecting and understanding image manipulations. His work addresses critical challenges in digital media authenticity, including deepfake detection, photomontage identification, and steganalysis. He investigates how machine learning techniques can be leveraged to improve robustness against increasingly sophisticated image manipulation methods, with applications in combating 'fake news' and verifying digital content authenticity. His publication record demonstrates a consistent focus on digital image forensics, with recent work exploring deep learning approaches for detecting splicing, analyzing noise residuals in deepfakes, and developing robust steganalysis techniques. His research shows a clear evolution from traditional image processing methods toward more sophisticated deep learning frameworks that can handle the complex challenges of modern digital media manipulation. Supervised Minh Thong Doi's thesis on 'Deepfake detection: combining noise and semantic features and improving generalization to new generators' Currently offering M2 Internships and Post-doc positions for 2025-2026 Leading research within the ANR TSIA CI2(IA) project which aims to develop new tools for detecting and understanding image manipulation Dr. Itier maintains an active research presence through his GitHub profile (vitier) and professional website, and can be contacted via email at vincent.itier@imt-nord-europe.fr for potential collaborations or research opportunities.

• Multimedia Security
• Digital Forensics
• Machine Learning
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Marcin Sawiński is a Researcher and Teaching Assistant at the Department of Information Systems within the Institute of Informatics and Quantitative Economics at Poznań University of Economics and Business. With expertise spanning computer and information sciences (75%) and management and quality studies (25%), his work focuses on developing AI tools for addressing misinformation and enhancing fact-checking processes. His primary research interests include: Artificial Intelligence applications for fake news detection Natural Language Processing techniques for misinformation analysis Machine learning approaches to credibility assessment Persuasion techniques detection in social media content Development of robust language models for fact-checking systems Analysis of political narratives during crisis events like pandemics Dr. Sawiński's recent publications (2023-2025) demonstrate a concentrated research trajectory focused on applying transformer-based models to misinformation challenges, particularly in Slavic languages. His work spans multiple dimensions of the fake news problem, from detection of persuasion techniques to cross-lingual transfer learning for check-worthiness assessment. He has been actively involved in international competitions like CheckThat! Lab at CLEF, where his team (OpenFact) has developed innovative approaches to fact-checking challenges, including adversarial text generation to test model robustness. His scientific contributions include 15 publications with over 50 citations, reflecting his growing impact in the field of AI for misinformation detection. His research often involves collaboration with colleagues including Krzysztof Węcel, Witold Abramowicz, and Ewelina Księżniak.

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
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Maria-Cristina Marinescu serves as a Lecturer in the Department of Mathematics and Data Analytics at IQS School of Management. With a strong foundation in computational methods and data analysis, she contributes significantly to both teaching and research within the institution. Her academic profile demonstrates consistent scholarly activity with 44 documented scientific production items spanning over two decades. Dr. Marinescu's research interests center on Machine Learning, Data Analytics, and Artificial Intelligence, with notable applications in epidemic modeling, wireless sensor networks, and programming models. Her work bridges theoretical computer science with practical applications addressing real-world challenges in public health, digital well-being, and medical diagnostics. The fingerprint analysis of her work shows strong emphasis on Machine Learning (100%), Learning Systems (100%), and Transportation Models (100%), with substantial contributions to Programming Models (84%) and Wireless Sensor Networks (77%). Her recent publications reveal a trend toward interdisciplinary research that combines computational methods with societal challenges. She has made significant contributions to epidemic modeling during the COVID-19 pandemic, developing methods for accurate incidence rate estimation in Spain and analyzing information gains from multiple epidemic model outputs. Additionally, her work extends to medical applications including research on ocular ischemia and glaucoma, as well as innovative approaches to automated metadata annotation using machine learning techniques. Dr. Marinescu actively leads and participates in multiple research projects including Uncovering patterns of unconscious reactions to fake content (as Principal Investigator), MobilePressure (focused on reducing children's smartphone exposure), and ADAMIQS: Applied Data Analytics and Modelling IQS . These projects demonstrate her commitment to addressing contemporary issues through data-driven approaches while mentoring students and collaborating with interdisciplinary research teams.

• Machine Learning
• Data Analytics
• Artificial Intelligence
• +۳ مورد دیگر

Johannes Münster is a Professor of Economics at the University of Cologne and an Associated Investigator in ECONtribute: Markets & Public Policy, a Cluster of Excellence under Germany’s Excellence Strategy. He is affiliated with the Center for Social and Economic Behavior (C-SEB) and the theme group Media and Democratic Publics at the Wissenschaftsforum zu Köln und Essen. His position integrates theoretical economics with real-world policy applications in media markets and consumer protection. He earned his Diplom in Economics and Master of Arts in Philosophy and Economics from Freie Universität Berlin, where he completed his PhD in 2006. Prior to his current role, he served as a scientific employee and junior professor at the same institution. His academic trajectory reflects deep expertise in economic theory applied to contemporary market challenges. Professor Münster's research centers on Media Economics, Industrial Organization, Political Economy, Microeconomic Theory, and Consumer Protection. He investigates how commercial incentives shape media bias, the political economy of information dissemination, and regulatory frameworks for digital markets. His work combines rigorous theoretical modeling with experimental and empirical methods to address pressing issues like fake news propagation and platform regulation. His publication record reveals a sustained focus on media-market dynamics and strategic behavior. Recent work analyzes fact-checking efficacy against misinformation, strategic complementarities in media bias, and political positioning of news outlets. Methodologically, he bridges game theory with experimental economics, producing policy-relevant insights for regulators and platform designers. As a principal investigator in the ECONtribute Cluster of Excellence, he leads significant research initiatives funded under Germany’s Excellence Strategy. His collaborative projects span multiple institutions, including partnerships with the Wissenschaftsforum zu Köln und Essen and C-SEB, fostering interdisciplinary approaches to market regulation and public policy. He actively contributes to Cologne’s research ecosystem through the Center for Social and Economic Behavior and the Media and Democratic Publics theme group. These roles position him at the intersection of economics, political science, and media studies, driving forward understanding of how information markets function in democratic societies.

• Media Economics
• Industrial Organization
• Political Economy
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