جست‌وجوی استاد و پژوهشگر

Agnieszka Łukasik-Tureckaمشاهده پروفایل

استاد

• Political Communication
• Media and Disinformation Studies
• Social Media Analysis
• +۴ مورد دیگر

Dr. hab. Agnieszka Łukasik-Turecka, Prof. KUL is a Full Professor at the John Paul II Catholic University of Lublin (KUL), Faculty of Social Sciences, Department of Political Systems and International Communication. She specializes in Political Communication Disinformation Studies Media Analysis Political Propaganda Election Campaign Strategies Her research focuses on disinformation mechanisms during crises (e.g., Russia-Ukraine war , COVID-19 pandemic ), political discourse in religious contexts (e.g., Russian Orthodox Church ), and evolving media roles in elections. She has led KUL-funded projects on Polish media disinformation (2022–2023) and participated in Erasmus+ staff mobility programs at Université Paris 8. Recent publications analyze Pro-Kremlin bot networks Gender dynamics in Belarusian opposition Political communication in Putin's Russia Kremlin narratives blaming NATO/USA for Ukraine invasion and she co-edited the journal Studia i Analizy Nauk o Polityce (2023). Awards include the Silver Medal for Long Service and multiple KUL Rector Awards . She organized international conferences like International, Interdisciplinary Research Project 'Threats to Democracy: Nationalism, Populism, Extremism' and International Conference 'Man, Society and the State in an Unstable World' , and serves on the editorial boards of Addiction & Social Media Communication and Studia i Analizy Nauk o Polityce .

David W. McDonaldمشاهده پروفایل

استاد

• Computer-Supported Cooperative Work
• Human-Computer Interaction
• Social Computing
• +۵ مورد دیگر

David W. McDonald is a Professor in the Department of Human Centered Design & Engineering (HCDE) at the University of Washington. His research and teaching focus on the intersection of social behavior and computing systems. He maintains an active research program with numerous Directed Research Groups (DRGs) examining topics including Wikipedia collaboration, social computing, and the application of Large Language Models in design contexts. Dr. McDonald earned his PhD in Information and Computer Science from the University of California, Irvine, with a dissertation titled "Supporting Nuance in Groupware Design: Moving from Naturalistic Expertise Location to Expertise Recommendation." He completed additional graduate work at UC Irvine (MS in Information & Computer Science) and California State University, Hayward (MS in Math & Computer Science), and received his BA in Cognitive Science from UCLA. His research interests span Computer-Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI), with particular focus on collaboration in Wikipedia, social matching systems, and behavior change through computational systems. His work examines how computational systems can be designed to interleave with human activity to foster collaboration and healthy behaviors. Recent research has explored social translucence in online communities, crisis communication on TikTok, and applications of Large Language Models for debugging assistance and content moderation. Dr. McDonald's publications demonstrate a consistent focus on collaborative systems, with key contributions in social computing, Wikipedia research, and health informatics. His work frequently appears in top-tier venues including ACM CSCW, CHI, and AMIA. Recent publications examine the intersection of social media, health communities, and computational analysis of online behavior. 2013 Distinguished Paper Award, American Medical Informatics Association Fall Symposium (AMIA) 2008 Best Paper Award, AAAI International Conference on Weblogs and Social Media (ICWSM) 2008 Honorable Mention, ACM Conference on Computer Supported Cooperative Work (CSCW) 2003 UW Royalty Research Scholar, University of Washington 1999 Regent's Dissertation Fellowship, University of California, Irvine 1983 Chancellor's Scholar, University of California, Los Angeles Dr. McDonald has served as a Program Director for the Human Centered Computing (HCC), Network Science and Engineering (NetSE), and Social Computational Systems (SoCS) programs at the National Science Foundation. His professional affiliations include the Association for Computing Machinery (since 1999) and ACM SIGCHI (since 1999). He has also co-edited the influential book "HCI Remixed: Essays on Works that have Influenced the HCI Community" (MIT Press, 2008).

Nan Yuمشاهده پروفایل

استاد مدعو

• Social Media Analysis
• Digital Epidemiology
• Public Health Communication
• +۲ مورد دیگر

Dr. Nan Yu serves as Adjunct Professor in Physics and Astronomy at the University of Southern California, with research focusing on social media dynamics and digital epidemiology. Research examines social media communication patterns during public health crises, misinformation diffusion networks, and digital methodologies for health research. Recent work includes longitudinal analysis of COVID-19 communication networks between politicians and health agencies, social media correlates of HIV transmission risks, and meta-analyses of social media's psychological impacts. Publications demonstrate expertise in computational social science methods including network modeling, sentiment analysis, and large-scale social media data analysis. Research contributes to understanding how digital platforms shape health behaviors, policy communication, and societal responses during crises.

Kevin Röbertمشاهده پروفایل

پژوهشگر

• Privacy-Preserving Techniques
• Decentralized Systems
• P2P Networks
• +۴ مورد دیگر

Kevin Röbert is a Researcher and PhD Student in the Computer Networks research group at the University of Hamburg's Department of Informatics, within the Faculty of Mathematics, Informatics and Natural Sciences. He holds a Master's degree (2022) and a Bachelor's degree (2019), both from the University of Hamburg. His research focuses on privacy-preserving techniques in decentralized systems, P2P networks, and edge computing. Notable contributions include work on untrustworthy environments, encrypted endpoint security, and latency-aware edge computing protocols. Education: Master's degree in Informatics, University of Hamburg, 2022 Bachelor's degree in Informatics, University of Hamburg, 2019 Research Interests: Kevin's work spans privacy-enhancing technologies, secure edge computing systems, and decentralized network architectures. He explores topics such as data anonymization in P2P networks, real-time scheduling for edge applications, and mitigating bot automation threats in online services. Publications: His recent work includes advancements in overlay networking middleware for edge applications, dynamic access control for data sharing, and latency-optimized TLS protocols. His 2024 paper on educational reform highlights innovative pedagogical approaches in computer science education. Lab/Team: Kevin is part of the Computer Networks group led by Prof. Mathias Fischer, collaborating with researchers like Dr. Heiko Bornholdt on projects involving network security and decentralized systems.

Dariusz Jemielniakمشاهده پروفایل

استاد

• Open Collaboration
• Media File Sharing Practices
• Political Memes Communities
• +۳ مورد دیگر

Dariusz Jemielniak is a Full Professor of Management at Kozminski University and a Faculty Associate at Harvard's Berkman Klein Center. He is also an entrepreneur, founding platforms like ling.pl and InstaLing. His research spans open collaboration (e.g., Wikipedia), media file sharing practices, and political memes communities. He has held visiting roles at Cornell, Harvard, MIT, and UC Berkeley. Key roles include a decade on the Wikimedia Foundation Board of Trustees (2015–2024), appointment to the European Institute of Innovation and Technology (EIT) board by the European Council, and vice-president of the Polish Academy of Sciences. His current projects total ~$2.5M USD, addressing vaccine misinformation, climate change denialism, and bot detection. Research focuses on collaborative societal trends, qualitative/quantitative digital society analysis (e.g., Thick Big Data , 2020), and the societal role of technology. Notable findings include debunking Godwin’s Law and studying intercessory prayer’s effects on longevity.

Qing Caoمشاهده پروفایل

دانشیار

• AI for Cyber-Physical Systems
• Networking and Distributed Systems
• Cyber Security
• +۵ مورد دیگر

Qing 'Charles' Cao is an Associate Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville, part of the Tickle College of Engineering. His research focuses on AI-driven Cyber-Physical Systems, Networking, and Cyber Security, with applications in IoT, Edge Computing, and Smart Agriculture. He holds a PhD from the University of Illinois at Urbana-Champaign, an MS from the University of Virginia, and a BS from Fudan University, China. Education: PhD in Computer Science, University of Illinois at Urbana-Champaign, 2008 MS in Computer Science, University of Virginia, 2005 BS in Computer Science, Fudan University, Shanghai, China, 2002 Research Interests: His work emphasizes interdisciplinary solutions at the intersection of AI, networking, and security. Key areas include: - Blockchain-based security mechanisms for edge devices - LLM applications in public policy and climate decision-making - IoT-driven precision agriculture and livestock health monitoring - Probabilistic error reasoning and fault tolerance in distributed systems Grants & Awards: No awards explicitly listed, but his research has been supported by grants in areas like smart agriculture and edge computing infrastructure. Advising & Labs: While specific advisees are not listed, his research group focuses on collaborative projects involving students in cybersecurity, IoT, and AI systems. He has contributed to lab initiatives related to environmental computing and distributed sensor networks. Professional Activities: Active in conferences like AAAI, IEEE IPCCC, and ACM/IEEE Edge Computing Symposiums, presenting on topics ranging from LLM-assisted governance to secure edge architectures.

Olga Boichakمشاهده پروفایل

مدرس ارشد

• Digital Cultures
• Conflict Studies
• Media Ecosystems
• +۵ مورد دیگر

Dr. Olga Boichak is a Senior Lecturer in Digital Cultures and Director of the Computational Social Science Lab at the University of Sydney's Faculty of Arts and Social Sciences. She holds a PhD from Syracuse University (USA) and an MPA. Her research focuses on the role of digital technologies in shaping public perception and outcomes of wars, particularly in Ukraine and other post-colonial contexts. Boichak is an Australian Research Council DECRA Fellow exploring colonial topographies of digital sovereignty and leads projects on geopolitical digital media dynamics. Education: PhD in Interdisciplinary Social Science, Syracuse University MPA, Syracuse University Research Interests: Mediatized warfare and participatory war dynamics Information warfare and digital sovereignty Online activism and diasporic humanitarianism Algorithmic agency and meme culture Cybersecurity and geopolitical tech landscapes Awards & Grants: 2024 Max Crawford Medal (Australia's top humanities honor) ARC DECRA Fellowship ($380k) for digital sovereignty research SOAR Prize for interdisciplinary info warfare research Her work bridges humanities and tech through projects like the Computational Social Science Lab, and she advises Australian government agencies on digital security risks. Boichak is also Director of the Ukrainian Studies Foundation in Australia and faces Kremlin sanctions for her advocacy. Labs & Teams: Leads the Computational Social Science Lab and collaborates with institutions like King's College London and the University of Glasgow.

Kaicheng Yangمشاهده پروفایل

استادیار

• Computational Social Science
• Social Bots
• Misinformation Detection
• +۴ مورد دیگر

Dr. Kaicheng Yang is an Assistant Professor in the School of Computing at Binghamton University, where he leads the Yang Lab. His research focuses on creating safe, fair, and trustworthy online information platforms by identifying how malicious actors and flawed systems distort information flow and developing effective countermeasures. His research interests include: Computational social science approaches to understanding online information ecosystems Social bots and automated accounts on social media platforms Misinformation detection and analysis Algorithmic biases in information systems Generative AI misuse and countermeasures Data science methods for social media analysis Dr. Yang's recent publications demonstrate a strong focus on AI-powered social manipulation, with particular emphasis on how generative AI is being used to create malicious social botnets, fake social media profiles with AI-generated faces, and to spread misinformation. His work combines computational methods, data science, and social science approaches to address critical challenges in online information ecosystems. His research has been featured in numerous media outlets including Forbes, Wired, MIT Technology Review, Business Insider, and IEEE Spectrum. He has developed several research tools including DomainDemo explorer for checking user demographics of domains and Botometer X for detecting bot-like Twitter accounts. Dr. Yang recently joined the editorial board of Information Processing & Management and will be hosting a tutorial on 'Introduction to the National Internet Observatory' at ICA 2025 in Denver, CO.

• Cybersecurity
• Network Security
• Machine Learning
• +۷ مورد دیگر

Roberto Perdisci is the Patty and D.R. Grimes Distinguished Professor in the School of Computing at the University of Georgia. He serves as Director of the University of Georgia Institute for Cybersecurity and Privacy and Faculty Fellow at the Center for International Trade and Security. PhD in Computer Engineering (2007) and MS in Electrical and Electronics Engineering (2003) from the University of Cagliari, Italy Current appointments since 2020 include Professor and Graduate Program Faculty in the School of Computing Research Interests span cybersecurity, network security, machine learning for threat detection, and forensic analysis of cybercrime. His work focuses on: Web-based social engineering attacks Robocall and voice spam mitigation DNS and CDN security analysis Malware behavior tracking Differential privacy in telecom systems Post-mortem cybercrime investigation Scientific Contributions include: 15+ years of publications at top venues (IEEE Security & Privacy, USENIX Security, ACM WWW, etc.) Co-inventor of 2023 patents for real-time attack mitigation Keynote speaker at IMC 2025 on internet measurement advancements Advising & Leadership involves mentoring graduate students and organizing workshops like the IMC 2025 Student Workshop. His lab develops systems like WEBRR for forensic replay of web attacks and TRIDENT for social engineering defense.

Xiangyang Liمشاهده پروفایل

استاد آموزشی

• Information Security
• Security Intelligence
• Human Security Informatics
• +۵ مورد دیگر

Xiangyang Li is an Associate Teaching Professor in the Department of Computer Science at Johns Hopkins University, specializing in information security, security intelligence, and human security informatics. His research focuses on computer and system security, user modeling, intelligent systems, simulation and modeling, and knowledge discovery. Education: PhD from Arizona State University Li's work integrates computational cognitive modeling to analyze user security behaviors, phishing classification decisions, and behavioral biometric vulnerabilities. He has developed high-interaction honeypots, explored adversarial techniques against spam detection models, and studied monetary incentives in phishing scenarios. His funded projects include contributions to cybersecurity from DARPA, Air Force Research Laboratory, University of Michigan, and Rensselaer Polytechnic Institute. Li has received recognition including Best Paper Nomination at HICSS-52 and First Place Award at SIG-KM International Research Symposium. At JHU, he teaches courses such as Introduction to Information Security and Computer Intrusion Detection. His research is affiliated with the Information Security Institute.

Aleksandra Urmanمشاهده پروفایل

پژوهشگر

• Computational Social Science
• Political Communication
• Social Media Analysis
• +۵ مورد دیگر

Aleksandra Urman serves as a Postdoctoral Researcher at the Social Computing Group within the Department of Informatics, Faculty of Business, Economics and Informatics at the University of Zurich. Her position bridges computational methodologies with social science inquiry to investigate digital political landscapes. Her academic credentials include a PhD in Social Sciences from the University of Bern (2020), a Master's in Political Science from Central European University (Budapest), and a BA in Asian Studies from the Higher School of Economics (Moscow). This interdisciplinary background informs her approach to analyzing online political behavior. Urman's research specializes in computational political communication with emphases on polarization mechanisms, authoritarian regime tactics, far-right group dynamics, and algorithmic bias in search systems. She employs large-scale data analysis to examine how digital platforms shape political discourse, including studies of Telegram far-right communities, cross-national Twitter polarization, and Russian social media dynamics. Her methodology combines web tracking, content analysis, and cross-platform comparison to audit information ecosystems. Recent publications reveal a concentrated focus on algorithm auditing across search engines and social media, particularly examining generative AI's impact on news consumption, visual representation of historical events, and misinformation patterns during elections. Key trends include cross-lingual comparisons of AI guardrails, longitudinal analysis of search result consistency, and cross-national studies of user trust in algorithmic systems. As an active contributor to the University of Zurich's Digital Society Initiative, Urman participates in multiple research communities including DSI Communication, DSI Democracy, and DSI Cybersecurity. Her work involves collaborations across disciplines to address societal challenges in digital governance and platform accountability.

• Computational Social Science
• Social Media Analysis
• Machine Learning for Social Good
• +۶ مورد دیگر

Kenny Joseph is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. He serves as Associate Director of the AI and Society Institute for Artificial Intelligence and Data Science. PhD in Societal Computing from Carnegie Mellon University (2016) MS in Societal Computing from Carnegie Mellon University (2012) BS in Computer Science from Carnegie Mellon University (2010) His research focuses on computational social science , examining stereotypes and prejudice dynamics , social inequality through computational tools , and gender disparities in organizations . He develops machine learning frameworks for neighborhood change prediction and predictive models for child welfare systems , with work featured in the New York Times . Scientific contributions include: UB Exceptional Scholar—Young Investigator Award (2021) He advises students Yuhao Du (Meta data scientist), Jason Yan (Michigan Ph.D. student), Arjunil Pathak (Amazon researcher), and Navid Madani (former Ph.D. student). His Computation and Equity Lab (cubelab) produces interdisciplinary work spanning social media analysis, urban equity, and computational methods for marginalized communities.

Roberto Rodriguez-Echeverriaمشاهده پروفایل

دانشیار

• Software Engineering
• Model-Driven Engineering
• Machine Learning
• +۵ مورد دیگر

Roberto Rodriguez-Echeverria is an Associate Professor at the Computer Languages and Systems Department of the Universidad de Extremadura (Spain). He leads the Applied Informatic Technology Institute and co-founded MetrikaMedia, a SaaS company for multimedia content measurement. His research focuses on software engineering, model-driven engineering, machine learning, and legacy system modernization. Education: Bachelor and Master in Computer Science (2000), Universidad de Extremadura Master on Education and IT (2012), Universitat Oberta de Catalunya PhD in Computer Science (2014), Universidad de Extremadura Research interests include data-driven software development, medical image segmentation using SAM models, and educational technology applications like chatbot performance analysis in exams. He explores reproducible data science workflows and hybrid recommendation systems for media. Recent work highlights include advancing loss function analysis for COVID-19 lung scans and developing cost-efficient medical image segmentation techniques leveraging SAM's zero-shot capabilities. He also studies the impact of AI tools like ChatGPT on academic assessment methods. His contributions span over 50 publications since 2004, with recent focus on smart city applications, cloud cost estimation, and cooperative social process modeling. Rodriguez-Echeverria's work bridges theoretical computer science with practical industry solutions through his lab's applied research initiatives. Labs/Teams: Applied Informatic Technology Institute (Director) i3 Lab (Affiliated)

János Kertészمشاهده پروفایل

استاد

• Network Science
• Computational Social Science
• Econophysics
• +۷ مورد دیگر

János Kertész is a Professor and Head of the Department of Network and Data Science at the Central European University (CEU), currently based in Vienna. He is also an elected member of the Hungarian Academy of Sciences. Previously, he held professorial and research positions at the Budapest University of Technology and Economics and the Hungarian Academy of Sciences, where he served as Director of the Institute of Physics. His research lies at the intersection of statistical physics and social sciences, with major contributions to network science, econophysics, computational social science, and algorithmic fairness. He investigates systemic risks in economic networks, opinion dynamics in algorithmically biased environments, poverty mapping using big data, and the role of automation in online extremism. The most recent articles highlight a strong trend in applying complex systems methodologies to real-world socioeconomic problems—particularly through large-scale network analysis, multimodal data integration, and policy-relevant modeling in inequality, supply chains, and digital fairness. His collaborative work spans institutions like the Complexity Science Hub (CSH) in Vienna. Elected member of the Hungarian Academy of Sciences He has advised numerous researchers and contributed to major interdisciplinary grants, particularly in complexity science and data-driven social science. His leadership in organizing NetSci 2023, the largest network science conference, underscores his role in fostering global scientific collaboration. He is central to projects involving high-resolution poverty mapping, economic systemic risk, and ethical AI applications. He is actively involved with the Complexity Science Hub (CSH), where CEU is a partner institution, and contributes to interdisciplinary teams working on economic modeling, social network analysis, and crisis preparedness using granular supply chain and VAT data.

• Data Mining
• Social Network Analysis
• Machine Learning
• +۳ مورد دیگر

Jaideep Srivastava is a Professor affiliated with Qatar Foundation (Doha, Qatar), University of Minnesota (Minneapolis, USA), and holds a PhD from University of California Berkeley. His research spans data mining, social network analysis, time series modeling, and health informatics. Recent work focuses on clinical deterioration prediction, sleep research, and computational analysis of pandemic behaviors. Key contributions in clustering algorithms and graph neural networks Active in multimodal learning and misinformation detection His publications from 2024-2022 demonstrate expertise in hierarchical clustering , large language model applications , and health data analytics . Articles often integrate machine learning , social network dynamics , and clinical monitoring systems . Current projects involve Covid-19 in-hospital mortality prediction , virtual influencer analysis , and low-light imaging techniques . Collaborations span institutions in Qatar, USA, and India with applications in urban mobility and precision medicine.