جست‌وجوی استاد و پژوهشگر

Walid Magdyمشاهده پروفایل

استاد

• Computational Social Science
• Natural Language Processing
• Social Content Analysis
• +۸ مورد دیگر

Walid Magdy is a Professor at the School of Informatics, The University of Edinburgh, where he is a faculty member at the Institute for Language, Cognition and Computation (ILCC). He holds a PhD from Dublin City University's School of Computing and has been a faculty fellow at The Alan Turing Institute. With substantial industry experience, he has worked for Qatar Computing Research Institute (QCRI), Microsoft, and IBM, where he filed nine patents. His research spans computational social science and natural language processing. In computational social science, he focuses on social content analysis, political bias detection, polarization, and users' behavior analysis/prediction, with particular attention to understudied communities. His natural language processing work centers on social applications including sentiment analysis, sarcasm detection, and hate-speech identification, with specialized expertise in Arabic and its dialects as well as Persian. Professor Magdy's recent publications demonstrate a consistent focus on Arabic language processing and social media analysis, with growing attention to cultural factors in NLP systems. His work bridges computational methods with social science questions, particularly regarding misinformation, cultural differences in online behavior, and the impact of platform policies on diverse linguistic communities. He has expanded his research to include Persian language processing and continues to explore social dynamics across different cultural contexts. His significant contributions have been recognized with multiple awards: Best Demo Award for TwiXplorer at CSCW 2024 Outstanding Paper Award at ACL 2024 Best Paper Award Runner-up at SocInfo 2022 Best Paper Award at WANLP-EMNLP 2022 Best Paper Award at AIRS 2015 Best Dataset Award at ICWSM 2013 Professor Magdy serves as an editor for Springer's Social Network Analysis and Mining journal and has held leadership roles in major conferences including ACL 2023, ArabicNLP 2023, ICWSM 2022, and ASONAM 2022. He has co-organized numerous shared tasks focused on Arabic language processing and social media analysis, contributing significantly to community resources and benchmarks. He founded and directs the Social Media Analysis and Support for Humanity (SMASH) research group at the University of Edinburgh, which has organized the Summer School in Computational Social Science (SICSS-Edinburgh) in 2022, 2023, and 2025, training researchers in computational social science methodologies.

Emilio Ferraraمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Social Science
• Network Analysis
• +۵ مورد دیگر

Dr. Emilio Ferrara is a Professor of Computer Science & Communication at the University of Southern California, holding appointments in the Viterbi School of Engineering, Annenberg School for Communication and Journalism, and Keck School of Medicine. He serves as Associate Director of Applied Data Science at USC, Research Team Leader at the USC Information Sciences Institute, and Principal Investigator at the USC/ISI Machine Intelligence and Data Science (MINDS) group. His educational background includes a PhD in Machine Learning and BSc & MSc in Computer Science. Ferrara's research focuses on the intersection of artificial intelligence and computational social science, specifically using AI to model and predict human behavior in techno-social systems. His work spans social networks, machine learning, and network science with applications to understanding communication dynamics in digital environments. Ferrara has published over 150 articles in prestigious venues including Proceedings of the National Academy of Sciences, Communications of the ACM, and Physical Review Letters. His research has been widely featured in major news outlets and examines topics ranging from social media manipulation to AI ethics and online behavior. His scientific achievements have been recognized with numerous awards: 2019 Viterbi Scientific Award 2018 DARPA Director's Fellowship 2016 DARPA Young Faculty Award 2016 Complex Systems Society Junior Scientific Award 2015 IBM Watson Big Data Influencer Ferrara's research is supported by major funding agencies including DARPA, IARPA, Air Force, and Office of Naval Research. He leads the HUMANS LAB (Humans & Machines + Networks & Social Systems) which has trained numerous PhD students who have gone on to successful careers in academia and industry. His work on social bots, election integrity, and AI ethics continues to shape understanding of digital social dynamics.

• Computer Security
• Cryptography
• Privacy
• +۴ مورد دیگر

Matteo Maffei is a Full Professor at TU Wien, leading the Security and Privacy group. He joined in 2017 after 11 years at Saarland University's CISPA. He holds a Ph.D. in Computer Science from Ca’ Foscari University of Venice (2006). He coordinates the TU Wien Cybersecurity Center, the SecInt Doctoral School, and the FWF Special Research Program SPyCoDe. His research focuses on formal methods for security and privacy, blockchain technologies, and web security. Roles: Full Professor, Coordinator of TU Wien Cybersecurity Center, Module Head of Christian Doppler Lab for Blockchain Technologies (CDL-BOT), Board Member of Vienna Cybersecurity and Privacy Research Cluster (ViSP). His work emphasizes formal verification of cryptographic protocols, smart contracts, and decentralized systems. Key achievements include ERC Advanced (2024) and Consolidator (2018) Grants, and leadership roles in conferences like IEEE Computer Security Foundations Symposium (CSF). Research Interests: Formal methods, smart contracts, blockchain scalability, web security, privacy-preserving protocols, and decentralized systems. His recent projects include optimizing Lightning Network channels, secure multi-hop payments, and AI-driven robustness verification. Publications: Over 200 publications in top venues like CCS, IEEE S&P, and CRYPTO. Recent work includes advancements in blockchain interoperability (e.g., Alba bridges), light client protocols (Blink), and neural network verification techniques. Grants & Awards: ERC Advanced Grant (2024), ERC Consolidator Grant (2018), DFG Emmy Noether Fellowship (2009). Led projects funded by EU Horizon, FWF, and industry partners like ABC Research GmbH. Advising & Labs: Supervised over 20 PhD/Master theses. Active in the Christian Doppler Lab for Blockchain Technologies and the SPyCoDe SFB. Collaborates with institutions like SBA Research and Stanford University.

Ingo Scholtesمشاهده پروفایل

استاد

• Network Science
• Graph Neural Networks
• Computational Social Science
• +۴ مورد دیگر

Prof. Dr. Ingo Scholtes is Chair of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). His research spans network science, graph machine learning, and computational social science, with applications in software engineering, ecology, biology, and physics. He received a Juniorfellowship from the German Informatics Society (2014) and an SNSF Professorship (CHF 1.5Mio, 2018). Current affiliations: JMU Würzburg (since 2021), University of Zurich (2018-2024), Bergische Universität Wuppertal (2019-2021) Research focus: Higher-order network modeling, temporal graph analysis, AI for collaborative systems, causality-aware machine learning His recent publications demonstrate strong trends in temporal network analysis , graph neural networks for time-series, and higher-order models across software engineering and social science domains. He co-chairs multiple international workshops on complex networks and serves as associate editor for EPJ Data Science and Advances in Complex Systems. Key scientific contributions: Foundational work on higher-order network models published in Nature Physics Methodological innovations in temporal network visualization (HOTVis) and path-based analysis (pathpy) As both educator and organizer, he leads the Computational Social Science Section at GI e.V., mentors across disciplines, and develops tools like git2net for collaboration analysis. His work bridges theoretical foundations with practical applications in network science.

Daniel Chenمشاهده پروفایل

استادیار

• Business Analytics
• Operations Management
• Machine Learning
• +۶ مورد دیگر

Daniel Chen is an Assistant Professor of Business Analytics at the Carroll School of Management, Boston College. His research focuses on strategy in online platforms, leveraging machine learning and structural estimation to analyze two-sided markets and gig economy dynamics. He holds a Ph.D. in Operations Management from the Wharton School (University of Pennsylvania), advised by Gad Allon, and degrees from the University of Southern California, including an M.S. in Mathematical Finance and a B.S. in Economics/Mathematics. His work addresses strategic interactions between platforms and users, with a particular emphasis on data-driven solutions. Research Interests: Chen’s research spans gig economy operations, causal inference with machine learning, algorithmic transparency, and network-based rating systems. He collaborates with industry partners like driver analytics companies to study worker behavior, platform efficiency, and policy impacts. His methodologies combine theoretical modeling with empirical analysis, addressing real-world challenges in platform design and regulatory frameworks. Awards: Finalist, 2023 INFORMS Behavioral Operations Management Best Working Paper Award Labs/Teams: Collaborates with a driver analytics company to analyze gig economy datasets, contributing to both academic research and practical policy recommendations. His work bridges theoretical insights with actionable strategies for platforms and regulators.

Dhiraj Murthyمشاهده پروفایل

استاد

• Social Media
• Digital Research Methods
• Race/Ethnicity Studies
• +۷ مورد دیگر

Professor Dhiraj Murthy holds appointments in the Moody College of Communication , Sociology , and School of Information at the University of Texas at Austin. He earned a Ph.D. in Sociology from the University of Cambridge. His research focuses on social media, digital methods, health communication, and disaster response. He directs the Computational Media Lab , a leading research group with over 20 students, and co-edits the journal Big Data & Society . Notably, he authored the seminal book Twitter: Social Communication in the Twitter Age (2013/2018), which won the ALA CHOICE Prize. Dr. Murthy's work has been funded by NIH, NSF, and other major grants, including studies on e-cigarette marketing, disaster informatics, and AI-driven disinformation detection. He also serves on advisory boards for MediaWell and chairs international social media conferences. Education : Ph.D. in Sociology, University of Cambridge. Grants : Over $5M from NIH, NSF, and UT Austin’s Good Systems initiative for projects like 'AI Technologies to Curb Disinformation' and 'E-cigarette Use Among Mexican American Students.' Labs/Teams : Computational Media Lab (UT Austin), focusing on AI, social media analytics, and health research. Awards : Stanford University’s 2023 Top 2% Global Scientists in Communication & Media Studies and Sociology, ALA CHOICE Prize (2018), and multiple NIH/NSF grants. His work bridges sociology, media studies, and computational methods to address societal challenges like health disparities and misinformation.

• Decision Support Systems
• Energy Management
• Environmental Analysis
• +۶ مورد دیگر

John Psarras is a Professor at the National Technical University of Athens (NTUA) in the School of Electrical and Computer Engineering, specifically within the Division of Industrial Electric Devices and Decision Systems. He serves as the Director of the Decision Support Systems Laboratory (DSSlab) and the University Research Institute of Communication and Computer Systems. He holds a Diploma in Mechanical Engineering (1982) and a Ph.D. in Electrical and Computer Engineering (1989), both from NTUA. His research specializes in decision support systems with applications in energy management, environmental analysis, and information systems. Key areas include: Multi-criteria analysis for energy policy and renewable integration AI-driven optimization of smart grids and building efficiency Sustainable finance mechanisms for green projects Blockchain applications in education and data security His recent publications (2023–2025) demonstrate a strong focus on AI-enhanced decision tools for energy transitions, smart infrastructure, healthcare diagnostics, and cross-border renewable cooperation, reflecting interdisciplinary innovation. He has supervised 22 PhD theses and coordinates EU-funded projects in energy policy, clean technology, and capacity building. No scientific awards are listed in available sources. He leads the Decision Support Systems Laboratory (DSSlab), advancing research in energy analytics, and directs the University Research Institute of Communication and Computer Systems, facilitating large-scale interdisciplinary collaborations.

Hasan Davulcuمشاهده پروفایل

استاد

• Sociocultural Modeling
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Hasan Davulcu is a Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). He holds a B.S. in Mathematics from Middle East Technical University (Turkey) and M.S./Ph.D. in Computer Science from Stony Brook University (NY). His research focuses on sociocultural modeling, AI, machine learning, and behavioral analytics for fraud detection. He leads the CIPS-AI Lab, developing data mining tools for semantic information extraction from social media and web data. Affiliations: Senior Global Futures Scientist (Global Futures Scientists and Scholars Program), Co-founder & CIO of ARTIS MAGI (AI-driven behavioral analysis startup). Education: Ph.D. Computer Science (Stony Brook, 2002), M.S. Computer Science (Stony Brook, 1995), B.S. Mathematics (METU, 1993). Research interests include: Sociocultural modeling and persuasive AI. Web/social media mining, information extraction, and database systems. Behavioral analytics for fraud detection and countering extremist influence. Key achievements: 2011 HSCB Focus Exceptional Scientific Achievement Award for work on sociocultural modeling in the DOD Minerva project. Principal Investigator on NSF and DoD grants, including behavioral analytics for financial fraud and social influence analysis of extremist groups. Grants & Projects: NSF PFI:BIC Grant (2014-2019): Behavioral analytics for fraud detection via visual analytics infrastructure. DoD Minerva (2015-2019): Measuring social influence of extremist groups. ONR Projects (2018-2021): Modeling polarization, adversarial framing, and disinformation tracking. Labs/Teams: Cognitive Information Processing Systems (CIPS-AI) Lab, which pioneers data mining techniques for unstructured social media data and semantic representation systems.

Kathleen M. Carleyمشاهده پروفایل

استاد

• Network Science
• Social Cybersecurity
• Computational Social Science
• +۴ مورد دیگر

Kathleen M. Carley is a full professor at Carnegie Mellon University's School of Computer Science with courtesy appointments in Engineering and Public Policy, Heinz School, and Electrical and Computer Engineering. As director of the Center for Computational Analysis of Social and Organizational Systems (CASOS) and the Center for Informed Democracy and Social-Cybersecurity (IDeaS) , she leads interdisciplinary research at the intersection of network science, cognitive modeling, and cybersecurity. Ph.D. in Sociology from Harvard University SB degrees in Economics and Political Science from MIT Her research focuses on Dynamic Network Analysis (DNA) and Social-Cybersecurity (SC) , developing tools like ORA (network analysis), AutoMap (semantic mining), Construct (influence simulation), and BotHunter (bot detection). She has over 400 publications and 15+ active research projects addressing disinformation, cognitive security, and organizational resilience. Recent work examines LLM-powered bots , multi-platform misinformation dynamics , and public health analytics . As an IEEE Fellow, she contributes to standards in computational social science while teaching courses on network analysis and complex socio-technical systems.

Onur Varolمشاهده پروفایل

استادیار

• Computational Social Science
• Network Science
• Machine Learning
• +۴ مورد دیگر

Onur Varol is an Assistant Professor at Sabanci University's Computer Science Department and leads the VIRAL Lab, which focuses on computational social science, network science, and machine learning. He has affiliations with the Center of Excellence for Data Analytics. His research spans social bot detection, misinformation analysis, and online behavior modeling.

Guy-Vincent Jourdanمشاهده پروفایل

استاد

• Software Security
• Cybersecurity
• Distributed Systems
• +۶ مورد دیگر

Professor Guy-Vincent Jourdan is affiliated with the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a Ph.D. from Université de Rennes/INRIA (France, 1995) focusing on distributed systems analysis. Prior to academia, he served as CTO and CEO of Decision Academic Graphics, an Ottawa-based firm. His research interests span software security, cybersecurity (including cybercrime prevention), distributed systems modeling, formal methods, mobile applications, and rich internet applications. Specific technical emphases include phishing detection systems, blockchain fraud analysis, and adversarial machine learning. Professor Jourdan has pioneered tools like D-ForenRIA for reconstructing user interactions in Rich Internet Applications and contributed to cybersecurity frameworks such as HEART for log anomaly detection. His work integrates machine learning techniques with domain-specific challenges in network security and software verification. His publications (2023-2025) reflect advancements in AI-driven vulnerability analysis, blockchain fraud detection, and automated phishing detection systems. Notable projects include SV-TrustEval-C for source code vulnerability analysis and Intellitweet for social media threat detection. While no scientific awards are explicitly listed, his prolific publication record and industry-academia transition highlight sustained contributions to computer science and cybersecurity domains.

Sandra González-Bailónمشاهده پروفایل

استاد

• Political Communication
• Social Media Analysis
• Computational Social Science
• +۳ مورد دیگر

Sandra González-Bailón is the Carolyn Marvin Professor of Communication at the University of Pennsylvania's Annenberg School for Communication and holds a secondary appointment in Sociology. As Director of the Center for Information Networks and Democracy (CIND), her research examines how communication networks shape information exposure, with implications for political engagement, mobilization dynamics, and news consumption patterns. Her methodological work bridges computational social science and political communication. Education M.S. from University of Oxford (2004) Ph.D. from University of Oxford (2007) Research Focus González-Bailón's research program investigates the intersection of technology and society, with emphasis on how digital networks transform political communication. Key areas include: Information diffusion : Studies how content spreads through social platforms during elections and crises Algorithmic curation : Examines how platform algorithms shape ideological segregation and exposure diversity Network dynamics : Maps communication patterns in online collective action and protest movements Her empirical work employs computational methods to analyze digital trace data at scale. Publication Trends Recent publications demonstrate sustained focus on social media's impact during democratic processes, particularly the 2020 U.S. election. Key thematic threads include: misinformation diffusion patterns, asymmetric polarization in news exposure, experimental analysis of platform deactivation effects, and methodological innovations in computational social science. Notable contributions appear in Science , Nature , and PNAS . Center Leadership As founding director of CIND, González-Bailón oversees research examining how digital technologies impact democratic resilience, with projects spanning misinformation analysis, network mapping of political discourse, and policy interventions for platform governance.

Mustafa A. Mustafaمشاهده پروفایل

مدرس ارشد

• Information Security
• Data Privacy
• Applied Cryptography
• +۷ مورد دیگر

Mustafa A. Mustafa is a Senior Lecturer (Associate Professor) in the Department of Computer Science at The University of Manchester, where he leads the Trusted Digital Systems Cluster as part of the university-wide Centre for Digital Trust and Society. His academic journey spans prestigious institutions including The University of Manchester, where he completed his PhD, and KU Leuven in Belgium, where he served as a post-doctoral research fellow. Dr. Mustafa earned his educational qualifications through an impressive academic path: a B.Sc. in communications from the Technical University of Varna, Bulgaria (2007), an M.Sc. in communications and signal processing from Newcastle University, UK (2010), and a Ph.D. in computer science from The University of Manchester, UK (2015). His doctoral research focused on "Smart Grid Security: Protecting Users' Privacy in Smart Grid Applications," laying the foundation for his subsequent research career. Dr. Mustafa's research expertise centers on information security, data privacy, and applied cryptography with particular focus on smart grid systems, smart city applications, e-health, and IoT. His work addresses critical challenges in securing peer-to-peer electricity trading markets, smart metering infrastructure, electric vehicle charging systems, and health data management. He has developed innovative solutions for keyless car sharing systems, frictionless authentication mechanisms, and privacy-preserving protocols for data collection and distribution. His scholarly contributions demonstrate a consistent trajectory toward increasingly sophisticated privacy-preserving techniques applied across multiple domains. Recent work shows a growing integration of artificial intelligence and machine learning approaches with traditional cryptographic methods, particularly in federated learning systems and large language model verification. His research bridges theoretical cryptography with practical implementations in energy systems and healthcare applications. Dr. Mustafa's scientific achievements have been recognized with several prestigious awards: Winner of the Student Video Competition at IEEE SmartGridComm 2017 for "Secure and Privacy-friendly Local Electricity Trading" Best Paper Award at SECURWARE 2017 Distinguished Achievement Award as Postgraduate Research Student of the Year nominee by the School of Computer Science of The University of Manchester (2015) Dame Kathleen Ollerenshaw Research Fellowship (2018-2023) As an academic supervisor, Dr. Mustafa has mentored numerous graduate students through their PhD and Master's research, with a particular focus on privacy and security challenges in emerging technologies. His current supervision portfolio includes research on privacy-friendly multi-agent systems for smart grids, security for IoT in e-health, vulnerability detection in IoT cryptography, and bot detection systems. He has secured significant research funding through multiple competitive grants including EnnCore: End-to-End Conceptual Guarding of Neural Architectures (EPSRC, 2020-2024), SCorCH: Secure Code for Capability Hardware (EPSRC, 2019-2023), and SNIPPET: Secure and Privacy-friendly Peer-to-peer Electricity Trading (FWO-SBO project, 2019-2023). Dr. Mustafa leads the Trusted Digital Systems Cluster within the Centre for Digital Trust and Society at The University of Manchester. His research group comprises PhD students, postdoctoral researchers, and collaborators working on cutting-edge security and privacy solutions. The team maintains strong international collaborations, particularly with KU Leuven in Belgium, and contributes to standards development as evidenced by Dr. Mustafa's role as an expert in the IEC/SYC/WG 3 "IEC Smart Energy Roadmap."

• Software Testing
• Mutation Testing
• Model-Based Testing
• +۷ مورد دیگر

Jeff Offutt is a Professor and Chair of the Department of Computer Science at the University at Albany, College of Nanotechnology, Software, & Engineering. Previously, he was a Full Professor with Tenure in Software Engineering at George Mason University since 2005. He received his PhD in Information & Computer Science from the Georgia Institute of Technology in 1988. His research spans software testing, mutation testing, model-based testing, automatic test data generation, web application testing, and software engineering education. He has led significant projects such as the NSF-funded integration of CS into K-5 classrooms and the Google-funded SPARC project for scalable CS1/CS2 instruction. The 15 most recent articles reflect a continued focus on mutation testing cost reduction, model-based testing oracles, educational innovations, and security aspects of web applications. Trends include empirical validation, industrial applicability, and bridging theory with practice in software testing and engineering education. John Toups Presidential Medal for Excellence in Teaching (2020) George Mason University’s Alumni Association Faculty Member of the Year (2020) Outstanding Faculty Award from the State Council of Higher Education for Virginia (2019) Best Paper Award at ICST 2021 10-Year Most Influential Paper Award at MODELS 2020 George Mason University Teaching Excellence Award (2013) ACM Notable Article Award (2013) Jeff Offutt has mentored numerous graduate students including Upsorn Praphamontripong, Nan Li, and Yu-Seung Ma, and has led major grant-funded projects such as the SPARC educational model and NSF initiatives on K-5 CS integration. His textbook Introduction to Software Testing (with Paul Ammann) is widely adopted globally. He led the MS in Software Engineering program at GMU and developed several new courses in software testing, web engineering, and usability. He pioneered innovative teaching methods using web technologies and asynchronous learning models. He also co-founded the IEEE International Conference on Software Testing, Verification and Validation (ICST) and served as Editor-in-Chief of Software Testing, Verification and Reliability from 2007 to 2019.

Amir Rahmatiمشاهده پروفایل

استادیار

• System Security
• Internet of Things (IoT) Security
• Augmented Reality (AR) Security
• +۳ مورد دیگر

Amir Rahmati is an Assistant Professor in the Department of Computer Science at Stony Brook University , where he directs the Ethos Security and Privacy Lab and contributes to the Stony Brook National Security Institute . His research focuses on system security , with specific emphasis on the security and privacy challenges of emerging technologies such as IoT , AR , and ML systems . Teaching: Instructor of SBU102: Computer Security (Spring 2025) Collaborations: Frequent collaborations with institutions like University of Michigan, University of Toronto, and IEEE/USENIX conferences. Rahmati’s research addresses security vulnerabilities in resource-constrained devices and real-world ML applications. His work includes adversarial robustness in neural networks, attack synthesis on medical devices, and privacy-preserving frameworks for IoT ecosystems. Trends in his publications reveal a focus on practical system design to mitigate security threats in cyber-physical systems , augmented reality , and blockchain technologies . Prospective students: Rahmati seeks researchers with expertise in hardware/software, machine learning, network protocols, and security to tackle system-stack challenges in his lab.