جست‌وجوی استاد و پژوهشگر

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.

• 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.

Abdullah Mueenمشاهده پروفایل

استاد

• Temporal Data Mining
• Blockchain Data Mining
• Social Media Mining
• +۵ مورد دیگر

Abdullah Mueen is a Professor and Associate Chair in the Department of Computer Science at the University of New Mexico (UNM), where he has been since 2013. Previously, he worked as a Scientist in the Cloud and Information Sciences Lab at Microsoft Corporation. Research Interests : His work focuses on Temporal Data Mining , with emphasis on efficiency , interactivity , and interpretability . Key areas include Blockchain Data Mining (e.g., BitLink for Bitcoin cluster analysis), Seismic Data Mining (e.g., PAW for aftershock detection), and Social Media Mining (e.g., DeBot for Twitter bot detection). Article Trends : His recent publications span four domains: Seismology : Algorithms for earthquake data analysis (e.g., focal depth inference, aftershock classification). Blockchain : Temporal linkage of Bitcoin addresses (BitLink) and cryptocurrency fraud detection. Traffic Safety : Multi-LiDAR data fusion for real-time road safety monitoring. Time Series Methods : Innovations like MASS similarity search and DAMP anomaly detection for massive datasets. Scientific Awards : ACM SIGKDD Test-of-Time Award (2022) UNM Provost Research Leader Award UNM School of Engineering Junior Faculty Research Excellence Award KDD 2012 Doctoral Dissertation Contest Runner-Up KDD 2012 Best Paper Award Advising and Grants : He has mentored 11 PhD students now employed at institutions like Microsoft, Meta, and Lawrence Livermore National Lab. His research is funded by NSF , NIH , DARPA , AFRL , NEC , Exxon , Microsoft , and LANL .

Patrik Wikstromمشاهده پروفایل

استاد

• Computational Communication
• Music Industries
• Algorithmic Culture
• +۳ مورد دیگر

Professor Patrik Wikstrom is a computational communication scholar at Queensland University of Technology (QUT), leading the School of Communication. He serves as Chief Investigator in QUT's Digital Media Research Centre (DMRC) and Associate Investigator in the Australian Research Council's ADM+S Centre. His expertise spans digital media's impact on music and meme cultures, algorithmic systems, and cultural economics. Education: PhD in Media and Communication Studies (Karlstad University), MScEng (Chalmers University). Former roles include Director of DMRC, Associate Dean (Research) at QUT's Creative Industries Faculty, and academic leadership at Northeastern University, Jönköping International Business School, and Karlstad University. Research focuses on digital technologies' societal impacts, including recommender systems, platform governance, and music industry dynamics. Key publications include TikTok: Creativity and Culture in Short Video and The Music Industry: Music in the Cloud . Active in grants like the Australian Cultural and Creative Activity Analysis project (LP160101724). Supervised over 10 doctoral and master's students on topics ranging from platform governance to decolonizing copyright. Current projects explore algorithmic culture, AI ethics, and fair payment models for artists. Labs/Teams: DMRC and ADM+S collaborate on automated decision-making's societal implications. Advocates for responsible AI and interdisciplinary computational methods in social sciences.

• Internet of Things
• Edge Computing
• Artificial Intelligence
• +۶ مورد دیگر

Professor Xiaodong Liu is a faculty member at Edinburgh Napier University, affiliated with the School of Computing, Engineering and the Built Environment . His research spans Internet of Things , Edge Computing , Artificial Intelligence , and Cybersecurity , with a focus on decentralized systems and data-driven decision-making. Research Themes : IoT orchestration, federated learning, smart city infrastructure, building maintenance optimization, and automotive cybersecurity. Current Projects : Leading Swarmchestrate (EU-funded), Long-range Perceptive Autonomous Vehicles (Royal Society), and Met-Bot for Disaster Surveillance (Royal Society). His recent publications emphasize privacy-preserving edge learning , semantic IoT data validation , and deep learning for weather prediction . As a supervisor, he has guided PhD students in areas like federated learning, smart building systems, and IoT security. Collaborations include partnerships with institutions in Scotland, China, and Italy, alongside funding from European Commission , Royal Society , and Scottish Funding Council . He contributes to international conferences and journals, with notable work in IEEE Transactions , ACM TAAS , and MDPI publications.

János Kertészمشاهده پروفایل

استاد

• Statistical Physics
• Complex Networks
• Financial Analysis
• +۹ مورد دیگر

János Kertész is a Professor at the Department of Network and Data Science at Central European University (CEU) since 2012, and previously held the position of Professor at the Budapest University of Technology and Economics (1992–2018). He obtained his PhD in Physics from Eötvös University (1980) and DSc from the Hungarian Academy of Sciences (1989). His research spans statistical physics applications, complex networks, and financial analysis. He has authored over 280 papers and served on editorial boards of journals like Journal of Physics A and Physical Review E . His research focuses on interdisciplinary topics including social network dynamics, systemic risk in economic systems, and algorithmic bias in digital environments. Notable awards include the Széchenyi Prize (Hungary’s highest scientific honor) and the Finland Distinguished Professorship. He has led projects such as SAI (Socially Explainable AI) and HUMANE-AI-NET, addressing algorithmic bias and AI ethics. His work bridges physics-based modeling with real-world social and economic systems, emphasizing computational approaches to corruption, opinion formation, and innovation diffusion. Key contributions include modeling cascading failures in interdependent networks and analyzing attention dynamics on platforms like Sina Weibo during the pandemic. He advises on systemic risk mitigation strategies and collaborates internationally, with visiting roles in Germany, the U.S., France, Italy, and Finland.

Malcolm Heywoodمشاهده پروفایل

استاد

• Genetic Programming
• Coevolution
• Reinforcement Learning
• +۱۲ مورد دیگر

Dr. Malcolm Heywood is a Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the Network Information Management and Security (NIMS) Lab and is actively involved in research on genetic programming, coevolution, reinforcement learning, and big data analytics. His research interests span: Genetic Programming and Evolutionary Computation Coevolution and Competitive Learning Problem Decomposition and Hierarchical Models Streaming Data Analysis and Anomaly Detection Network Security and Insider Threat Detection Reinforcement Learning in Games (Atari, ViZDoom, Dota 2) Dr. Heywood's recent publications focus on emergent behaviors in reinforcement learning using Tangled Program Graphs (TPG), benchmarking genetic programming for streaming data, and applications in cybersecurity and computational finance. His work demonstrates a strong trend toward scalable, efficient evolutionary models for complex, real-world problems. His scientific awards include: Silver placed at Human-Competitive (Humies) Competition (2018) Best Paper at EuroGP (2017) Best Paper at DETA track, ACM GECCO (2017) Best Paper at RWA track, ACM GECCO (2018) Nomination for Best Paper at DETA track, ACM GECCO (2019) He has supervised numerous graduate students, including PhD and Master's candidates, many of whom have continued research in evolutionary computation. His lab has developed open-source code distributions for Tangled Program Graphs and Symbiotic Bid-Based GP. Dr. Heywood teaches courses in Computer Organization, Introduction to AI with Gaming Applications, and Genetic Algorithms and Programming.

Pietro Saggeseمشاهده پروفایل

استادیار

• Cryptofinance
• DeFi (Decentralized Finance)
• Cryptoasset Analytics
• +۵ مورد دیگر

Pietro Saggese is an Assistant Professor at IMT School for Advanced Studies Lucca, Italy, since November 2023. He holds a PhD in Economics and Data Science from IMT Lucca (2021), preceded by a BSc and MSc in Physics from the University of Turin. His research focuses on cryptofinance, cryptoasset analytics, CBDCs, and their socio-economic implications, with particular attention to decentralized finance (DeFi) protocols and their interactions with traditional financial systems. Before joining IMT, Pietro worked as a Scientist at AIT Austrian Institute of Technology (2021–2023), collaborating with the Complexity Science Hub. His work there explored DeFi’s systemic risks, smart contract interdependencies, and cryptoasset monitoring. Notable contributions include analyzing Bitcoin arbitrageurs’ behavior using Mt. Gox data and investigating DeFi governance structures. Research interests span: Cryptocurrency market dynamics and arbitrage strategies DeFi protocols and their regulatory challenges CBDC design and implications for monetary policy Blockchain-based systems’ socio-economic impacts His publications address topics like TVL verification in DeFi, bot detection on Ethereum, and DAO governance mechanisms. He actively contributes to international conferences and policy-oriented groups such as the Crypto-Asset Monitoring Expert Group (CAMEG). Pietro’s work bridges academic research with real-world applications, aiming to inform policymakers and market participants about emerging risks and opportunities in digital finance.

• Cybersecurity
• Cyber-physical systems security
• Social network analysis
• +۷ مورد دیگر

Andrey Chechulin is a Professor at the St. Petersburg Federal Research Center of the Russian Academy of Sciences, Institute of Informatics Problems, where he leads research in cybersecurity within the Information Security Department. His work spans over 15 years with more than 80 publications in top-tier security conferences and journals. Dr. Chechulin's primary research interests include cybersecurity, cyber-physical systems security, social network analysis, and bot detection. His work focuses on developing practical methodologies for incident investigation, access control, and vulnerability assessment. He has pioneered approaches for analyzing social media bots, particularly on the VKontakte platform, and developed innovative frameworks for cryptocurrency transaction anomaly detection using neural networks. His publication record shows consistent contributions to major security venues including PDP, COMSNETS, and Sensors, with a notable increase in output since 2018. Recent work demonstrates his adaptation to emerging security challenges in AI-generated content detection and blockchain security. Best Paper Award at PDP 2021 Cybersecurity Research Excellence Award (2019) Dr. Chechulin maintains strong collaborative relationships with Igor V. Kotenko (60 joint publications), Dmitry Levshun, and Maxim Kolomeets. His research often bridges theoretical security concepts with practical implementations for real-world systems, including smart city infrastructure and automotive security applications.

Alireza Mohammadinodooshanمشاهده پروفایل

پژوهشگر ارشد

• Social Media Analysis
• User Engagement Dynamics
• Data Mining
• +۳ مورد دیگر

Alireza Mohammadinodooshan is a Postdoctoral Fellow at Linköping University's Department of Computer Science (IDA), Sweden, working within the Database and Information Technology (ADIT) research group. He contributes to the Wallenberg AI, Autonomous Systems and Software Program (WASP) – Sweden's largest individual research initiative – focusing on data-driven analysis of social media engagement dynamics across Twitter, Facebook, and Instagram platforms. His research centers on quantifying how political bias, news reliability, and content-agnostic factors shape temporal user engagement patterns. Key interests include social media analysis, user engagement dynamics, data mining, information systems, network science, and artificial intelligence, with emphasis on cross-platform comparative studies and algorithmic amplification effects in news consumption. Analysis of his 15 most recent publications reveals consistent focus on temporal modeling of engagement decay, multi-format content interaction (photos/videos/albums), and the interplay between news source characteristics and user behavior. His methodological approach combines large-scale dataset analysis with network theory to identify virality predictors and platform-specific engagement mechanics. No scientific awards were documented in available sources. No advising roles or research grants were referenced in the provided materials. He operates within the ADIT research group at Linköping University, which specializes in advanced database and information systems for the digital society. This group forms part of IDA's broader WASP-affiliated ecosystem focused on AI-driven autonomous systems, enabling interdisciplinary collaboration on large-scale data challenges in social computing.

Jeff Offuttمشاهده پروفایل

استاد

• Software Testing
• Mutation Testing
• Model-Based Testing
• +۸ مورد دیگر

Jeff Offutt is a Professor and Chair of the Department of Computer Science at the University at Albany, part of the College of Nanotechnology, Software, & Engineering. Previously, he was a Full Professor with Tenure in Software Engineering at George Mason University, where he led the MS in Software Engineering program and developed numerous courses in software testing, web engineering, and software analysis. His research has significantly influenced both academia and industry in software testing and engineering education. His research interests span software testing, mutation testing, model-based testing, automatic test data generation, web application testing, and software engineering education. He has pioneered techniques such as bypass testing and model-based testing, with applications embedded in tools like Selenium. His work emphasizes practical, impactful research—what he calls "science fiction"—with a clear path to real-world use by software engineers. His recent publications reflect a strong trend in cost-effective mutation testing, model-based testing strategies, educational innovations like self-paced learning models (e.g., SPARC), and security aspects of web applications. The keywords across his work include software testing, empirical studies, automation, and education, with subfields such as fault localization, test oracles, input partitioning, and scalable pedagogy. John Toups Presidential Medal for Excellence in Teaching (2020) George Mason University Faculty Member of the Year (2020) Virginia Outstanding Faculty Award (2019) George Mason Teaching Excellence Award (2013) 10-year Most Influential Paper Award, MODELS 2020 Best Paper Award, ICST 2021 Most Influential Paper, ISSRE (2019) Jeff Offutt has advised numerous students and collaborated internationally, including at Swedish universities. He has led major educational initiatives such as the NSF-funded SPARC project, aimed at improving scalability and diversity in introductory CS courses. He has also been actively involved in curriculum development, including co-authoring IEEE/ACM guidelines for software engineering programs. His leadership extends to founding and chairing major conferences like IEEE ICST and serving as Editor-in-Chief of Software Testing, Verification and Reliability . He leads research projects on smart test automation, minimal mutation, and K–5 computer science integration. His work has resulted in widely used tools including muJava, Mothra, and SpecTest. He frequently gives keynote talks on testing, education, and research methodology, and is known for his popular talk "How to Get Your Paper Rejected."

Matthias C. Kettemannمشاهده پروفایل

استاد

• Internet Law
• Digitalization Governance
• Artificial Intelligence Regulation
• +۵ مورد دیگر

Matthias C. Kettemann is a full-time Professor of Innovation, Theory and Philosophy of Law at the University of Innsbruck , where he leads the Department of Legal Theory and Future of Law . He also serves as a research programme leader at the Leibniz Institute for Media Research | Hans Bredow Institute in Hamburg, research group leader at the Alexander von Humboldt Institute for Internet and Society in Berlin, and as a research group leader at the Sustainable Computing Lab in Vienna. His academic affiliations extend to associated researcher roles at institutions in Hamburg, Frankfurt am Main, and Graz. Head of Department, University of Innsbruck Research Programme Leader, Leibniz Institute for Media Research Research Group Leader, Humboldt Institute for Internet and Society Board Member, Sustainable Computing Lab Associated Researcher, Research Institute for Social Cohesion Prof. Kettemann’s research focuses on internet law , AI governance , platform regulation , digital constitutionalism , and innovation law . His publications and projects explore normative orders , multinormativity , and cybersecurity in digital spaces. His recent articles address human-in-the-loop AI systems , consumer credit algorithms , and democratic resilience against disinformation . He also investigates legal frameworks for data sovereignty and platform accountability in crisis situations . He has received prestigious scholarships, including the Fulbright and Boas Scholarships , and contributes to academic discourse as Managing Editor of the Mohr/Siebeck series 'Internet und Gesellschaft' , editorial board member of journals like jusIT and Media and Communication Studies , and scientific advisory board member of the European Yearbook on Human Rights . He is actively involved in reviewing for international publishers and academic journals. Prof. Kettemann teaches in German, English, and French, with courses in internet law , innovation law , international law , and legal philosophy since 2008. He supervises theses at bachelor, diploma, doctoral, and post-doctoral levels, emphasizing digital law and innovation frameworks . His third-party funding includes grants from the European Union , Friedrich-Ebert-Stiftung , Google , and Deutsche Telekom .

Pietro Saggeseمشاهده پروفایل

استادیار

• Cryptofinance
• Cryptoasset Analytics
• Decentralized Finance (DeFi)
• +۷ مورد دیگر

Pietro Saggese is an Assistant Professor at the IMT School for Advanced Studies Lucca, Italy, where he is affiliated with the Department of Economics and Data Science within the School of Economics, Management, and Data Science. He is also associated with the Complexity Science Hub (CSH) in Vienna, contributing to the Digital Currency Ecosystems research group. Prior to his current role, he was a postdoctoral researcher at CSH and the AIT Austrian Institute of Technology from 2021 to 2023. Bachelor’s and Master’s in Physics, University of Turin PhD in Economics and Data Science, IMT School for Advanced Studies Lucca (2021) Pietro Saggese's research centers on cryptofinance and cryptoasset analytics, with a particular focus on decentralized finance (DeFi) ecosystems. He investigates the interplay between DeFi protocols on Ethereum to assess risks and opportunities, analyzes arbitrage behavior in Bitcoin, detects financial bots on blockchains, and evaluates the governance structures of Decentralized Autonomous Organizations (DAOs). His work combines data science, complex systems theory, and economic modeling to provide empirical insights into digital currency markets. His recent publications reveal a strong trend toward understanding systemic structures in DeFi, including composability, governance centralization, and solvency risks. The works span technical blockchain analysis, economic modeling, and regulatory implications, highlighting an interdisciplinary approach to cryptoeconomic systems. Scientific Awards and Recognition: No specific awards listed in the text, though his collaborator Stefan Kitzler won a research prize for a DeFi-related paper. Pietro actively collaborates with researchers at CSH, AIT, and IMT Lucca on grants and projects related to digital currency ecosystems. His advising roles are not explicitly mentioned, but his publications suggest mentorship and collaboration with early-career researchers. He is involved in high-impact research projects on blockchain transparency, DAO governance, and financial stability in crypto markets. He is a key contributor to the Digital Currency Ecosystems research initiative at the Complexity Science Hub, where he continues to publish and present on pressing issues in decentralized finance, including power concentration in DAOs and the solvency of crypto exchanges.