جست‌وجوی استاد و پژوهشگر

Paul Patrasمشاهده پروفایل

استاد

• Artificial Intelligence
• Network Traffic Analytics
• Network Security & Privacy
• +۴ مورد دیگر

Paul Patras is a Professor of Mobile Intelligence at the University of Edinburgh's School of Informatics, leading the Mobile Intelligence Lab and affiliated with the Institute for Computing Systems Architecture (ICSA) and Security & Privacy group. He co-founded Net AI, a university spin-off advancing AI-driven network management. His expertise spans AI for network traffic analytics, security, and biomedical innovation. He holds a Ph.D. from University Carlos III of Madrid and has held visiting roles at institutions like Northeastern University and Rice University. Research focuses on bridging mathematical models with real-world network applications, including high-speed data stream processing and adversarial robustness. Recent work includes Stable-Sketch for web-scale data streams and Sabre for adversarial noise filtering. Awards include the SICSA Best Dissertation Award (2024, student Alec Diallo) and a best paper award at WWW '24 (with Weihe Li). Advising includes PhD students like Weihe Li and postdocs like Alec Diallo. He has supervised numerous students in areas like network security and machine learning. His labs and collaborations drive innovation in 6G, edge computing, and AI ethics. He frequently speaks at global forums, including the UN's AI for Good Summit and 6G Evolution Summit.

• Recommender Systems
• Artificial Intelligence
• Data Mining
• +۶ مورد دیگر

Dr. Yongli Ren is an Associate Professor in the School of Computing Technologies at RMIT University, located at the City Campus in Australia. His research focuses on advancing Recommender Systems, with particular emphasis on fairness, quantum computing optimization, and spatio-temporal data analysis. He holds expertise in data mining, collaborative filtering, and context-aware systems. Dr. Ren is open to supervising Masters and PhD students in areas such as quantum annealer-based optimization, fairness-aware recommendation, and heterogeneous time-series analysis. His teaching interests include web search algorithms, collaborative filtering, data mining methodologies, and log analysis. Dr. Ren has contributed to over 70 research outputs, with recent works addressing recommendation system evaluation metrics, fairness in AI, and energy-efficient routing protocols in wireless sensor networks. His research has been published in prestigious venues like ACM Transactions on Information Systems, IEEE Access, and the ACM Web Conference (WWW). Key research themes include addressing the precision-diversity trade-off in recommendations, enhancing fairness through dual-temporal networks, and leveraging quantum computing for QUBO optimization. He has also explored applications in mobility pattern analysis, workplace productivity systems, and fog-based distributed recommendations.

Savvas Zannettouمشاهده پروفایل

استادیار

• Machine Learning
• Data-Driven Analysis
• Social Media
• +۱۲ مورد دیگر

Savvas Zannettou is an Assistant Professor at the Faculty of Technology, Policy, and Management (TPM) at Delft University of Technology (TU Delft). He is also an Associated Researcher at the Max Planck Institute for Informatics (MPI-INF) and a core member of the iDrama Lab. His research is centered on applying machine learning and data-driven analysis to understand online phenomena such as misinformation, hate speech, and algorithmic recommendations on social media platforms. His research interests lie at the intersection of computer science, social media, and policy. Key areas include online disinformation, hate speech detection, algorithmic transparency, toxic content in AI-generated media, and platform moderation. He employs large-scale data analysis to study user behavior on platforms like TikTok, Twitter, Reddit, and WhatsApp, with a strong focus on ethical and societal implications. His recent publications reveal a strong trend in auditing social media algorithms, analyzing AI-generated harmful content (e.g., hateful memes), and leveraging data donation frameworks to study user exposure. His work frequently appears in top-tier venues like WWW, IEEE S&P, USENIX Security, and ICWSM. He has led research on topics including QAnon, incel communities, state-sponsored trolls, and the impact of moderation policies. He has received several scientific honors, including: Distinguished Paper Award, ACM IMC 2018 Best Paper Honorable Mention, ICWSM 2020, 2024 Best Paper Honorable Mention, CSCW 2021 Best Paper Honorable Mention, ACM CCS 2022 Best Senior Program Committee Member, AAAI ICWSM 2022, 2023 Savvas Zannettou actively mentors students, with several PhD and Master’s advisees leading publications in top conferences. He has been involved in significant grant-funded research, including a €1.3M NWO project on trusted flaggers under the Digital Services Act (DSA), and has secured funding from Google’s Academic Research Award Program. He serves on the program committees of major conferences such as ICML, NeurIPS, USENIX Security, and The Web Conference. He is a core member of the iDrama Lab, which focuses on data-driven research for advanced modeling and analysis, particularly in the context of online safety and platform governance.

Srijan Kumarمشاهده پروفایل

استادیار

• AI for Security
• Security for AI
• Graph Learning
• +۷ مورد دیگر

Srijan Kumar is an Assistant Professor in the Computer Science and Engineering department at the College of Computing, Georgia Institute of Technology. His research focuses on developing AI and data mining methods to enhance web safety and security, with particular expertise in graph and multimodal learning for detecting malicious actors and harmful content. Dr. Kumar earned his Bachelor's degree from the Indian Institute of Technology at Kharagpur, followed by his Master's and Ph.D. from the University of Maryland, College Park. Prior to joining Georgia Tech, he was a visiting researcher at Google AI and a postdoctoral researcher at Stanford University. His research spans four main areas: (1) AI for Security, developing methods to detect misinformation, malicious actors, and online hate; (2) Secure, Robust, and Responsible AI, investigating the trustworthiness of AI systems; (3) Graphs and Networks, modeling large-scale interconnected data; and (4) Recommender Systems and Behavior Modeling. His work has been deployed at Flipkart and has influenced Twitter's Birdwatch platform. Dr. Kumar leads The CLAWS (Computational Data Science Lab for the Web and Social Media), which has received funding from NSF, DARPA, CDC, Adobe, Google, Facebook, and Microsoft. His lab mentors numerous PhD, MS, and undergraduate students working on cutting-edge web safety and AI security challenges. NSF CAREER Award recipient Kavli Fellow by the National Academy of Sciences Forbes 30 Under 30 (2022) Rising Star in Data Science by Frontiers in Big Data ACM SIGKDD Doctoral Dissertation Award Runner-Up WWW Best Paper Award Honorable Mention

• Asynchronous learning technologies and environments
• WWW-based education
• VLSI placement and routing
• +۴ مورد دیگر

Donna Brown serves as a Research Associate Professor in the College of Engineering at the University of Illinois Urbana-Champaign, with her office located in the Electrical and Computer Engineering Building (2054 ECE Bldg). She maintains active roles in teaching, research, and academic advising within the university's engineering framework. Her academic credentials include: PhD in Computer Science from the Massachusetts Institute of Technology (1978) Dr. Brown's research program integrates theoretical computer science with educational technology, focusing on asynchronous learning systems, web-based education platforms, and VLSI design methodologies. Her algorithmic work spans parallel/distributed architectures, approximation techniques, and graph theory, consistently examined through the lens of societal technology impacts. This interdisciplinary approach bridges engineering rigor with pedagogical innovation. Her distinguished recognition includes: Outstanding Young Woman of America (1984) Campus Award for Innovation in Undergraduate Instruction Using Educational Technologies (1999) College of Engineering Advisor's List for advising excellence (1993, 1996) Dr. Brown has demonstrated exceptional commitment to student mentorship through multiple advising excellence awards, while her 1999 campus-wide teaching honor specifically acknowledges pioneering integration of educational technologies in undergraduate instruction. These accolades reflect sustained contributions to both curriculum development and individualized student guidance within engineering education.

• Artificial Intelligence
• Trustworthy Federated Learning
• Collaborative AI
• +۳ مورد دیگر

Dr. Han Yu is an Associate Professor at the College of Computing and Data Science (CCDS), Nanyang Technological University (NTU), Singapore. He leads the TrustFUL Research Lab, focusing on trustworthy federated learning and ethical AI systems. His work bridges AI, data privacy, and collaborative computing, with over 300 publications in top conferences/journals. He holds prestigious fellowships and chairs roles in IEEE and AAAI. Dr. Yu earned his PhD in Computer Science from NTU in 2014, followed by postdoctoral research at the Lee Kuan Yew Fellowship. His research has been recognized with multiple awards, including World's Top 2% Scientists in AI and the TOYP Singapore honor. Education: PhD (2014), BEng (2007), both from NTU's School of Computer Engineering. Professional roles include Associate Editor of IEEE TNNLS and sponsorship roles in IJCAI/WWW conferences. He has secured grants exceeding S$10M for projects like federated learning in healthcare and edge-cloud networks. His lab develops tools like FedVision and CrowdFL, addressing real-world challenges through federated learning frameworks. Dr. Yu also contributes to AI ethics education, teaching courses on ethical AI governance and sustainable computing. His research spans federated learning security, multi-modal LLMs, and healthcare applications, with recent breakthroughs in quantum federated learning and privacy-preserving algorithms. Over 25 PhD students have graduated under his mentorship, contributing to industry and academia. Awards highlight his impact, including the Innovative Application of AI Award (AAAI 2025) and recognition in federated learning’s foundational work.

Reza Zafaraniمشاهده پروفایل

دانشیار

• Big Data Analytics
• Data Mining
• Web Mining
• +۵ مورد دیگر

Reza Zafarani is an Associate Professor in the Department of Electrical Engineering and Computer Science at Syracuse University, where he directs the Data Lab. He holds a Ph.D. from Arizona State University. His research focuses on the intersection of data mining, machine learning, and social sciences, particularly in analyzing large-scale networks and social media data. Key areas of interest include big data analytics, social network analysis, and behavior modeling. He has authored influential works such as the textbook Social Media Mining: An Introduction (Cambridge University Press, 2014) and pioneered research in fake news detection, network robustness, and graph representation learning. Notable awards include the NSF CAREER Award (2020) and NVIDIA/Google education grants. His work spans theoretical advancements (e.g., spectral network analysis) and applied solutions (e.g., noise-enhanced machine learning). Recent research highlights include higher-order network models, adversarial graph attacks, and interpretable network representations. He advises students like Shengmin Jin and Xinyi Zhou, whose work addresses critical challenges in network science and misinformation detection. Reza Zafarani actively contributes to the academic community through tutorials (e.g., WWW/SDM conferences) and open-source datasets like CHECKED, enabling reproducible research in misinformation studies.

• Blockchain Technology
• Cryptocurrency Forensics
• Decentralized Finance (DeFi)
• +۴ مورد دیگر

Bernhard Haslhofer is a Professor at the University of Vienna, affiliated with the Department of Information Systems within the Faculty of Computer Science and Mathematics. His research focuses on blockchain technology, cryptocurrency analytics, and digital libraries, with a particular emphasis on forensic applications of blockchain, decentralized finance (DeFi), and semantic web technologies. He co-leads the Cryptoasset Analytics Workshop (CAAW) and contributes to projects like GraphSense, a cryptoasset analytics platform. His work spans academic collaborations with institutions worldwide, including contributions to standards like ResourceSync and Open Annotation Collaboration (OAC) models. Research interests include machine learning applications in finance, decentralized governance mechanisms, and improving the interoperability of digital resources. Publications highlight trends in DeFi composability, blockchain forensics for crime mapping, and ethical considerations in AI-driven cryptocurrency analysis. He has organized major workshops at venues like The Web Conference (WWW) and Financial Cryptography (FC), fostering interdisciplinary dialogue between academia and industry. No notable scientific awards are explicitly listed in the provided text, though his extensive publication record reflects significant contributions to the field.

Hanrui Zhangمشاهده پروفایل

استادیار

• Economics and Computation
• Algorithmic Game Theory
• Machine Learning Theory
• +۳ مورد دیگر

Hanrui Zhang is an Assistant Professor of Computer Science and Engineering at the Chinese University of Hong Kong. Previously, he was a visiting faculty researcher at Google Research NYC (OMEGA Market Algorithms team), a postdoctoral fellow at Simons Laufer Mathematical Sciences Institute (SLMath, UC Berkeley), and completed his PhD at Carnegie Mellon University under Vincent Conitzer. His research focuses on Economics and Computation, particularly learning/decision-making in strategic environments, mechanism design for online platforms, and auction theory. He holds a BEng from Tsinghua University’s Yao Class, advised by Wei Chen and Pingzhong Tang. Research Interests: Strategic learning in complex environments, algorithmic game theory, online auctions, mechanism design, and computational social choice. His work combines tools from theoretical computer science, microeconomic theory, and machine learning to address practical problems with rigorous mathematical foundations. Publications: Recent work includes studies on nonbossy mechanisms, strategic classification, Bayesian persuasion, and autobidding systems in auctions. His papers span venues like NeurIPS, EC, AAAI, and WWW, reflecting contributions to both theory and applications. Awards: Best Student Paper at ESA 2020, Honorable Mention at HCOMP 2022. Teaching: CSCI5350 (Advanced Topics in Game Theory, Spring 2025). Advises PhD students Yilin Li and Chenran Yang (both 2025-). Professional Contributions: Reviews for AAAI, NeurIPS, EC, and journals like Algorithmica and Games and Economic Behavior. Active in conference organizing (e.g., WWW, WINE).

Hermann Maurerمشاهده پروفایل

استاد

• Networked Multimedia Systems
• Societal Implications of Informatics
• e-Learning
• +۷ مورد دیگر

Hermann Maurer is Full Professor of Computer Science at Graz University of Technology (since 1978), with additional roles as Honorary Visiting Professor at Danube University and Honorary Research Fellow at University of Auckland (till 2012). He has held leadership positions including Dean of Computer Science (2004-2007) and Chair of Informatics Section in Academia Europaea (2007-2012). Key Institutions: Graz University of Technology, Austria-Forum, JOANNEUM RESEARCH, Academia Europaea Academic Honors: Member of Finnish Academy of Sciences, Cross of Honour for Arts and Science (Austria), multiple honorary doctorates Research Focus: Networked Interactive Digital (NID) Books, Austria-Forum (1.1M entries), hypermedia systems, anti-monopoly digital frameworks, and societal impacts of technology. He pioneered second-generation web systems and co-founded conferences like ED-MEDIA and I-KNOW. Key Trends in Publications: Recent works emphasize NID systems, interactive encyclopedias, web reliability, and the future of digital libraries. His research spans e-learning, information integration, and combating web-based plagiarism. Scientific Awards: Honorary Doctorates (St. Petersburg, Karlsruhe, Calgary) AACE Fellowship (2003) Integrata Prize (2000) Enter Prize (1999) Advising & Grants: Supervised over 400 M.Sc. and 50 Ph.D. theses. Led multimillion-dollar projects including Hyperwave AG, COSTOC, and Austria-Forum. His work on optical storage and color-graphic microcomputers (MUPID) exemplifies technological innovation.

Serafeim Chatzopoulosمشاهده پروفایل

پژوهشگر

• Data Mining
• Scientific Databases
• Scientometrics
• +۵ مورد دیگر

Serafeim Chatzopoulos is a Researcher at the Information Management Systems Institute (IMSI) of the ATHENA Research Center since 2014 and a software engineer at OpenAIRE AMKE since 2022. He holds a PhD in Computer Science from the University of the Peloponnese (2022), an MSc in Computer Systems, and a BSc in Informatics and Telecommunications from the University of Athens. His research focuses on data mining, scientific databases, scientometrics, and big data management, with notable contributions to systems like OpenAIRE Graph and BIP! Finder. His work emphasizes improving academic search engines, citation analysis, and researcher assessment tools. Key projects include developing algorithms for expert recommendation (VeTo toolkit), impact-based literature search (BIP! Finder), and scalable data science tools like SciNeM and SmartDataLake. He has collaborated on EU and national research initiatives, leveraging technologies such as Apache Spark, Java, and React.js. Chatzopoulos has co-authored over 30 publications in venues like TPDL, WWW, and VLDB, addressing challenges in scientific impact measurement, metadata integration, and open science infrastructure. His PhD dissertation explored data mining in scholarly networks under Prof. Christos Tryfonopoulos' supervision. He remains active in advancing tools for academic transparency and reproducibility, including narrative CV frameworks and article deduplication systems.

• Computer Science
• Theoretical Computer Science
• Algorithms
• +۲ مورد دیگر

James Baxter serves as a Research Fellow within the Department of Computer Science at the University of York, based in office CSE/010-3 at Deramore Lane, York YO10 5GH, UK. His contact phone number is +44 (0)1904 325437, and he is part of the university's research staff structure under the "Research Fellows and Research Associates" category. His research spans core areas of Computer Science , with emphasis on Theoretical Computer Science , Algorithms , Formal Methods , and Programming Languages . These interests align with the department's focus on foundational computing principles. Further details about his work are available through his personal web page .

Huy Voمشاهده پروفایل

دانشیار

• Big Data Systems
• Data Science
• High-Performance Computing
• +۳ مورد دیگر

Huy Vo is an Associate Professor in the Department of Computer Science at the City College of New York (CCNY), part of the City University of New York (CUNY) system. His research focuses on Big Data Systems, Data Science, High-Performance Computing, Large-Scale Visualization, and Scalable Display Building. He is affiliated with CCNY's School of Engineering and maintains an active website at https://www-cs.ccny.cuny.edu/~hvo/ . His work spans urban analytics, visualization technologies, and machine learning applications. Notable contributions include analyzing global urban road networks using GNNs, developing semiconductor-specific large language models (SemiKong), and optimizing fleet management for ride-pooling in NYC. He also explores public health technologies, such as asthma self-management apps and pandemic policy impact visualizations. Vo's research often integrates interdisciplinary approaches, combining geospatial data, computational modeling, and real-time systems. His recent work highlights trends in scalable computing, AR visualization, and data-driven decision-making in urban environments. No scientific awards are explicitly mentioned in the provided text. His advising and grants sections remain unspecified, but his extensive publication record indicates active research collaborations and grant-funded projects. The SAT-Hub project exemplifies his focus on smart urban infrastructure, while his work on visualization frameworks like Unitypic underscores innovations in interactive 3D systems.

Panagiotis G. Ipeirotisمشاهده پروفایل

استاد

• Crowdsourcing
• Human-AI Collaboration
• Data Science
• +۱۱ مورد دیگر

Panagiotis G. Ipeirotis is a Professor and George A. Kellner Faculty Fellow at the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. He is also affiliated with the Center for Data Science and the Computer Science department at NYU. His work bridges computer science, data science, and business analytics, with a focus on integrating human and machine intelligence. PhD in Computer Science, Columbia University (2004) MSc in Computer Science, Columbia University (2001) BSc in Computer Engineering & Informatics, University of Patras, Greece (1999) His research centers on crowdsourcing, human-AI collaboration, online labor markets, and social media analytics . He is widely recognized as a pioneer in human computation and has developed foundational techniques for ensuring data quality in crowd-sourced environments. His interdisciplinary work combines insights from computer science, economics, and social psychology to model user behavior and improve decision-making systems. His recent publications explore algorithmic fairness in hiring, theoretical voting models in crowdsourcing, and the economic impact of user-generated content. These works reflect a consistent trend toward building robust, ethical, and scalable human-machine systems that enhance data quality and decision accuracy across domains. Notable scientific awards include: Lagrange Prize in Complex Systems (2015) SIGKDD Test of Time Award (2020) NSF CAREER Award Best Paper Award at WWW 2011 Best Paper at SIGMOD 2006 Multiple best paper recognitions in Management Science and KDD Professor Ipeirotis has led significant research initiatives, including a $1.5 million Google Research Grant to integrate crowdsourcing with machine learning for visual search. He advises graduate students and contributes to academic leadership through editorial and collaborative roles. His work has been featured in prominent media outlets such as Forbes, WIRED, and Bloomberg Businessweek, highlighting its real-world impact. He is actively involved in research labs and initiatives at NYU, particularly those focused on data science, behavioral research, and computing. His affiliations with the Center for Research Computing and the Center for Behavioral Research underscore his interdisciplinary approach to solving complex problems at the intersection of technology and human behavior.

Gulden Uchyigitمشاهده پروفایل

مدرس ارشد

• Statistical Machine Learning
• Anomaly Detection
• Data Mining
• +۱۳ مورد دیگر

Gulden Uchyigit is a Principal Lecturer at the School of Architecture, Technology and Engineering at the University of Brighton, with a strong academic background including a PhD in Computer Science from Imperial College London, an MSc in Advanced Computing and a BSc in Computer Science from King's College London, and an MBA from the University of Sussex. Her research focuses on machine learning applications, particularly in the domains of recommender systems, user modelling, personalization systems, anomaly detection, and text mining. She has published extensively on topics such as adaptive interaction, data mining for web personalization, and agent-based recommendation systems. The trend in her recent publications (2008–2017) centers on intelligent personalization technologies, emphasizing user modelling through concept trees, feature selection for text classification, and the design of recommender systems for research papers and mass customization environments. These works reflect a consistent focus on enhancing user experience through data-driven adaptation. PhD in Computer Science, Imperial College London MSc Advanced Computing, King's College London Bachelor in Computer Science, King's College London MBA, University of Sussex Scientific Awards and Honors: No scientific awards mentioned in the provided text. She has supervised five research works, indicating active involvement in student mentoring, particularly in areas related to machine learning and recommender systems. Although no specific grants or funding sources are listed, her sustained research output suggests engagement in funded or institutionally supported projects. She expresses interest in supervising future PhD students in machine learning applications for assisted daily living and anomaly detection. No dedicated lab or research team is explicitly mentioned in the profile, but her collaborative publications suggest participation in interdisciplinary research networks.