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نمایش ۳۱-۴۵ از ۳۰۰ استاد راهنما

اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Alan Misloveمشاهده پروفایل

استاد

Max Planck Institute for Software SystemsGermany

Algorithmic Auditing
Fairness
Privacy
+۴ مورد دیگر

Alan Mislove is a Professor of Computer Science at Northeastern University and serves as Senior Associate Dean for Academic Affairs and Interim Executive Director of the Institute of Experiential Artificial Intelligence (EAI). He is also a core faculty member of the Cybersecurity and Privacy Institute and EAI at Northeastern. His research focuses on algorithmic auditing, fairness, and privacy in online systems. Notably, he has testified before Congress on algorithmic discrimination, challenged the CFAA in ACLU litigation, and contributed to landmark FTC and DOJ cases against Meta. His work has been funded by the NSF, Army Research Office, Google, and Amazon Web Services. Mislove’s academic leadership includes roles in Khoury College, where he supports over 2,000 students and faculty. He has advised numerous PhD students, including current research scientist Piotr Sapieżyński. His lab explores algorithmic transparency, PKI security, and middlebox traffic differentiation. Key awards include the 2017 ACM SIGCOMM Test of Time Award and recognition for work on QUIC protocol evaluation. He has authored over 150 publications, including seminal studies on Facebook ad discrimination and Uber’s surge pricing. His teaching spans systems, networks, and social computing courses, emphasizing experiential learning. Mislove actively serves on program committees for IMC, PETS, and other top conferences, and contributed to policy as Deputy U.S. CTO for Privacy (2023–2024).

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Xiaodong Liuمشاهده پروفایل

استاد

Edinburgh Napier UniversityUnited Kingdom

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.

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Rahat Masoodمشاهده پروفایل

مدرس

University of New South WalesAustralia

Dr. Rahat Masood is a Lecturer at the School of Computer Science & Engineering (CSE), UNSW Sydney. Her research focuses on cybersecurity, including privacy-preserving technologies, authentication mechanisms, critical infrastructure protection, and network security analysis. She holds a PhD in Information Security and Privacy from UNSW (Data61-CSIRO, Australia), an MS in Computer and Communication Security from NUST, Pakistan, and a B.Sc. in Software Engineering from the University of Engineering & Technology, Pakistan. Her academic contributions span theoretical and applied cybersecurity domains. Dr. Masood’s educational background includes: PhD: Information Security and Privacy (UNSW, Data61-CSIRO, Australia) MS: Computer and Communication Security (NUST, Pakistan) B.Sc.: Software Engineering (University of Engineering & Technology, Pakistan) Her research interests emphasize privacy technologies, authentication systems, and securing distributed energy resources. Recent work includes developing frameworks for quantifying privacy risks and analyzing social media manipulations. She employs data-driven methodologies and machine learning to address challenges such as WiFi device tracking and federated learning security. In her publications, she highlights trends in privacy controls usability, satirical news detection using multilingual models, and threat modeling for critical infrastructure. These studies underscore her commitment to bridging cybersecurity theory with real-world applications. No scientific awards are mentioned in the provided texts. Her teaching and supervision roles at UNSW are active, though specific student advisees or grant details are not listed. She is affiliated with Data61-CSIRO through her PhD and contributes to interdisciplinary cybersecurity efforts within CSE.

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Julian Fierrezمشاهده پروفایل

استاد

Autonomous University of MadridSpain

Julian Fierrez is a Full Professor at the School of Engineering, Universidad Autonoma de Madrid. With an h-index of 74 and over 20,000 citations, his work spans biometrics, signal/image processing, artificial intelligence, and human-computer interaction. Key research areas include: Biometric anti-spoofing and DeepFakes detection Mobile and behavioral biometrics Bias/fairness in AI systems Biometric applications in e-health and education Security in multimodal biometric systems His recent publications show strong focus on deep learning applications for biometric security, with specific subfields including fake detection, keystroke authentication, facial analysis for Parkinson detection, and privacy-preserving AI. He serves as Associate Editor for multiple IEEE and Elsevier journals. Scientific distinctions include: IAPR Young Biometrics Investigator Award (2017) Miguel Catalan Award to Best Researcher under 40 (2017) EURASIP Best PhD Award (2012) EBF European Biometric Industry Award (2006) Prof. Fierrez leads the BiDA Lab and supervises students like Ruben Tolosana and Aythami Morales. Current projects include BBforTAI (Biometrics and Behavior for Unbiased & Trustworthy AI) and PRIMA (Privacy Matters). He also contributes to standardization efforts in biometric evaluation.

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Stuart Gibsonمشاهده پروفایل

مدرس ارشد

University of KentUnited Kingdom

Dr. Stuart Gibson is a Senior Lecturer in Physics and Astronomy at the School of Physics and Astronomy, University of Kent. He is the co-inventor of the EFIT-V facial composite system, widely adopted by UK police constabularies and international agencies. His academic contributions span interdisciplinary research bridging forensic science, computational methods, and machine learning. Research Interests: Forensic applications of digital image processing Machine learning in natural sciences Facial composites for criminal investigations Medical image analysis Computer vision with security applications Teaching: Stuart teaches numerical and computational methods, mathematical techniques for physical sciences, and digital forensics. His pedagogical focus integrates theoretical frameworks with practical forensic and computational tools. Publications & Collaborations: Over his career, Dr. Gibson has published extensively in journals such as Pattern Recognition Letters , ACS Nano , and Utilities Policy . His work includes innovations in evolutionary algorithms, facial composite systems, and applications of machine learning to muon spectroscopy and Raman spectroscopy.

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Mohammad Ali Salahuddinمشاهده پروفایل

دانشگاهی

University of WaterlooCanada

Mohammad Ali Salahuddin is a Research Assistant Professor at the David R. Cheriton School of Computer Science, University of Waterloo , specializing in networking and machine learning. He holds a Ph.D. in Computer Science from Western Michigan University (2014), with prior academic roles at Université du Québec à Montréal and Concordia University. His research spans 5G network slicing, vehicular networks, and secure content delivery systems. Education: Ph.D. (2014, Western Michigan University); M.S. (2003, Western Michigan University); M.S. (2001, SZABIST); B.S. (1999, FAST-NUCES) Dr. Salahuddin's research focuses on 5G/6G network softwarization , autonomous threat mitigation , and machine learning for network management . His work integrates reinforcement learning and federated learning for scalable solutions in SDN/NFV , IoT , and edge computing . Recent studies address data drift in encrypted traffic classification and DDoS detection using outlier exposure-based federated learning . He has received multiple best paper awards at IEEE/IFIP NOMS (2023, 2022), IEEE CNOM (2021), and Kenneth C. Sevcik Outstanding Student Paper Award (ACM SIGMETRICS, 2021). His NSF-funded projects include vehicular cloud resource management and localization techniques. Dr. Salahuddin actively contributes to academic service as Vice-Chair of IEEE KW Section's Communications Society and TPC member for top conferences.

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János Kertészمشاهده پروفایل

استاد

Central European UniversityHungary

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.

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Stefan Schulteمشاهده پروفایل

استاد

Hamburg University of TechnologyGermany

Prof. Dr.-Ing. Stefan Schulte is a Full Professor at Hamburg University of Technology, leading the Institute for Data Engineering and the Christian Doppler Laboratory Blockchain Technologies for the Internet of Things (CDL-BOT). He holds a diploma in Economics and a Bachelor's in Computer Science from the University of Oldenburg, followed by a Master's in Information Technology (with Merit) from the University of Newcastle. After completing his PhD at TU Darmstadt in 2010, he held roles as Postdoctoral Researcher at TU Wien, Assistant Professor (tenure-track), and eventually Associate Professor before joining TU Hamburg in 2021. His research focuses on data engineering, blockchain technologies applied to IoT, elastic computing, and quality-of-service (QoS) aspects in smart systems. Notable contributions include work on fog computing, federated learning, and cross-blockchain interoperability. He has published over 140 papers in top-tier venues like IEEE Transactions on Services Computing and ACM Computing Surveys. Key awards include Best Paper Awards at the IEEE International Conference on Blockchain (2020) and the European Conference on Service-Oriented and Cloud Computing (2023). Prof. Schulte chairs major conferences such as the IEEE International Conference on Fog and Edge Computing (ICFEC 2025) and serves on editorial boards for journals like IEEE Transactions on Services Computing. He leads CDL-BOT, a lab exploring blockchain applications in IoT and manufacturing. His industrial collaborations include projects like SIMPLI-CITY (smart mobility) and CREMA (cloud-based manufacturing). Current research emphasizes blockchain interoperability, federated learning frameworks, and edge-AI systems. He actively reviews proposals for the German Research Foundation, EU programs, and industry initiatives.

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Malcolm Heywoodمشاهده پروفایل

استاد

Dalhousie UniversityCanada

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.

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Jun Liمشاهده پروفایل

استاد

University of OregonUnited States

Jun Li is a Professor at the University of Oregon within the College of Arts and Sciences . He serves as the Director of the Network & Security Research Laboratory and Founding Director of the Center for Cyber Security and Privacy. His academic career spans over 100 peer-reviewed publications and significant contributions to cybersecurity, networking, and distributed systems. PhD (UCLA, 2002) - Outstanding Doctor of Philosophy M.E. (Chinese Academy of Sciences, 1995) - Presidential Scholarship B.S. (Peking University, 1992) His research focuses on Cyber Security & Privacy , Computer Networking , and Distributed Systems , particularly examining: DDoS defense mechanisms Internet routing security DNS security Blockchain applications IoT security protocols Social bot detection Recent work (2023-2024) emphasizes adaptive DDoS filtering, cryptojacking detection, and security frameworks for smart homes. His publications span top-tier venues like IEEE Transactions on Dependable and Secure Computing , ACM Transactions on Internet of Things , and INFOCOM . Scientific honors include: ACM Recognition of Service Award University of Oregon Faculty Excellence Award Ripple Faculty Fellowship Multiple best paper awards He has received funding from the NSF (including CAREER award), DHS , Intel , Narus , Ripple , and the State of Oregon . His work extends to patents in hybrid peer-to-peer security protocols.

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Michael Sirivianosمشاهده پروفایل

دانشیار

Cyprus University of TechnologyCyprus

Michael Sirivianos is an Associate Professor at the Cyprus University of Technology (CUT), where he serves as Dean of the School of Engineering and Technology. He holds a PhD in Computer Science from Duke University (2010) and leads research in cybersecurity, disinformation detection, and social media analysis. He coordinates multiple EU-funded projects including ReCRED (Horizon 2020) and ENCASE (Marie Curie RISE), securing over €3M in research funding. Research Focus His work spans: Cybersafety : Detection of cyberbullying, hate speech, and inappropriate content targeting children Trust Systems : Device-centric authentication and blockchain applications Disinformation Analysis : Graph-based detection of fake news and state-sponsored manipulation Scalable Systems : Distributed databases and network infrastructure Achievements & Recognition Best Paper Award (2019) for work on state-sponsored disinformation Distinguished Paper (2018) for fringe web community analysis Spotlight Session recognition (2020) for child protection research Featured in NYT, Washington Post, and Wired for YouTube content analysis Leadership Co-directs the Network Systems Research Lab, serves on the Board of CYENS Centre of Excellence, and coordinates the Fact-check Cyprus Centre against Disinformation.

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Shivakant Mishraمشاهده پروفایل

استاد

University of ColoradoUnited States

Shivakant Mishra is a Professor in the Department of Computer Science at the University of Colorado, Boulder, and currently on leave as a Program Director at the NSF's CSR (Computer Systems Research) program. He holds roles as Site Co-Director of the NSF IUCRC Pervasive Personalized Intelligence Center and co-founded the Colorado Research Center for Democracy and Technology. Affiliations: Department of Computer Science, College of Engineering and Applied Sciences Professional Roles: NSF Program Director, Center Leadership Education: Ph.D. in Computer Science (University of Arizona), M.S. (Southern Illinois University), B.Tech. (IIT Bombay). Research focuses on distributed systems, edge computing, socio-technical systems for environmental justice, CyberSafety, and technology's role in democracy. Key projects include the C70 community impact study and smart agriculture systems. His work integrates technology with societal challenges, such as mitigating highway construction impacts and combating cyberbullying. Teaching includes courses on operating systems, distributed systems, and special topics like democracy through technology. Advised Ph.D. students Fei Hu and Jinpeng Miao. Professional activities include organizing conferences (e.g., DSN 2017, CyberSafety workshops) and serving on NSF panels.

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Suzanne R. Blackمشاهده پروفایل

پژوهشگر ارشد

University of EdinburghUnited Kingdom

Dr. Suzanne R. Black is a Postdoctoral Research Associate at the University of Edinburgh, working within the CoSTAR Foresight Lab. Her research sits at the intersection of humanities inquiry and computational methods, focusing on digital literary culture, fanfiction, and creative industries data analysis. She holds a PhD from the University of Edinburgh where her doctoral work examined 21st-century digital literary networks. PhD in English Literature, University of Edinburgh Her research interests center on digital literary culture , fanfiction archives , and queer/feminist theories applied to contemporary reading practices. She pioneers computational approaches like topic modeling to analyze large-scale datasets from platforms such as Goodreads, investigating how readers construct literary value and negotiate canonicity in digital spaces. Her work reveals how fan communities dynamically define 'classics' through emotional engagement and collective discourse. Recent publications demonstrate a cohesive trajectory in cultural analytics and digital literary studies , blending computational techniques with critical theory to examine pandemic impacts on festivals, NFT ownership models, and DEI initiatives in creative sectors. A recurring theme is the tension between algorithmic systems and human-centered cultural production. Fellowship at the Institute for Advanced Studies in the Humanities Dr. Black has secured research funding through institutional fellowships supporting her Goodreads fanfiction analysis. While not formally advising graduate students, she actively shapes academic discourse through invited lectures at Newcastle University, the University of Edinburgh, and Porty Pride events. Her grant-funded projects often involve cross-disciplinary collaboration with data scientists and social researchers. As a core member of the CoSTAR Foresight Lab, she contributes to the Edinburgh Futures Institute's mission of data-driven creative industry innovation. Her work integrates digital humanities methodologies with cultural analytics to address structural biases in digital heritage preservation and fanfiction archiving.

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Audris Mockusمشاهده پروفایل

استاد

University of TennesseeUnited States

Audris Mockus is the Ericsson-Harlan D. Mills Chair Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. He holds a PhD in Statistics from Carnegie Mellon University and has extensive experience in software engineering research, particularly in Digital Archaeology and Open Source Software analysis. His work focuses on understanding developer behavior, software evolution, and global software development challenges. Education: PhD in Statistics, Carnegie Mellon University, 1994 MS in Applied Mathematics, Moscow Institute of Physics and Technology, 1991 BS in Applied Mathematics, Moscow Institute of Physics and Technology, 1988 Research Interests: Mockus specializes in recovering and analyzing digital traces of software development to study developer behavior, software quality, and project dynamics. His research includes quantifying open-source supply chains, measuring developer expertise, and improving software defect prediction. He has pioneered methods for analyzing version control systems and global software teams. Awards and Grants: Recipient of multiple best paper awards at ICSE, FSE, and other conferences. Lead PI on NSF grants totaling over $2M for projects like the World of Code (WoC) infrastructure and forensic image curation. Patents in data visualization and software defect prediction. Advising and Labs: Mockus has advised PhD students on topics like npm ecosystem analysis and forensic image clustering. His research group collaborates with institutions like the National Institute of Justice on projects like Image Cloud Platform for forensic data curation.

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Mark Stampمشاهده پروفایل

استاد

San José State UniversityUnited States

Mark Stamp is a Professor in the Department of Computer Science at San Jose State University (SJSU), part of the College of Engineering. His research focuses on information security, machine learning, malware analysis, and cryptography, with a strong emphasis on applying these fields to cybersecurity challenges. He has authored multiple textbooks, including Information Security: Principles and Practice and Introduction to Machine Learning with Applications in Information Security . Education: PhD in Mathematics (Texas Tech University, 1992), MS in Mathematics (Texas Tech, 1988), BS in Computer Science (Morningside College, 1983). Research interests include malware detection using machine learning, cryptographic algorithms, and adversarial attack analysis. His work spans theoretical research and practical applications, such as developing tools for malware clustering, steganography analysis, and secure communication protocols. Recent projects include AI-driven precision agriculture and detecting AI-generated content. Teaching includes courses on information security (CS 166), machine learning (CS 171), and cryptography. He advises numerous graduate and undergraduate students, many of whom contribute to his research in cybersecurity and AI. Publications span over 200 papers in journals like Journal of Computer Virology and Hacking Techniques and IEEE Transactions . He has also contributed to conferences and edited several Springer volumes on AI and cybersecurity. His work often bridges academic research with real-world security challenges.

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