جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Recommender Systems
• Information Systems
• +۲ مورد دیگر

Dietmar Jannach is a Full Professor at the University of Klagenfurt, Austria, affiliated with the Institute for Artificial Intelligence and Cybersecurity where he leads the Research Group for Information Systems. His academic roles include membership in the university's Senate and Curricular Commissions for Liberal Arts and Information Management. His research spans: Core Areas : Artificial Intelligence, Recommender Systems, and Software Engineering. Methodological Focus : Algorithm reproducibility, fairness in AI, sequential recommendations, and hybrid learning models. Emerging Interests : Generative AI for group decision support, ethical recommender systems, and foundation model applications. Jannach's recent publications critically evaluate reproducibility challenges in AI research, advocate for calibrated recommendations to mitigate bias, and explore agentic paradigms in group recommender systems. He emphasizes real-world validation, with studies on deployment challenges and developer experiences in software processes. He actively contributes to academic governance and mentors through research groups, though specific student advisees are not listed. Contact via Dietmar.Jannach@aau.at .

• Computer Science
• Machine Learning
• Data Science
• +۴ مورد دیگر

Yong Zhang is affiliated with Tsinghua University's Research Institute of Information Technology in Beijing, China. His research focuses on machine learning, optimization algorithms, edge computing, and their applications in areas like time series analysis, federated learning, and sensor networks. He has collaborated on projects involving neural networks, scheduling problems, and privacy-preserving techniques. Education: Yong Zhang earned a PhD in Computer Science and Engineering from Fudan University in 2007. His academic career includes roles at institutions like the Chinese Academy of Sciences and the University of Hong Kong, reflecting a strong interdisciplinary background. Research Contributions: His work spans theoretical computer science, algorithm design, and applied machine learning. Notable areas include developing efficient scheduling algorithms for energy systems, creating robust federated learning frameworks for industrial demand forecasting, and advancing methods for sentiment analysis using multimodal data. He has also contributed to biomedical engineering through smartphone-based health monitoring systems. Collaborations: He frequently collaborates with researchers at institutions like the University of Electronic Science and Technology of China, Nanyang Technological University, and The Hong Kong Polytechnic University. Key projects involve data caching optimization in edge computing, distributed algorithms for dynamic networks, and combinatorial optimization problems. Labs & Future Work: His team explores cutting-edge topics in AI-driven systems, including trust-aware machine learning, distributed resource allocation, and real-time data processing for IoT applications. Current research emphasizes scalable solutions for complex optimization challenges in both academic and industrial settings.

Lizhen Quمشاهده پروفایل

مدرس

• Natural Language Processing (NLP)
• Privacy-Preserving Methods
• Neuro-Symbolic AI
• +۴ مورد دیگر

Dr. Lizhen Qu is a Lecturer at Monash University’s Faculty of Information Technology, part of the AIM Lab. His research focuses on robust and privacy-preserving neuro-symbolic methods for NLP and multimodal applications, including causal reasoning in dialogue systems, legal AI, digital health, and social NLP. Previously, he worked at Data61/CSIRO and completed his PhD at Saarland University and the Max-Planck-Institute for Informatics. Education: PhD in Computer Science from Saarland University and Max-Planck-Institute for Informatics. Research interests include integrating deep learning with logical reasoning, causal discovery, and ethical AI applications. He leads projects like TMLGenAI (Trusted Generative AI) and HARNESS (Neuro-Symbolic Systems), addressing model robustness and societal impact. Projects: TMLGenAI (2024–2026), HARNESS (2023–2027), and Accessible Data Exploration for Blind People (2023–2027) Contributions: Developed benchmarks like LazyReview and ACCESS, and co-organized ACL and IJCNLP workshops Research trends span causal discovery in NLP, federated learning for legal systems (e.g., FedLegal), and multimodal security. His work aligns with UN SDGs for innovation and health.

Dario Fioreمشاهده پروفایل

استاد پژوهشی

• Cryptography
• Computer Security
• Cloud Computing Security
• +۳ مورد دیگر

Dario Fiore is an Associate Research Professor at the IMDEA Software Institute in Madrid, Spain. He holds a Ph.D. in Computer Science from the University of Catania, Italy (2010), and has held postdoctoral positions at institutions including Max Planck Institute for Software Systems (Germany), New York University (USA), and École Normale Supérieure (France). His research focuses on Cryptography and Security, particularly designing provably-secure cryptographic primitives for Cloud computing applications, including secure data delegation, homomorphic authenticators, zero-knowledge proof systems, functional encryption, and homomorphic encryption. He advises multiple PhD students and has led projects like the ERC Consolidator Grant PICOCRYPT (2021–2026). He has been recognized with awards such as the ACM SIGMM Test of Time Honourable Mention (2022) and the IET Information Security Premium Award (2020). Education: Ph.D. in Computer Science, University of Catania, Italy (2010) Visiting Student: IBM T.J. Watson Research Center and New York University (USA) Research Interests Fiore’s work bridges theoretical and practical aspects of Cryptography, emphasizing efficiency and real-world applications. Key areas include: Secure delegation of computation/data to Cloud systems Homomorphic encryption and authenticators Zero-knowledge proof systems (e.g., zkSNARKs) and functional encryption Cryptography for privacy-preserving machine learning and decentralized systems Grants & Projects ERC Consolidator Grant PICOCRYPT (2021–2026) NEC Laboratories Europe collaboration (2024) ESPADA and PRODIGY EU projects Awards ACM SIGMM Test of Time Honourable Mention (2022) IET Information Security Premium Award (2020) ACM CCS 2022 Top Reviewer Award Labs & Teams Fiore leads a dynamic research group with active collaborations in cryptography, including postdocs, PhD students, and industry partnerships with Protocol Labs, Nomadic Labs, and Tezos Foundation.

Ahmed Sherifمشاهده پروفایل

دانشیار

• Cybersecurity
• Cryptography
• Autonomous Vehicles
• +۲ مورد دیگر

Ahmed Sherif is an Associate Professor specializing in cybersecurity and privacy-preserving systems, likely affiliated with the University of Southern Mississippi. He holds a Ph.D. from Tennessee Technological University (2017) and M.S. from Egypt Japan University of Science & Technology (2014). His research focuses on cryptographic solutions for autonomous vehicles, IoT networks, and smart grid security. Research interests span privacy-preserving systems across transportation and critical infrastructure, with emphasis on authentication protocols and hardware security. Teaches courses including Digital Logic, Network Security, and Advanced Security Techniques. Publications show strong focus on privacy engineering in emerging technologies (2021-2025), particularly in vehicular/drone networks and medical AI. No awards listed. Research includes federated learning for medical diagnosis and hardware-accelerated security frameworks.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Li Wei is a distinguished academic affiliated with Tsinghua University, with a focus on interdisciplinary research spanning artificial intelligence, machine learning, and computer vision. His work often intersects with medical informatics, remote sensing, and signal processing, demonstrating a commitment to advancing technological solutions in healthcare, environmental monitoring, and engineering systems. Research interests include deep learning applications in clinical diagnostics, satellite data analysis for climate modeling, and optimization of energy storage systems. He has contributed to innovative solutions in areas such as UAV-enabled edge computing, privacy-preserving blockchain protocols, and thermal-based surveillance systems. His collaborative projects often involve multidisciplinary teams across institutions. Publications reflect a strong emphasis on practical applications, such as mobile health tools for tumor recognition, transformer-based super-resolution techniques for oceanography, and AI-driven risk classification models for respiratory diseases. While no specific awards or grants are listed, his prolific output across top-tier journals indicates sustained research impact. Professional activities include contributions to conferences like RecSys, MICCAI, and AAAI, and editorial roles are implied through his extensive publication record. Collaborations with industry partners (e.g., in energy systems and medical imaging) suggest engagement with real-world problem-solving.

• Artificial Intelligence
• Extended Reality
• Computer Science
• +۳ مورد دیگر

Pasquale Cascarano is a fixed-term Assistant Professor at the University of Bologna , affiliated with the Department of the Arts . His research bridges computer science with creative industries (cinema, art, fashion) and biomedical applications, focusing on Artificial Intelligence and Extended Reality paradigms. He collaborates with national and international institutions on interdisciplinary projects. Institutional Affiliation: University of Bologna Academic Role: Assistant Professor (fixed-term) Research Interests center on integrating AI and XR into creative sectors and healthcare, with emphasis on: Generative AI for immersive environments Medical imaging and diagnostics Digital heritage preservation XR-based educational tools 3D visualization techniques Creative technology applications Recent Publications highlight trends in: Medical XR for surgical training AI-driven image restoration LLM integration with AR/VR Privacy in collaborative AR Fashion and cultural heritage digitization Deep learning for video enhancement

Urs Hengartnerمشاهده پروفایل

دانشیار

• Information Privacy
• Computer Security
• Network Security
• +۳ مورد دیگر

Urs Hengartner is an Associate Professor at the Department of Computer Science, University of Waterloo. His research focuses on information privacy, computer and network security with emphasis on smartphones, IoT, and machine learning-based authentication systems. He holds a Ph.D. (2005) and M.Sc. (2003) from Carnegie Mellon University, and a Diploma from ETH Zürich (1997). His work spans Adaptive security attacks on ML systems Implicit user authentication frameworks Privacy-preserving technologies for location and genomic data Secure authentication systems resilient to voice/spoofing attacks Recent publication trends show a strong focus on adversarial attack detection (e.g., watermarking evasion, diffusion model attacks) and context-aware authentication systems . His frameworks like MRAAC and SHRIMPS address multi-stage authentication challenges in mobile ecosystems. Key contributions include frameworks for evaluating multi-user authentication systems (SHRIMPS), risk-aware access control (MRAAC), and novel defense strategies against collaborative robot traffic fingerprinting. His work bridges security mechanisms with user-centric design principles.

Katerina Sotirakiمشاهده پروفایل

استادیار

• Theoretical Cryptography
• Post-Quantum Cryptography
• Complexity Theory
• +۴ مورد دیگر

Katerina Sotiraki is an Assistant Professor in the Department of Computer Science at Yale University, associated with the Archimedes AI research group. Her work focuses on theoretical cryptography, post-quantum cryptography, complexity theory, and secure computation. She holds a Ph.D. (2020) and M.S. (2016) in Computer Science from MIT, and a B.S. in Applied Mathematics (2013) from the National Technical University of Athens. Prior to Yale, she was a postdoc at UC Berkeley working with Raluca Ada Popa and Alessandro Chiesa, under the guidance of Vinod Vaikuntanathan at MIT. Research Interests : Design and analysis of cryptographic protocols resilient to quantum computing Efficient zero-knowledge proofs and succinct arguments Algorithmic game theory and fair division problems Lattice-based cryptography and post-quantum security Awards & Honors : Winner of 7th iDASH Competition (Track 3) - 2018 Chateaubriand Fellowship - 2018 Paris C. Kanellakis Fellowship (MIT EECS) - 2013 Key Contributions : Developed HOLMES - a system for secure collaborative learning Pioneered lattice-based succinct arguments with polylogarithmic verification Advanced understanding of modulo-p arguments through topological methods Labs & Teams : Active member of the Archimedes AI group at Yale, focusing on secure computation and cryptographic protocols.

Liang Xueمشاهده پروفایل

استادیار

• Applied Cryptography
• Cybersecurity
• Data Security and Privacy
• +۴ مورد دیگر

Liang Xue is an Assistant Professor in the School of Information Technology at York University. She holds a PhD in Electrical and Computer Engineering from the University of Waterloo (2022) and completed a postdoctoral fellowship at the University of Guelph’s School of Computer Science (2022–2024). Her research focuses on applied cryptography, blockchain security, privacy-preserving AI, and cybersecurity in cloud and IoT systems. She has published in top-tier journals like IEEE Transactions on Dependable and Secure Computing, and conferences such as IEEE International Conference on Communications. Her work addresses challenges in data privacy, secure authentication, and regulatory compliance in decentralized systems. Recent projects include privacy-enhancing technologies for access control, blockchain-based data trading frameworks, and federated learning with privacy guarantees. She actively contributes to standards for cybersecurity in smart cities and next-generation wireless networks.

• Cyber Security
• Network Security
• IoT Security
• +۳ مورد دیگر

Dr. Vasileios Vasilakis is a Senior Lecturer in Cyber Security at the University of York's Department of Computer Science. He holds a PhD in Electrical & Computer Engineering from the University of Patras, Greece. His research focuses on IoT Security, Mobile Network Security, and SDN Security, with contributions to projects like the EC H2020 SESAME and RIFE initiatives. He has supervised numerous PhD students and contributed to academic service roles such as Guest Editor in IEICE Transactions and Program Committee member for IEEE ICC and GLOBECOM. His work spans secure systems design, network protocols, and cybersecurity defenses, with recent publications addressing SDN-based intrusion detection, IoT attack mitigation, and ransomware analysis. Affiliations: York Interdisciplinary Centre for Cyber Security (YiCS), University of York Education: PhD in Electrical & Computer Engineering (University of Patras, 2011) Research Interests His research emphasizes practical cybersecurity solutions for modern networks, including: IoT Security: Developing authentication and intrusion detection mechanisms for RPL-based networks SDN/NFV Security: Designing defenses against ransomware and distributed attacks Wireless Network Security: Analyzing vulnerabilities in 5G and UAV communication systems Academic Contributions He has organized special sessions at IEEE conferences, co-authored over 120 publications, and reviewed PhD theses at multiple institutions. Current projects include secure edge computing in 5G networks and lightweight authentication schemes for IoT protocols like MQTT.

• Cyber-Physical Systems
• Internet of Things (IoT) Security
• Computer Architecture
• +۵ مورد دیگر

Hokeun Kim is an Assistant Professor in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), part of the Ira A. Fulton Schools of Engineering. He previously held positions at Hanyang University (2021-2023) and worked in industry roles at Google, LinkedIn, and HP Labs. His research focuses on cyber-physical systems, IoT security, and computer architecture, with a particular emphasis on safety and security aspects of time-sensitive systems. Education: Ph.D. in EECS, University of California, Berkeley (2017) M.S. in EECS, Seoul National University (2012) B.S. in Computer Science and Engineering, Seoul National University (2010) Research Interests: Kim’s work spans secure IoT frameworks, real-time embedded systems, and edge computing. He develops tools like the Secure Swarm Toolkit (SST) and Lingua Franca, addressing challenges in distributed system security, interoperability, and performance. Key Contributions: Authored over 30 peer-reviewed publications in top venues like IEEE Transactions, ACM Conferences, and DATE. Received the ACM/IEEE Best Paper Award (IoTDI 2017) and IEEE Micro Top Picks Honorable Mention (2017). Active in organizing conferences (e.g., DATE, FDL) and serves on technical committees for top journals/conferences. Teaching: Courses include Computer Architecture I/II, Real-Time Embedded Systems, and IoT design at both undergraduate and graduate levels.

• Databases
• Spatial Data Management
• Geospatial Systems
• +۳ مورد دیگر

Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.

Chris Cliftonمشاهده پروفایل

استاد

• Data Mining
• Data Science
• Machine Learning
• +۴ مورد دیگر

Chris Clifton is a Professor of Statistics (Courtesy) at Purdue University, affiliated with the Department of Computer Sciences and the Department of Statistics. He holds a Ph.D. from Princeton University (1991) and has over 30 years of experience in academia and industry. His research focuses on data privacy, data mining, and database security, with notable contributions to privacy-preserving techniques and differential privacy. Education: B.S./M.S. in Computer Science and Engineering, MIT (1986) M.A./Ph.D. in Computer Science, Princeton University (1988/1991) Research Interests: Data privacy (including anonymization, disclosure limitation), machine learning fairness, privacy-preserving data mining, database security, and interdisciplinary applications in healthcare and logistics. Awards: IEEE Fellow (2020) ACM Distinguished Member (2017) Multiple College of Science Awards (Teaching, Teamwork, Mentoring) Grants & Advising: Led NSF-funded research on algorithmic bias and privacy metrics. Advised numerous graduate students in data science and security. Played roles such as Interim Department Head (2023–2024) and served on NSF program director teams. Labs/Teams: Active in Purdue’s Data Privacy Research Group and collaborations with industry partners like MITRE and NSF initiatives.

Jaron Minkمشاهده پروفایل

استادیار

• Usable Security
• Machine Learning
• System Security
• +۵ مورد دیگر

Jaron Mink is an Assistant Professor at Arizona State University's School of Computing and Augmented Intelligence, leading the Human Aspects in cyber Protections and Privacy Lab (Happy Lab). His research focuses on the intersection of usable security, machine learning, and system security, particularly exploring how human factors impact ML security. He holds a Magna Cum Laude from UCLA and completed graduate studies at the University of Illinois at Urbana-Champaign (UIUC), where he served as a Teaching Assistant and Guest Lecturer in Computer Security courses. Education: University of California, Los Angeles (UCLA) - Bachelors (Magna Cum Laude) University of Illinois at Urbana-Champaign (UIUC) - PhD in Computer Science Research Interests: Human-ML Interaction Dynamics Deepfake Detection and User Perception Adversarial ML Defense Adoption Barriers User Trust in Security Tools Privacy-Preserving Technology Design His work bridges technical security solutions with human-centric usability challenges, emphasizing real-world application in social media, fitness apps, and enterprise systems. Recent Publications: Focus on quantifying sociodemographic influences in security behaviors, analyzing deepfake moderation biases, and evaluating ML security tool usability across industries. Awards: Google Research Scholar Program NSF Graduate Research Fellowship Teaching: Instructs courses like Information Assurance and Trustworthy Human-ML Interaction at ASU, previously teaching Computer Security II at UIUC. Labs/Teams: Directs the Happy Lab, actively recruiting PhD students to tackle challenges in human-centric cybersecurity and privacy. Hobbies: Passionate about vintage dance styles (Lindy Hop, Blues, Balboa) and strategic board games like Spirit Island and War of the Ring .