جستجوی استاد راهنمای دانشگاهی

Zhize Li is a tenure-track Assistant Professor at the School of Computing and Information Systems, Singapore Management University (since Nov 2023), focusing on optimization, federated learning, and AI privacy. Previously, he held research roles at Carnegie Mellon University, King Abdullah University of Science and Technology (KAUST), and visiting positions at Duke University and Georgia Institute of Technology. Education: PhD in Computer Science, Tsinghua University (2019) MTech, National University of Singapore BEng, Shanghai Jiao Tong University Research Interests: Zhize Li’s work centers on large-scale/distributed/decentralized optimization and private/efficient/resilient federated learning. His research addresses challenges in communication compression, data heterogeneity, and privacy-preserving analytics in AI systems. Publication Trends: His recent articles focus on federated learning frameworks (e.g., X-VFL, SoteriaFL), optimization algorithms (e.g., SIFAR, DESTRESS), and privacy/security in distributed settings (e.g., data reconstruction attacks, three-point compressors). Keywords include Artificial Intelligence , Machine Learning , Privacy-Preserving , and Optimization . Scientific Awards: Rising Star in AI, KAUST AI Initiative (2022) Tsinghua University Outstanding Doctoral Dissertation Award (2019) National Scholarship (top 1%, 2018) Pacesetter of Outstanding Graduates (2014) Advising and Grants: Zhize Li actively recruits fully-funded PhD students, visiting scholars, and research engineers. While specific grants are not detailed, his work has been supported by multiple top-tier publications in journals like JMLR and conferences such as NeurIPS , ICML , and AAAI .

• Optimization
• Federated Learning
• AI Privacy
• +۳ مورد دیگر

Javier Verdu Mula is a Professor at the Departament d'Arquitectura de Computadors (Universitat Politècnica de Catalunya - UPC) and a key researcher at the CRAAX - Centre de Recerca d'Arquitectures Avançades de Xarxes . His work focuses on computer architecture, parallel processing, and networking systems. Fields of Research include RISC-V virtualization, multithreaded processor optimization, and performance analysis of stateful networking applications. Scientific Awards include the BDigital Global Congress (2015) and Wayra Barcelona (2012) recognitions. Collaborations span institutions like Barcelona Supercomputing Center and researchers such as Manuel Alejandro Pajuelo, Mateo Valero Cortes, and Mario Nemirovsky. His recent publications address RISC-V hypervisor extensions, deep packet processing in parallel architectures, and statistical thread assignment models. He also holds patents in hardware virtualization and resource control systems.

• Computer Architecture
• Parallel Processing
• Networking Systems
• +۳ مورد دیگر

Lu Feng is an Associate Professor of Computer Science at the University of Virginia, affiliated with the Link Lab, a center specializing in Cyber-Physical Systems (CPS). She holds a Ph.D. in Computer Science from the University of Oxford. Her research focuses on ensuring safety and trustworthiness in CPS, with applications in medical devices, autonomous robotics, and smart cities. She has received prestigious awards including the NSF CRII Award (2018) and NSF CAREER Award (2020). Her work integrates formal methods, AI, and robotics to address challenges in CPS assurance and human-machine collaboration. Notable contributions include developing risk-assessment tools for heart failure patients, predictive monitoring frameworks for CPS, and trust-aware planning algorithms for autonomous systems. She has pioneered frameworks like DP-RuL for clinical decision support systems and IrrMap for precision agriculture. Her research bridges theoretical foundations (e.g., model checking, reinforcement learning) with practical applications in healthcare, transportation, and urban systems. She collaborates across disciplines, contributing to initiatives like the Link Lab’s smart city simulations and safety-critical medical CPS assurance. Education: Ph.D., Computer Science, University of Oxford Awards: NSF CRII (2018), NSF CAREER (2020) Labs: Link Lab (Cyber-Physical Systems Center) Focus Areas: Runtime safety, human-AI trust, medical device assurance, smart city systems

• Cyber-Physical Systems
• Artificial Intelligence
• Robotics
• +۳ مورد دیگر

Vasileios Argyriou is an academic researcher affiliated with Kingston University, with extensive contributions in computer vision, image processing, and AI applications in IoT, healthcare, and smart agriculture. His work spans photometric stereo, motion estimation, federated learning, and UAV-based remote sensing. Research Interests: His research focuses on developing advanced computer vision techniques including photometric stereo for 3D reconstruction, deep learning for anomaly detection, federated learning for privacy-preserving AI, and AI-driven solutions for smart farming and healthcare. He explores motion estimation, facial recognition, and synthetic data generation for training robust models. The recent publications (2024–2025) highlight a strong trend toward applying machine learning in real-world systems—particularly federated learning for intrusion detection, UAV-based agricultural monitoring, 5G security, and medical imaging. His work integrates computer vision with IoT, wireless communications, and edge computing, demonstrating interdisciplinary innovation. Scientific Contributions: Author of a book on image and video registration with quality metrics. Extensive publication record in top journals including IEEE Transactions, Sensors, and Pattern Recognition. Pioneering work in photometric stereo, motion estimation using phase correlation, and GAN-based face reenactment. He has advised or collaborated with numerous researchers across fields, though specific student names are not listed. His work often involves large-scale data analysis, simulation frameworks, and real-time systems. He is involved in projects related to smart cities, precision agriculture, and autonomous systems. Laboratories and Teams: His collaborations suggest involvement with research groups focused on intelligent systems, computer vision, and IoT at Kingston University and beyond, particularly with Panagiotis Sarigiannidis, Thomas Lagkas, and others in networking and AI.

• Computer Vision
• Image Processing
• Photometric Stereo
• +۱۲ مورد دیگر

Professor Brian Logan is a Chair in Computing Science at the University of Aberdeen, School of Natural and Computing Sciences. He also holds a visiting position as Visiting Associate Professor at Utrecht University and an honorary Special Professor title at the University of Nottingham. His research focuses on autonomous intelligent systems and safe AI, particularly systems that adapt or learn during runtime. His research interests include: Safe AI and runtime safety of learning systems Belief revision and reasoning Search algorithms Agent-based simulation Autonomous multi-agent systems His recent work is supported by major grants from EPSRC, EU (Marie Skłodowska-Curie), ONRG/EOARD/DSTL, and NWO, covering areas such as normative cooperation among agents, causality in multi-agent systems, safe control programs for fusion reactors, and digital manufacturing platforms. He is currently supervising PhD students including Simon Castle-Green (University of Nottingham), Jan de Mooij (Utrecht University), and Giovanni Varricchione (Utrecht University), and is accepting new PhD candidates in Computing Science. His scientific leadership includes roles in: Normative cooperation among autonomous agents (ONRG/EOARD/DSTL) CAUSES: Causality in multi-agent systems (Netherlands NWO) Safe Correct-by-Construction Control Programs for FUSION reactor maintenance (EPSRC/RAIN) Elastic Manufacturing Systems (EPSRC EP/T024429/1) DiManD: Digital Manufacturing and Design Training Network (EU Marie Skłodowska-Curie) He leads the Core Research in Computing Science at his institution and is actively contributing to the advancement of trustworthy autonomous systems.

• Artificial Intelligence
• Autonomous Intelligent Systems
• Safe AI
• +۶ مورد دیگر

Dr. Alvitta Ottley is an Assistant Professor in the Department of Computer Science & Engineering at Washington University in St. Louis, with a courtesy appointment in the Department of Psychological & Brain Sciences. Her research focuses on interdisciplinary approaches to visualization systems, machine learning, and cognitive science to enhance decision-making in healthcare, intelligence analysis, and scientific discovery. She leads the AIR-D Summer Science Camp, a program aimed at underrepresented high school students in STEM. Education: PhD and MS in Computer Science from Tufts University (2016, 2013). Research Interests: Developing adaptive visualization systems that account for users' cognitive traits and goals. Her work integrates AI-driven insights with human factors to create context-aware interfaces. Recent studies explore trust dynamics in visualizations, cross-cultural visualization literacy, and the impact of chart types on legal decision-making. Awards: NSF CRII Award (2018), NSF Career Award, EuroVis Early Career Award (2022), and multiple best paper recognitions at top visualization conferences. Grants & Advising: Active in mentoring students and securing grants such as the NSF CAREER Award for context-aware systems. Her work on medical decision-making visualization received NSF support in 2018. Labs & Teams: Affiliated with the Division of Computational & Data Sciences and the Center for Trustworthy AI in CPS, focusing on ethical AI integration in visual analytics.

• Human-Computer Interaction
• Data Visualization
• Artificial Intelligence
• +۴ مورد دیگر

Tony Zhongshun Shi is an Assistant Professor in the Department of Industrial and Systems Engineering at the Tickle College of Engineering, University of Tennessee Knoxville (UT). He directs the System-driven AI Lab (SAIL) and is an Affiliated Faculty of the Machine Tool Research Center and the Institute of Advanced Materials and Manufacturing at UT. Prior to joining UT, he was a Postdoctoral Research Associate at the University of Wisconsin-Madison. Education: PhD in Management Science and Engineering, Peking University, 2017 BS in Pure and Applied Mathematics, China University of Geosciences (Beijing), 2011 His research focuses on the integration of AI, human, and complex systems through trustworthy system-driven AI methods, with applications in smart manufacturing, digital twin modeling, simulation, and engineering innovation. He emphasizes the development of AI-enabled simulation tools and digital frameworks for dynamic systems, leveraging scientific machine learning and mathematical optimization. His work bridges theoretical AI advancements with practical engineering solutions. While specific publications are not listed in the text, his research trends indicate a strong focus on AI for scientific discovery, manufacturing systems, and dynamic system control. His interdisciplinary approach combines industrial engineering principles with cutting-edge AI methodologies. Scientific Awards: No awards listed in the provided text. He teaches courses such as Artificial Intelligence for Complex Systems, AI-enabled Manufacturing Systems, Advanced Design of Experiments, and Statistical Methods in Industrial Engineering. He advises research through the System-driven AI Lab (SAIL) and collaborates with interdisciplinary centers to advance materials and manufacturing innovation. His work is supported by research in modeling, simulation, and optimization of dynamic systems. Labs and Affiliations: Director, System-driven AI Lab (SAIL) Affiliated Faculty, Machine Tool Research Center, UT Affiliated Faculty, Institute of Advanced Materials and Manufacturing, UT

• Artificial Intelligence
• Smart Manufacturing
• Digital Twin
• +۷ مورد دیگر

Liang Zhao is a Professor in the Department of Computer Science at Harbin Institute of Technology's School of Computer Science and Technology. His research spans multiple areas of natural language processing, artificial intelligence, and machine learning, with a particular focus on large language models, graph neural networks, and explainable AI systems. His research interests include natural language processing, graph neural networks, large language models, model explainability, federated learning, sparse attention mechanisms, hallucination mitigation, transformer length extrapolation, and uncertainty quantification. His work addresses fundamental challenges in modern AI systems, particularly in improving the reliability, efficiency, and interpretability of large language models. Professor Zhao's research output shows a clear trend toward addressing critical limitations in large language models, with recent work focusing on hallucination mitigation, model attribution, uncertainty quantification, and efficient attention mechanisms. His publications demonstrate strong interdisciplinary connections between NLP, machine learning theory, and practical system implementation. Through his extensive collaboration network with researchers including Xiaocheng Feng, Bing Qin, Weihong Zhong, and Yuntong Hu, Professor Zhao has established himself as a leading researcher in the Chinese NLP community. His work appears consistently in top-tier conferences including ACL, EMNLP, and NAACL.

• Natural Language Processing
• Graph Neural Networks
• Large Language Models
• +۷ مورد دیگر

Jaime Delgado is a prominent researcher with over three decades of contributions to digital rights management, healthcare information systems, and security and privacy in eHealth. With an extensive publication record spanning from 1994 to 2025, Delgado has established themselves as a leading expert at the intersection of computer science and healthcare, developing practical frameworks that enhance security, privacy, and interoperability in medical systems. Delgado's research spans multiple critical domains: Digital Rights Management and Multimedia Content Security Healthcare Information Systems and eHealth Applications Security and Privacy in Medical Data Management Ontologies and Semantic Web Technologies for Healthcare Genomic Information Systems and FAIR Data Principles Trustworthy Media Systems and Provenance Tracking Analysis of recent publications (2021-2025) reveals a strategic shift toward healthcare applications, particularly focusing on security requirements for Internet of Medical Things (IoMT), privacy-enhancing techniques for medical data, and genomic information systems. Delgado's work demonstrates consistent development of practical architectures addressing real-world security challenges in healthcare settings, with increasing collaboration with medical professionals and participation in European health informatics initiatives like the MedSecurance Project. Delgado has received recognition for contributions to standardization efforts, particularly in developing frameworks for media trustworthiness and international standards for assessing trust in digital media. Their work on the JPEG Privacy and Security framework has significantly influenced industry practices. Through extensive collaboration with researchers like Silvia Llorente (43 joint publications), Eva Rodríguez (29 publications), and Rubén Tous (26 publications), Delgado has built a strong research network across European institutions. Their work consistently combines theoretical framework development with practical implementation considerations, addressing the critical balance between security requirements and clinical workflow usability. Delgado's laboratory work centers on developing secure frameworks for medical data management, with recent emphasis on genomic information systems, provenance tracking in eHealth, and security requirements for medical IoT devices. Their research group actively participates in European health informatics initiatives and contributes to international standards development, maintaining exceptional productivity with 4-7 publications annually in recent years.

• Digital Rights Management
• Multimedia Content Management
• Healthcare Information Systems
• +۶ مورد دیگر

Muhammad El-Hindi is a researcher at the Technical University of Darmstadt , focusing on database systems , blockchain technology , and secure data management . His work bridges theoretical innovation with practical applications in cloud computing, trusted execution environments, and decentralized systems.

• Database Systems
• Blockchain Technology
• Secure Data Management
• +۵ مورد دیگر

Martin Pirker serves as a Professor and Senior Researcher at the Institute of IT Security Research within the Department of Computer Science and Security at St. Pölten University of Applied Sciences in Austria. His academic profile demonstrates extensive expertise in IT security, trusted computing, and blockchain technologies, with a particular focus on Trusted Platform Modules, boot integrity, and secure systems development. His research interests span multiple critical areas of modern cybersecurity including Trusted Computing, blockchain-based security solutions, boot integrity verification, secure system design, privacy-preserving technologies, and information security auditing. Dr. Pirker's work consistently addresses practical security challenges in contemporary computing environments, with numerous publications exploring the intersection of hardware security, software implementation, and cryptographic verification mechanisms. His recent publications reveal a strong focus on blockchain applications for security auditing, Trusted Platform Module implementations, and secure boot processes. The research trends show progression from foundational trusted computing work toward more distributed security solutions using blockchain technology and addressing emerging challenges in AI security. His publications demonstrate consistent contributions to major security conferences including ARES (International Conference on Availability, Reliability and Security) and ICISSP (International Conference on Information Systems Security and Privacy). Dr. Pirker leads or participates in several significant research projects including the Josef Ressel Center for Blockchain-Technologies & Security Management, Josef Ressel Center for Unified Threat Intelligence on Targeted Attacks (TARGET), RPM- Rusted Plattform Module, and E³UDRES² Ent-r-e-novators. These projects reflect his commitment to addressing real-world security challenges through collaborative research initiatives. He actively contributes to the academic community through teaching in the IT Security (BA) and Cyber Security and Resilience (MA) programs at St. Pölten University of Applied Sciences. His work bridges theoretical security concepts with practical implementation challenges, making significant contributions to the field of applied cybersecurity research.

• IT Security
• Trusted Computing
• Blockchain Technologies
• +۶ مورد دیگر

Dr. José María Alonso Moral is an Associate Professor at the Department of Electronics and Computation of CiTIUS-USC (Research Center for Intelligent Technologies) at the University of Santiago de Compostela. With a PhD in Telecommunication Engineering from Universidad Politécnica de Madrid (2007) and a 'Doctor Europeus' distinction, he has held prestigious positions including Ramón y Cajal researcher (2018-2022), Juan de la Cierva postdoctoral researcher (2012), and served as President of the European Society for Fuzzy Logic and Technology (EUSFLAT) and Vice-Chair of IEEE Computational Intelligence Society's Explainable Fuzzy Systems Task Force. IEEE Senior Member Associate Editor: IEEE Computational Intelligence Magazine (Q1) and International Journal of Approximate Reasoning (Q2) Board Member: ACL SIGGEN (2019-2022) His research focuses on Explainable AI and Trustworthy Artificial Intelligence , particularly through interpretable fuzzy systems and natural language generation. Key contributions include the ExpliClas web service for decision tree explanations and JFML library for IEEE 1855-2016 fuzzy system modeling. Recent publications examine counterfactual explanations, fuzzy temporal constraint networks, and feature attribution faithfulness metrics. He leads projects like TRUST (Trustworthy AI) and ReproHum (NLP Evaluation Reproducibility), emphasizing human-centered AI design and cross-disciplinary applications in healthcare and education. As an educator, he developed high school workshops on explainable AI fundamentals through CiTIUS-USC's STEM outreach program. His technical work includes developing explainable classifiers for Alzheimer's disease progression and implementing fuzzy logic systems in embedded platforms like Arduino and Raspberry Pi.

• Explainable Artificial Intelligence
• Trustworthy AI
• Machine Learning
• +۳ مورد دیگر

Dr. Sayanton Dibbo is an Assistant Professor in the Department of Computer Science at the University of Alabama, where he leads the Trustworthy AI Lab. He is also a faculty affiliate of the Alabama Center for the Advancement of AI (ALA-AI) and Alabama Cyber Institution & HPC. His academic journey includes a PhD in Computer Science from Dartmouth College (2020-2025) and an MS in Computer Science from the University of California, Riverside (2017-2019). Dr. Dibbo's research spans several critical areas in modern computing: Deep Learning and Computer Vision Secure & Trustworthy AI/ML modeling Analysis of foundation models (LLMs and Multimodal systems) Security and privacy aspects of machine learning systems Biometric-based user authentication for IoT devices His recent work focuses on developing defenses against model inversion attacks using sparse coding architectures, improving robustness in audio classification, and creating secure authentication systems for wearable devices. Dr. Dibbo's research spans multiple data modalities including images, tabular data, audio, and text, addressing critical security and privacy challenges in AI systems. Dr. Dibbo has received recognition for his work including the Dartmouth Graduate Student Council Travel Grant for ICASSP 2024 and the Cybersecurity Cluster Research Fellowship. His service to the academic community includes reviewing for Women in Computer Vision (WiCV) at ECCV 2024, Computers and Security Journal, and Neural Networks Journal. As an educator and mentor, Dr. Dibbo is actively seeking highly motivated undergraduate and graduate students to join his research team at the University of Alabama. His lab focuses on cutting-edge problems at the intersection of AI security, privacy, and robustness, offering students opportunities to work on impactful research with real-world applications.

• Deep Learning
• Computer Vision
• Secure & Trustworthy AI/ML
• +۵ مورد دیگر

Muhao Chen is an Assistant Professor in the Department of Computer Science at the University of California, Davis, and directs the Language Understanding and Knowledge Acquisition (LUKA) Lab. His research focuses on robust and minimally supervised machine learning for natural language processing, alongside accountability and security challenges in large language models. Ph.D. in Computer Science, University of California, Los Angeles (UCLA) B.S. in Computer Science, Fudan University His work emphasizes developing trustworthy AI systems through techniques like constitutional alignment, adversarial defense, and multimodal reasoning. Current trends in his publications highlight LLM security, retrieval-augmented generation, and mitigation of hallucinations and jailbreak attacks. Key collaborations and interdisciplinary applications include geospatial AI, biomedical NLP, and mechanical metamaterial design. He actively contributes to frameworks like MetaScientist for automated design and tools for evaluating radiology models. Focus on agentic memory systems ( R2D2 ) Advancements in vision-language modeling ( QLIP, Adashield ) Security benchmarks and datasets ( MuirBench, VLM-Guard )

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Patrik Hummel is an Assistant Professor at Eindhoven University of Technology, focusing on the intersection of Artificial Intelligence Ethics , Health Data Governance , and Digital Sovereignty . His work bridges technical and normative challenges in medical AI applications.

• Artificial Intelligence Ethics
• Health Data Governance
• Digital Sovereignty
• +۳ مورد دیگر