جست‌وجوی استاد و پژوهشگر

• Open Science
• Digital Educational Research
• Information Infrastructures
• +۳ مورد دیگر

Prof. Dr. Marc Rittberger serves as Deputy Managing Director of the Leibniz Institute for Research and Information in Education (DIPF) since April 2025, concurrently holding a Professorship for Information Management at DIPF and Darmstadt University of Applied Sciences since 2005. His institutional leadership spans multiple directorial roles including Managing Director (2008-2012) and current oversight of the Education Information Center. Rittberger's research focuses on open science infrastructure development , digital educational architectures , and information management systems . His work examines practical implementations of Open Educational Resources (OER), bibliographic database optimization for systematic reviews, and metadata standards for distributed learning environments. Recent publications demonstrate growing emphasis on AI-driven knowledge graphs and machine learning applications in scholarly communication. Analysis of his 15 most recent publications reveals consistent engagement with Open Science implementation challenges Systematic review methodology in educational research Teacher practices in digital resource sharing Quality metrics for research infrastructures His work bridges theoretical informatics with practical educational applications, particularly in K-12 digital transformation contexts. Rittberger actively shapes research policy through key committee roles including: Spokesperson of Leibniz Association's Open Science Strategy Forum (since 2023) Deputy Spokesperson of Standing Commission for Scientific Infrastructure Facilities (since 2024) Former Scientific Advisory Board positions at GESIS, ZBW, and Know Center His academic supervision extends through leadership in major research initiatives including Digi-EBF (Digitalization in Education), ABIBA (reducing educational barriers), and EduArc (digital educational architectures). Current projects emphasize open scholarship quantification, AI-based search infrastructures, and internationalization of educational research infrastructures.

Balaji Palanisamyمشاهده پروفایل

دانشیار

• Data Privacy
• Privacy-Preserving System Design
• Cloud Computing
• +۳ مورد دیگر

Dr. Balaji Palanisamy is an Associate Professor at the University of Pittsburgh's School of Computing and Information, specializing in data privacy, privacy-preserving system design, and scalable resource management for distributed systems and cloud computing. He co-directs the Laboratory of Research and Education on Security Assured Information Systems (LERSAIS) and serves as an Associate Editor for IEEE Transactions on Services Computing . He is also the chair of the IEEE Communications Society's Pittsburgh chapter. Research Interests : Data Privacy Privacy-Preserving System Design Blockchain and DeFi Security Cloud and Edge Computing Secure Resource Management Smart Contract Resilience Scientific Awards : 2017 IBM Faculty Award Best Paper Award, 2017 IEEE BigData Congress Best Paper Award, 2015 IEEE/ACM CCGrid Best Paper Award, 2012 IEEE CLOUD Recent Publications (2024–2025) focus on privacy-preserving data release mechanisms, DeFi security, blockchain governance, and edge computing. His work integrates cryptography, smart contracts, and decentralized systems to address vulnerabilities in cryptocurrency, IoT, and cloud infrastructures.

Victor-Alexandru Darvariuمشاهده پروفایل

مدرس

• Reinforcement Learning
• Graph Learning
• Combinatorial Optimization
• +۴ مورد دیگر

Victor-Alexandru Darvariu is a Retained Lecturer in Engineering Science at Jesus College, University of Oxford, and a Postdoctoral Research Assistant at the Oxford Robotics Institute. He is also an Honorary Research Fellow at UCL Computer Science. His academic journey includes a PhD in Computer Science from University College London (UCL, 2023), an MSci in Computer Science from the University of Birmingham (2018), and pre-university education in Moldavia, Romania. His research focuses on developing AI techniques for decision-making problems, with a particular emphasis on reinforcement learning, graph theory, combinatorial optimization, and multi-agent systems. Recent projects include mission planning for underwater gliders and causal discovery using reinforcement learning. His work bridges theoretical foundations with practical applications in robotics, operations research, and causal inference. Key publications include contributions to causal discovery via model-based RL, bike-sharing system optimization, and graph reinforcement learning for combinatorial problems. Code and data for many projects are publicly available. He actively shares research updates on platforms like Bluesky, highlighting collaborations and methodological innovations.

Dong Wangمشاهده پروفایل

دانشیار

• Social Sensing
• Human-Centered AI
• AI for Social Good
• +۹ مورد دیگر

Roles and Affiliations: Associate Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, affiliated with the Siebel School of Computing and Data Science, Illinois Informatics, and multiple research centers including NCSA and CSBS. Director of the Social Sensing & Intelligence Lab. Education: PhD in Computer Science from University of Illinois Urbana-Champaign. Research Focus: Social sensing, human-centered AI, responsible AI, edge computing, and smart cities. Develops systems that integrate human and machine intelligence for societal challenges like disaster response, healthcare, and environmental sustainability. Authored two books on social sensing and edge computing. Recent Trends in Publications: Focuses on AI fairness, crowd-AI collaboration, misinformation detection, and federated learning applications. Recent work addresses demographic bias mitigation in AI models and symbiotic learning frameworks for healthcare policy adherence. Awards and Honors: NSF CAREER Award, ARO YIP Award, Google Faculty Award, and multiple teaching excellence recognitions. Senior Member of IEEE. Grants and Projects: Active projects include metacognitive calibration interventions, disaster scene assessment systems, and environmental contaminant modeling. Completed work includes smart water crowdsensing and health policy projects. Labs and Teams: Leads the Social Sensing & Intelligence Lab, collaborating on interdisciplinary initiatives across computer science, informatics, and environmental science.

Bryan HOOI Kuen-Yewمشاهده پروفایل

استادیار

• Machine Learning
• Graph Algorithms
• Anomaly Detection
• +۷ مورد دیگر

Bryan HOOI Kuen-Yew is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), affiliated with the School of Computing and the Institute of Data Science. His research focuses on enhancing the reliability and applicability of machine learning systems, particularly in trustworthiness, graph learning, and real-world applications such as cybersecurity and biomedical informatics. He holds a PhD in Machine Learning from Carnegie Mellon University (2019), and dual MSc (Computer Science) and BSc (Mathematics) degrees from Stanford University (2014). His work has been recognized with awards including the KDD 2016 Best Paper Award (Research Track) and the ECML-PKDD 2018 Runner-Up Best Student Data Mining Paper Award. Research Interests: Trustworthy AI: Mitigating hallucinations, biases, and distribution shifts in large language models. Graph Algorithms: Developing robust methods for graph mining, anomaly detection, and fraud detection in dynamic networks. Multimodal Learning: Leveraging vision-language models and knowledge graphs for improved reasoning and security. Biomedical Applications: Applying AI to healthcare challenges, including drug design and medical data analysis. Selected Awards: ECML-PKDD 2018 Runner-Up Best Student Data Mining Paper Award KDD 2016 Best Paper Award (Research Track) NUS Computing Teaching Excellence Award (2024) Advising and Grants: Actively supervises PhD and master’s students, with research supported by grants focusing on AI security, graph learning, and trustworthy systems. Collaborates on projects such as PhishAgent (multimodal phishing detection) and UniGraph (cross-domain graph foundation models).

• Machine Learning
• Algorithm Engineering
• Combinatorial Optimization
• +۳ مورد دیگر

Dr. Deepak Ajwani is an Assistant Professor in the School of Computer Science at University College Dublin . His research focuses on leveraging machine learning techniques for solving combinatorial optimization problems, with expertise in algorithm design, algorithm engineering, and graph algorithms. He holds a PhD from the University of Saarland, Germany, and has held postdoctoral positions at Aarhus University (Denmark) and University College Cork (Ireland). He also worked at Nokia Bell Labs (2012–2018) on learning systems for unstructured content analysis. Dr. Ajwani has received over 50 peer-reviewed publications in top-tier conferences and journals. He serves on the editorial board of the Machine Learning journal and is a frequent senior program committee member for conferences like WWW, IJCAI, AAAI, and ALENEX. He is a funded investigator at the SFI Centre for Research Training in Machine Learning (ML-Labs) . His teaching excellence has been recognized through multiple nominations for the UCD Teaching and Learning Award . His students have won the Franz Geiselbrechtinger Medal (2023, 2024) for best final-year projects in theoretical computer science. Key grants include the Irish Research Council and IBM Research Grant (2010–2012) for graph partitioning techniques in exascale computing. His research explores integrating machine learning with optimization algorithms, graph neural networks, and algorithm engineering for real-world applications.

Soumyabrata Devمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Remote Sensing
• +۶ مورد دیگر

Soumyabrata Dev is an Assistant Professor in the School of Computer Science at University College Dublin (UCD), where he leads the THEIA lab focusing on interdisciplinary research in computer vision, machine learning, and remote sensing. His work addresses challenges in climate science, environmental monitoring, solar forecasting, and healthcare. He holds a PhD from Nanyang Technological University (Singapore) and has held postdoctoral roles at Trinity College Dublin and ADAPT SFI Research Centre. His education includes a B.Tech. (summa cum laude) from National Institute of Technology Silchar, India, and a visiting doctoral experience at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Prior to academia, he worked as a network engineer at Ericsson India (2010–2012). Research interests span image processing, environmental data analytics (e.g., air/water quality), solar energy forecasting, and AI-driven solutions for sustainable development. He collaborates globally with institutions in academia, industry, and government, aligning with UCD’s strategic goals for impactful innovation. He is an SFI Funded Investigator at ADAPT SFI and a UCD Climate Fellow (2024–2026). His scientific awards include the 2024 Stanford/Elsevier Top 2% Scientists List. He advises numerous PhD/MSc students on topics like coastal monitoring, air quality modeling, and AI for sustainability. THEIA Lab’s projects include solar irradiance forecasting, knowledge graph-based climate data platforms, and blockchain-enhanced healthcare systems. He teaches modules on Operating Systems, Wireless Sensor Networks, and Augmented/Virtual Reality. His work bridges theory and application, emphasizing real-world impact in climate action and renewable energy.

Anh Tu Hoangمشاهده پروفایل

پژوهشگر

• Privacy-Preserving Systems
• Data Security
• Blockchain Technology
• +۳ مورد دیگر

Dr. Anh Tu Hoang is a Postdoctoral Researcher at TU Hamburg's Institute for Data Engineering, specializing in privacy-preserving systems, blockchain technologies, and knowledge graph anonymization. He holds a PhD from the University of Insubria, Italy, and degrees from Vietnam National University. His research addresses data security challenges in machine learning, federated learning, and decentralized systems. Education: Bachelor of Information Technology, University of Science, Vietnam National University (Ho Chi Minh City) Master of Information Systems, University of Science, Vietnam National University PhD in Computer Science, University of Insubria, Italy (2020) Research Focus: His work centers on developing mechanisms to protect privacy and security during data sharing, machine learning training, and blockchain operations. Key contributions include time-aware anonymization for knowledge graphs and blockchain-based federated learning frameworks. Projects include CDL-BOT and Orchid, focusing on decentralized learning and privacy-aware systems. Publications: Over 12 peer-reviewed articles in top venues like IEEE ICDE, ACM TOPS, and IEEE TDSC, with a focus on anonymization techniques, federated learning, and cryptographic protocols. Recent work explores zero-knowledge proofs for self-sovereign identity and proximity marketing privacy. Expertise: Combines cryptographic methods with data privacy principles to address modern challenges in distributed systems, social networks, and collaborative AI environments.

• Cloud Computing
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Prasad Calyam is the Greg L. Gilliom Professor of Cybersecurity in the Department of Electrical Engineering and Computer Science at the University of Missouri-Columbia. He also serves as Director of the Center for Cyber Education, Research, and Infrastructure (Mizzou CERI). His research focuses on Cloud Computing, Machine Learning, Artificial Intelligence, Cyber Security, and Advanced Cyberinfrastructure. He has led over $39 million in sponsored projects from NSF, DOE, NSA, and industry partners. Dr. Calyam's work emphasizes interdisciplinary collaboration, particularly in cybersecurity education for neurodivergent populations, drone networking, and blockchain-based systems. He has developed tools like 'Narada Metrics' for network monitoring and contributes to platforms like FlyNet for drone applications. His publications span AI-driven cybersecurity solutions, VR-based learning environments, and edge computing optimization. He holds IEEE Senior Member status and serves as an Associate Editor for IEEE Transactions on Network and Service Management. His recent projects address tactical edge network security, federated learning for drone swarms, and AI-driven anomaly detection. He actively fosters inclusive cybersecurity education through VR curricula and collaborates on medical data governance frameworks.

Tianyi Liمشاهده پروفایل

استادیار

• Digital Twin
• Graph Neural Networks
• Energy Engineering
• +۸ مورد دیگر

Tianyi Li is a Tenure Track Assistant Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design and the Daisy - Center for Data-intensive Systems. His research focuses on data engineering, spatiotemporal systems, and digital twins, with applications in energy, transportation, and vehicular networks. Key areas include trajectory data management, graph neural networks, compression techniques, and federated learning. He has contributed to over 37 publications, including works on digital twin frameworks for autonomous driving and smart grids, trajectory prediction systems, and efficient time series compression. His research integrates machine learning, distributed systems, and cybersecurity to address challenges in data-intensive environments. Key Projects: Digital twin-enabled voltage control, federated vehicular networks, trajectory simplification frameworks. Labs: Involved with the Daisy Center, focusing on data-centric systems and scalable solutions. Technical Expertise: Blockchain systems, energy systems optimization, and real-time data processing.

Wenbin Zhangمشاهده پروفایل

استادیار

• Societal Aspects of AI
• Generative AI
• Health Informatics
• +۵ مورد دیگر

Wenbin Zhang is an Assistant Professor in the Knight Foundation School of Computing & Information Sciences at Florida International University and an Associate Member at the Te Ipu o te Mahara Artificial Intelligence Institute. His research focuses on the theoretical foundations of machine learning with societal impact, including fairness, generative AI, health informatics, and interdisciplinary applications in healthcare, digital forensics, and energy. He has received awards such as the NSF CRII Award and recognition in the AAAI’24 New Faculty Highlights. Zhang serves in organizing committees for major conferences like AAAI, WSDM, and AIES, contributing to academic leadership. Ph.D., University of Maryland, Baltimore County (2020) Research interests span societal aspects of AI, generative models, and fairness-aware machine learning. His work bridges theory and practice, addressing ethical challenges in AI deployment across domains like healthcare and cybersecurity. Key contributions include frameworks for fair graph learning, bias mitigation in LLMs, and adaptive pruning techniques for large models. Publications emphasize fairness in ML systems, digital forensics, and interdisciplinary applications. Over 50 papers span venues like FAccT, ICDM, and AAAI, with multiple best-paper recognitions. His NSF-funded research highlights innovation in fair AI and robust model adaptation. Scientific Awards: NSF CRII Award, FAccT’23 Best Paper Candidate, ICDM’23 Best Paper Academic service includes roles as Travel Award Chair (AAAI’24), Volunteer Chair (WSDM’24), and Student Program Chair (AIES’23). Teaching and mentorship are integral to his mission, fostering next-generation AI researchers through rigorous training in ethics-driven innovation. Labs/Teams: Active collaborations with the Te Ipu o te Mahara Institute, focusing on AI ethics and societal impact.

• Artificial Intelligence
• Semantic Web
• Machine Learning
• +۲ مورد دیگر

Eugenio Di Sciascio is a Full Professor and Scientific Coordinator at Politecnico di Bari. His research spans artificial intelligence, semantic web technologies, machine learning, and explainable AI, with applications in recommender systems, healthcare, and cybersecurity. As evidenced by 413+ publications, his work frequently bridges theoretical computer science with practical implementations. Recent research trends focus on trustworthy AI systems, particularly in healthcare diagnostics and recommendation security. His 2024-2025 publications demonstrate strong emphasis on explainable AI methodologies in medical applications (e.g., BRAINEX for brain age prediction, MORIX for mortality inference) and adversarial robustness in recommender systems. Additional work explores neurotechnology interfaces and semantic web foundations.

• Artificial Intelligence
• Optimization Techniques
• Reinforcement Learning
• +۲ مورد دیگر

Michele Ciavotta is an Associate Professor at the University of Milano-Bicocca's Department of Computer Science, Systems, and Communication, specializing in AI-driven optimization for complex systems. His research integrates reinforcement learning, graph neural networks, and metaheuristics applied to distributed computing and physical systems like smart mobility and production lines. Research spans cloud/edge computing optimization, industrial production systems, smart city applications, and graph-based learning methods. Recent publications demonstrate focus on decentralized AI systems, geospatial data processing, and hypergraph neural networks for chemical and urban applications. Extensive involvement in European R&D projects addresses challenges in cloud computing infrastructure, Industry 4.0 implementations, and distributed AI solutions.

Guiling Wangمشاهده پروفایل

استاد

• Machine Learning
• Blockchain Technology
• Deep Learning
• +۹ مورد دیگر

Dr. Guiling Wang is a Distinguished Professor of Computer Science and Associate Dean of Research and External Relations at New Jersey Institute of Technology (NJIT). She holds a Ph.D. in Computer Science and Engineering from The Pennsylvania State University (2006) and a B.S. in Software Engineering from Nankai University, China (2002). Her research focuses on machine learning, blockchain technology, deep learning, and intelligent transportation systems. Dr. Wang’s work spans interdisciplinary areas such as: Machine Learning and Reinforcement Learning Blockchain-based systems and decentralized applications Intelligent traffic signal control and autonomous systems Data privacy and cybersecurity Generative adversarial networks (GANs) and vision-language models Her recent research emphasizes practical solutions for: Optimizing traffic management through multi-agent reinforcement learning Developing robust watermarking techniques for digital security Applying LLMs for financial decision-making and portfolio management Designing blockchain frameworks for vehicular networks and edge computing Notable contributions include: Proposing novel architectures like MCFN for high-performance watermarking Advancing RAGIC for risk-aware stock interval construction Developing PairUpLight for multi-intersection traffic coordination Pioneering blockchain-based systems iBCTrans and VDKMS for vehicular networks Dr. Wang’s research is supported by grants from federal agencies and industry collaborators, with applications in smart cities, financial technology, and healthcare systems.

Franco Raimondiمشاهده پروفایل

استاد

• Software Verification
• Multi-agent Systems
• Formal Methods
• +۱ مورد دیگر

Franco Raimondi is Professor of Computer Science at Middlesex University and an Amazon Scholar in the Prime Video Automated Reasoning Group. His research focuses on formal verification methods, multi-agent systems, and security protocols. He leads projects in automated reasoning for distributed systems, including blockchain alternatives and privacy-preserving social platforms (CoSMed). His educational innovations include developing the MIRTO open-source robotics platform for coding instruction and digital twin technologies for scalable computer science education. His work bridges theoretical foundations with industrial applications in security and cloud computing.