جستجوی استاد راهنمای دانشگاهی

• Service-Oriented Architectures
• Cloud Computing
• Data Management
• +۷ مورد دیگر

Professor Kyriazis Dimosthenis holds a faculty position at the Department of Digital Systems, University of Piraeus. He earned his diploma in Electrical & Computer Engineering from the National Technical University of Athens (2001) and a cross-disciplinary MSc in Techno-Economic Systems (2004). His academic rank is Professor specializing in service-oriented architectures with a focus on quality of service and workflow management. He has led European projects like BigDataStack, CrowdHEALTH, and CYBELE, addressing challenges in cloud computing, edge computing, and AI-driven solutions for healthcare, finance, and industrial sectors. His research emphasizes resilient service-oriented systems, AI explainability, and human-centric digital transformation. Notable contributions include frameworks for dynamic resource allocation in hybrid cloud/edge environments, AI applications for maritime safety, and data governance solutions for cross-sector integration. He coordinates initiatives such as the Future Internet Architecture Board and Cloud QoS&SLAs, driving advancements in federated data marketplaces and sustainable computing practices. His recent work explores Large Language Model (LLM) applications in financial decision-making, conversational AI for MLOps, and neurosymbolic systems for defect detection. He also investigates XAI methodologies, including VirtualXAI, which leverages GPT-generated personas for explainability assessment. His projects often bridge technical innovation with societal impact, such as the iHELP platform for holistic health records and SmartCHANGE for behavioral change strategies in youth health. Key themes in his publications include bias mitigation in machine learning, dynamic deployment prediction in hybrid cloud settings, and energy-efficient data spaces for mobility. He has contributed to standards like the H2020-funded IRMOS and 5GTANGO, emphasizing interoperability and fault-tolerant architectures. His work frequently intersects with EU policy frameworks, particularly in data governance and ethical AI implementation.

Qi Tangمشاهده پروفایل

پژوهشگر ارشد

• Hydrogeology
• Data Assimilation
• Earth System Modeling
• +۳ مورد دیگر

Qi Tang is a Research Fellow in the Department of Environmental Science at the University of Basel and concurrently serves as Coordinator of the Swiss Water Earth Systems PhD School at the University of Neuchâtel. His expertise spans hydrogeology, data assimilation, and Earth system modeling. He holds a PhD in Hydrogeology from RWTH Aachen University (2017) and has held postdoctoral positions at institutions including the Alfred Wegener Institute (Germany), the University of Basel, and the Chinese Academy of Sciences. Education: PhD in Hydrogeology, RWTH Aachen University, Germany (2012–2017) MSc in Hydrology and Water Resources, Beijing Normal University (2009–2012) BSc in Applied Mathematics, China Agriculture University (2005–2009) Research Interests: Qi Tang focuses on advancing coupled Earth system models through data assimilation techniques. His work integrates hydrological, oceanographic, and climatic processes to improve predictive accuracy. Key areas include river-aquifer interaction dynamics, satellite data integration in ocean-atmosphere models, and cloud computing for real-time water resource management. His research bridges theoretical modeling with practical applications in environmental monitoring and climate prediction. Publications: His articles emphasize data-driven approaches to environmental systems. Recent work highlights coupled model improvements using satellite data (e.g., ocean-atmosphere interactions), ensemble Kalman filtering for flood simulations, and Bayesian networks for precipitation modeling. These studies underscore his expertise in both computational methods and field applications. Advising & Grants: While no formal advisees are listed, his postdoctoral roles suggest involvement in mentoring junior researchers. No specific grants are mentioned in the provided texts. Labs/Teams: Affiliated with the Hydrogeological Processes group at the Center for Hydrogeology and Geothermal Energy (CHYN), University of Neuchâtel. This group specializes in geothermal energy, hydrochemistry, and stochastic hydrogeology.

Andrew Tordaمشاهده پروفایل

استاد

• Protein Structure Prediction
• Computational Biology
• Bioinformatics
• +۳ مورد دیگر

Prof. Andrew Torda leads the Biomolecular Modeling research group at the University of Hamburg's Center for Bioinformatics (ZBH). He holds a professorship and focuses on computational methods in structural biology. His work includes developing force fields for protein structure prediction, substitution matrices for sequence alignment, and threading algorithms like 'wurst.' He joined the University of Hamburg in 2002 after positions at the Australian National University and ETH Zurich. Research Interests: Low-resolution force fields for protein modeling Scoring functions and protein sequence optimization Lattice models for free energy estimation Protein sequence-structure alignment methods Software Projects: 'wurst' threading code with a web server for structural predictions Optimized amino acid substitution matrices Genome simulation tools (collaboration with Jan Wigger) Teaching & Collaboration: Teaches bioinformatics modules and mentors students on software development projects. His group actively engages in computational methods for molecular design and systems biology.

• Cryptocurrencies
• Blockchain Interoperability
• Blockchain Scalability
• +۳ مورد دیگر

Giulia Scaffino is a PreDoc Researcher in the Security and Privacy group at the Technical University of Vienna (TU Wien). Her research focuses on cryptocurrencies, blockchain interoperability, scalability, off-chain protocols, and decentralized finance (DeFi). She holds an MSc in Nuclear and Particle Physics from the University of Pavia, Italy, and is pursuing a PhD at TU Wien. Before her PhD, she worked as a Salesforce developer at Deloitte Digital in Milan. Giulia is involved in projects such as CDL-BOT (2020–2025), Browsec (2018–2024), CoRaF (2022–2025), and SFB SPyCoDe (2023–2026). Her work includes developing frameworks for cross-blockchain smart contracts, optimizing light clients for DeFi, and creating scalable blockchain bridges. She has contributed to publications on topics like IoT data marketplaces and distributed key generation using zk-SNARKs. Her research trends emphasize solving blockchain scalability challenges through innovative protocols and enhancing interoperability between different blockchain systems. Projects like Glimpse and Alba highlight her focus on efficient, secure, and decentralized solutions for modern blockchain ecosystems.

• Human-Computer Interaction
• Machine Learning
• Natural Language Processing
• +۱ مورد دیگر

Sarah Frances Homewood is an Assistant Professor (Tenure Track) in the Department of Computer Science at the University of Copenhagen, affiliated with the Human-Centred Computing research section. Her research focuses on the intersection of human-computer interaction and artificial intelligence, with applications in healthcare, natural language processing, and interpretable machine learning. Her diverse research interests span Human-Computer Interaction, Machine Learning, Natural Language Processing, and Artificial Intelligence. Recent investigations include interpretability of large language models, clinical NLP applications, fairness in recommender systems, and quantum natural language processing. Analysis of her recent publications reveals strong emphasis on NLP interpretability techniques, healthcare applications of AI, and theoretical foundations of machine learning. Her work frequently bridges fundamental computer science with practical applications in medicine and human-centered systems. Emerging research directions include quantum NLP and protein sequence modeling. Dr. Homewood's research contributes to the Machine Learning Section's focus on both theoretical foundations and applied domains including medical data analysis and information retrieval.

• Information Systems Engineering
• Logic and Computation
• Smart Grids
• +۳ مورد دیگر

Prof. Sandford Bessler is affiliated with the Technische Universität Wien's Faculty of Informatics, specifically within the Compilers and Languages department. His research focuses 75% on Information Systems Engineering and 25% on Logic and Computation. He teaches courses such as Bachelor Thesis for Informatics and Business Informatics . His work emphasizes smart grids, electric vehicle management, scheduling algorithms, and distributed systems. Research interests include optimizing energy networks, resilience in power grids, and integrating renewable energy systems. He has contributed to projects like ARTE (2012–2017) , exploring disruption-tolerant vehicle-infrastructure communication. Supervised theses include Electric vehicles recharge scheduling with time windows and A service overlay network for telematic applications . Key projects involve improving grid resilience through demand-side management and collaborative frameworks. He collaborates with industry and academia on smart grid technologies and electric vehicle infrastructure.

Josep Casanovasمشاهده پروفایل

استاد

• Modelling and Simulation
• Internet and Information Systems
• Urban Mobility
• +۴ مورد دیگر

Josep Casanovas is a Full Professor at the Statistics and Operations Research Department of the Technical University of Catalonia (UPC), affiliated with the Barcelona School of Informatics. He previously served as head of inLab FIB (2012-2020) and as dean (1998-2004) and vice-rector (2006-2011) of UPC, leading strategic initiatives in university governance and ICT policies. His research focuses on Modelling and Simulation , Internet and Information Systems , and Urban Mobility . He has led projects for the European Union, including C-ROADS Spain, REMEDiAL, and ECHORD++, addressing intelligent transport, software automation, and robotic innovation. Recent publications highlight his work on agent-based simulation for urban health, deep learning applications in traffic and energy savings, and wildfire management tools . He co-directs LogiSim and coordinates the Severo Ochoa Research Excellence Program at the Barcelona Supercomputing Center (BSC-CNS).

Yucheng Xieمشاهده پروفایل

استادیار

• Security in Machine Learning
• Smart and Mobile Healthcare
• Mobile Computing and Sensing
• +۳ مورد دیگر

Yucheng Xie is a Tenure-track Assistant Professor in the Graduate Department of Computer Science and Engineering at Yeshiva University, affiliated with the Katz School of Science and Health. He holds a Ph.D. in Electrical and Computer Engineering from Purdue University and a Master’s in Computer Science from Stevens Institute of Technology. His research focuses on Security in Machine Learning/AI Systems , Smart and Mobile Healthcare , and Mobile Computing and Sensing . Notable contributions include non-invasive wireless sensing via mmWave and Wi-Fi signals for health monitoring, adversarial attacks on activity recognition systems, and secure mobile deep learning. His recent articles explore topics like contactless human concentration monitoring, palm-based authentication, and environment-invariant eating behavior tracking. These works highlight innovations in mmWave technology, cybersecurity for AI systems, and healthcare applications. Awards : Best Paper Runner-up (IEEE Conference on Communications and Network Security, 2024) Best Paper Runner-up (IEEE International Conference on Computer Communications and Networks, 2022) Best Paper Award (EAI International Conference on IoT Technologies for HealthCare, 2019) While no advising or grant details are provided, his work emphasizes practical applications of wireless sensing and secure AI systems. No specific lab affiliations are mentioned, though his research often involves interdisciplinary collaboration.

• Data Privacy
• Cyber Security
• Cloud Security
• +۵ مورد دیگر

Professor Xun Yi is a faculty member at RMIT University's School of Computing Technologies, specializing in cybersecurity, data privacy, and distributed computing. His research focuses on privacy-preserving technologies in cloud systems, blockchain applications, federated learning, and secure communication protocols. He has published over 150 papers in top-tier journals and conferences, including IEEE Transactions on Dependable and Secure Computing. Since 2014, he has served as an Associate Editor for the IEEE Transactions on Dependable and Secure Computing, and has organized major events like the Australasian Information Security Conference (AISC) in 2015 and 2016. Professor Yi actively supervises PhD and Master's research projects, focusing on topics like secure IoT systems, privacy-aware machine learning, and blockchain-enabled frameworks. His work emphasizes practical solutions for real-world challenges in data security and privacy. Research Interests: Data Privacy, Cyber Security, Cloud Security, Wireless/Mobile Security, Applied Cryptography Blockchain-based Systems, Federated Learning, Privacy-Preserving AI Recent Contributions: Developed frameworks for secure data aggregation in smart grids and healthcare systems Advanced techniques for privacy-preserving federated learning and graph neural networks Contributed to standards for secure authentication in vehicular networks (VANETs) Supervision: Open to mentoring students in cybersecurity, privacy-enhancing technologies, and IoT security.

• Health Economics
• Health Policy Evaluation
• Payment Models
• +۱ مورد دیگر

Kees van Gool is Professor of Health Policy and Systems at the Menzies Centre for Health Policy and Economics, University of Sydney, with a joint appointment as Executive Director of the Pricing and Analytics Branch at the Independent Health and Aged Care Pricing Authority (IHACPA). He previously held positions at the University of Technology Sydney and the OECD. His work bridges academic research and national health policy implementation. His research focuses on: Health policy evaluation and system performance measurement Economic modeling of healthcare payment systems Analysis of out-of-pocket costs and insurance design impacts Geographic and socioeconomic disparities in healthcare access His publications (2013-2025) demonstrate consistent focus on health economics, cancer care financing, primary care models, and policy evaluation methodologies. Recent work emphasizes Medicare reforms, radiation oncology costs, and activity-based hospital funding. Current Projects: NHMRC Centre on Research Excellence Value Based Payments in Cancer Care The International Collaboration on Costs, Outcomes and Needs in Care (ICCONIC) NHMRC Centre on Research Excellence on Medicines Intelligence Recent Grants: 2024: Empowering patients to self-manage blood pressure (NSW Health) 2023: FMH Start-up scheme (University of Sydney) He leads health economics research at the Menzies Centre while directing national pricing analytics at IHACPA, influencing Australian health policy through data-driven approaches.

• Smart Grid
• IoT Data Management
• Cloud Resource Management
• +۳ مورد دیگر

Thanasis Papaioannou is a Senior Researcher at the STEcon lab at Athens University of Economics and Business (AUEB), where he has been since 2006. He concurrently teaches Python Programming and Android Programming at the University of Thessaly and Data Management in IoT at the Technological Educational Institute of Thessaly. His research focuses on smart grid demand-side management, IoT data management, gamification for energy conservation, and privacy mechanisms in distributed systems. Education: He holds a B.Sc. (1998), M.Sc. (2000) in Computer Science from the University of Crete, and a Ph.D. (2006) in Informatics from AUEB. He has held postdoctoral roles at EPFL (2008-2013) and positions at CERTH (2013-2016). Research Interests: His work spans IoT-enabled gamification for energy efficiency, decentralized management frameworks, and blockchain applications for trustworthy systems. Notable contributions include frameworks like ONTOCHAIN and CHArGED, addressing challenges in energy conservation and distributed trust. Publications: Over 65 papers in high-impact venues including IEEE Transactions on Smart Grid and ACM e-Energy. Recent work emphasizes blockchain-semantic convergence, decentralized IoT governance, and energy market coordination under uncertainty. Professional Impact: Project manager in 17 national/EU projects, with over 1200 citations. Advises on energy systems and decentralized technologies, emphasizing user-centric design and behavioral interventions.

• Machine Learning
• Biomedical Informatics
• Data Science
• +۳ مورد دیگر

Claus Horn is an Associate Research Scientist in the Department of Biomedical Informatics & Data Science at Yale School of Medicine. His research focuses on integrating machine learning with biomedical applications, including enzyme design and deep learning frameworks for protein-ligand interactions. Previously, he held roles at CERN (ATLAS collaboration) and Stanford University, contributing to particle physics breakthroughs like the Higgs boson discovery. He has also pioneered data science education, founding an open education platform with 3,000+ members and teaching over 300 students in AI and machine learning courses. Education: PhD in Physics (University of Hamburg, 2006), Postdoctoral Researcher at Stanford University (2010), DipEd in University Didactics (PH Zurich, 2021), MBA in Management & Leadership (HWZ Zurich, 2023). Research interests span interdisciplinary fields: developing GNN frameworks for drug discovery (e.g., GEMS), optimizing protein structures via geometric methods, and applying AI to drone signal analysis and indoor farming simulations. His particle physics work includes searches for supersymmetry, microscopic black holes, and long-lived particles using ATLAS data. Publications highlight contributions to both biomedical innovation and high-energy physics, with recent work emphasizing AI-driven systems and collaborative data science education. He has no explicitly listed scientific awards but contributed to Nobel Prize-winning Higgs boson research.

• Artificial Intelligence
• Neural Networks
• Quantization
• +۳ مورد دیگر

Hang Li is a Researcher in the Department of Molecular Biophysics and Biochemistry at Yale University’s Yale School of Medicine. Their work focuses on advancing neural network architectures, quantization techniques, and spiking neural networks (SNNs). They are affiliated with the Molecular Biophysics and Biochemistry department and contribute to interdisciplinary research in artificial intelligence and computational neuroscience. Research interests include optimizing neural networks for efficiency through quantization, exploring spiking neural networks for low-power computing, and developing methods like hybrid SNN designs, post-training calibration, and neuromorphic architectures. Their recent work addresses challenges in extreme low-bit quantization, data augmentation for object detection, and temporal coding in SNNs. Publications highlight innovations in quantization methods (e.g., TesseraQ, GenQ), spiking transformer architectures, and workload-balanced pruning strategies. While no awards are explicitly listed, their contributions to model efficiency and neuromorphic computing are notable in the field. Hang Li collaborates on projects involving neuromorphic hardware, system inconsistency benchmarking (SysNoise), and data-driven spatio-temporal analysis. Their research bridges theoretical advancements and practical applications in AI and biomedical informatics.

Dimitrios Diochnosمشاهده پروفایل

استادیار

• Supervised Learning
• Semi-Supervised Learning
• Adversarial Learning
• +۴ مورد دیگر

Dr. Dimitrios Diochnos is an Assistant Professor in the School of Computer Science at the University of Oklahoma (OU) . His research focuses on theoretical and practical aspects of machine learning, including adversarial learning, semi-supervised learning, and imbalanced data classification. Prior to OU, he was a Hobby Postdoctoral Research Fellow at the University of Virginia and a Research Associate at the University of Edinburgh. He holds a PhD in Mathematics from the University of Illinois at Chicago, an MS in Mathematics from the University of Athens, and a BS in Informatics and Telecommunications from the University of Athens. Research Interests: Dr. Diochnos works on foundational aspects of machine learning, particularly under adversarial conditions. His current projects include developing semi-supervised learning techniques, analyzing online/streaming learning algorithms, and exploring theoretical guarantees for imbalanced classification. He is also involved in the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES), focusing on trustworthy AI applications in environmental science. Awards and Recognition: He has received NeurIPS Top Reviewer awards (2023, 2019), NSF reviewer status, and the Teaching Award (2009). His postdoctoral fellowship at UVA was supported by the Hobby Endowment. He has held fellowships from the Zossima Brothers Foundation and the University of Illinois at Chicago. Service and Editorial Work: Dr. Diochnos serves as Managing Associate Editor of the Annals of Mathematics and Artificial Intelligence and has been a program committee member for major conferences like NeurIPS, AAAI, and IJCAI. He has organized events such as the Symposium on AI & ML at OU and served on the Scientific Committee for the International Olympiad in Informatics. Grants and Collaborations: His work is supported by NSF grants, including leadership in AI2ES. He collaborates with researchers in meteorology, oceanography, and climate science to address challenges in trustworthy AI for environmental systems. Labs and Affiliations: Dr. Diochnos is affiliated with the OU School of Computer Science and contributes to interdisciplinary initiatives like AI2ES. His research lab focuses on advancing machine learning theory with practical applications in adversarial robustness and environmental forecasting.

Ashok Agrawalaمشاهده پروفایل

استاد

• Computer Systems
• Distributed Algorithms
• Real-Time Systems
• +۱۰ مورد دیگر

Ashok Agrawala is a Professor in the Department of Computer Science at the University of Maryland, with joint affiliations in the University of Maryland Institute for Advanced Computer Studies (UMIACS) and the Department of Electrical Engineering. He directs the Maryland Information and Network Dynamics (MIND) Lab, a leading research group focused on real-time systems, network performance, and wireless localization technologies. Education: B.E. and M.E. in Electrical Engineering, Indian Institute of Science, Bangalore M.A. and Ph.D. in Applied Mathematics, Harvard University His research interests span computer systems, distributed algorithms, real-time operating systems, network performance modeling, traffic shaping, and indoor localization . He is renowned for pioneering the Ricart-Agrawala distributed mutual exclusion algorithm, developing the Maruti real-time operating system, and creating the Horus and Locus indoor positioning systems. His work has led to significant advances in temporal guarantees, jitter-free data delivery, and calibration-free localization. His recent publications show sustained innovation in wireless localization, context-aware computing, breath analytics, and energy-efficient IoT systems , reflecting an evolving research trajectory toward smart health and urban computing. He has authored over 200 papers and seven books, with consistent publication activity into 2024. Scientific Recognition: Fellow, IEEE Member, ACM Member, AAAS Member, Sigma Xi Agrawala has advised over thirty Ph.D. students, many of whom now lead research in academia and industry. His research has been funded by DARPA, NSF, and AFOSR, with corporate partnerships from IBM, Novell, and AT&T. He previously worked at Honeywell Information Systems, where he designed an Optical Character Reader. He has also served as a consultant to the UNDP and the Government of India, and as a technical expert in legal cases. He leads the MIND Lab, which focuses on mobile computing, wireless networks, and context-aware systems , continuing to innovate in real-time and networked systems.