جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Deep Learning
• Computer Vision
• +۷ مورد دیگر

Dr. Guy C. Hembroff is an Associate Professor in the College of Computing at Michigan Technological University, serving as the founding director of the MS in Health Informatics Program and director of the Computational Science & Engineering PhD Program. He leads the Biomedical Data Science (BDS) Lab, focusing on healthcare innovation through AI, cybersecurity, and data science. His expertise spans machine learning, medical image analysis, and healthcare interoperability. Education: PhD in Computational Science & Engineering (Michigan Tech), MPA in Public Administration (Northern Michigan University), BS in Finance and Economics (Michigan Tech). Research interests include human health-focused AI/ML models, cybersecurity in healthcare, medical image segmentation, and public health surveillance. His work emphasizes collaboration with medical institutions like Henry Ford Hospital to develop clinical decision support systems and improve disease surveillance. Recent projects include AI-driven fracture risk prediction from knee radiographs and enhancing mental health intervention efficacy through multi-source data integration. Advising includes four doctoral students in areas like medical image analysis, blockchain for patient data security, and cost-effective mental health modeling. His software projects include FHIR-enabled health information exchange systems and Tick-Talk, a crowdsourced tick disease monitoring platform. Labs/Teams: The BDS Lab integrates expertise in medicine, AI, and cybersecurity to tackle healthcare challenges, emphasizing real-world impact through industry and academic partnerships.

• Blockchain Technology
• Business Process Management
• Process Mining
• +۳ مورد دیگر

Ingo Weber is a Professor affiliated with Technische Universität München (TU Munich) and Fraunhofer Gesellschaft. His research focuses on blockchain technology, business process management (BPM), and artificial intelligence (AI), with a particular emphasis on integrating these fields. He has held former positions at TU Berlin, CSIRO Data61, and other institutions. Current affiliations: TU Munich and Fraunhofer Gesellschaft Former affiliations: TU Berlin, CSIRO Sydney, University of New South Wales, SAP Research, and University of Massachusetts Amherst Research interests include blockchain applications in business processes, process mining, AI-driven systems, and sustainability-oriented process analysis. His work explores topics such as blockchain scalability, data confidentiality, and cost-efficient process execution on next-generation blockchains like Algorand. He has pioneered frameworks like SOPA for sustainability analysis and FhGenie for confidentiality-preserving AI. Key contributions include over 200 publications in journals like IEEE Access, Future Generation Computer Systems, and ACM Transactions on Management Information Systems. Recent work emphasizes AI-augmented BPM systems and the application of large language models (LLMs) in scientific contexts. Notable projects include blockchain-based process execution engines (e.g., Caterpillar), platform architectures for multi-tenant blockchain systems, and frameworks for evaluating payment channel networks. He has collaborated extensively with industry partners and academic institutions globally.

• Software Security
• Software Testing
• Program Analysis
• +۲ مورد دیگر

Dr. Seongmin Lee is a researcher at the Max Planck Institute for Security and Privacy, specializing in software security and program analysis. Their work bridges theoretical and practical aspects of software testing, with a particular focus on automated testing techniques, dependency modeling, and genetic improvement. Research interests include: Software Security Software Testing and Fuzzing Program Analysis and Slicing Machine Learning Applications in Software Engineering Genetic Algorithms for Code Optimization Statistical and Causal Analysis of Code Behavior Recent publications (2016–2025) demonstrate a trajectory from foundational work on GPU parameter optimization to cutting-edge research on LLM-driven regression testing. Key trends include: Statistical modeling of software behavior Machine learning for bug classification and optimization Approximate analysis techniques for scalability Advancements in greybox fuzzing and coverage prediction Application of causal inference to mutation testing

Henning Frankeمشاهده پروفایل

پژوهشگر

• Climate Dynamics
• Atmospheric Physics
• Climate Modeling
• +۳ مورد دیگر

Henning Franke is a Researcher (Postdoc) at the Max Planck Institute for Meteorology's Department of Climate Physics, focusing on the Climate Surface Interaction group. His research leverages km-scale general circulation models to explore atmospheric circulation systems, from thunderstorms to global wind patterns. He investigates processes like gravity waves and their role in phenomena like the Quasi-Biennial Oscillation (QBO), while advancing model development to address climate change impacts. Education PhD in Earth System Sciences (2020–2024): Max Planck Institute for Meteorology M.Sc. Meteorology (2017–2020): University of Hamburg B.Sc. Meteorology (2014–2018): University of Hamburg Research Focus His work emphasizes understanding climate dynamics through high-resolution modeling, particularly the QBO's behavior under global warming and interactions between ocean mesoscale structures and tropical convection. He explores model dependencies and aerosol effects on atmospheric circulation. Publications Recent studies highlight advancements in QBO simulation accuracy, effects of global warming on gravity waves, and stratospheric aerosol modeling during volcanic eruptions. His work bridges theoretical insights with computational innovations, such as GPU-based climate modeling frameworks. Labs/Teams He contributes to the Climate Surface Interaction group and collaborates on projects like the ICON model development and RV SONNE ocean-atmosphere measurement campaigns.

• Computer-Supported Collaborative Learning (CSCL)
• Artificial Intelligence in Education
• Human-Computer Interaction
• +۳ مورد دیگر

Nikol Rummel is a Professor at Ruhr-Universität Bochum , specializing in Computer-Supported Collaborative Learning (CSCL) , Artificial Intelligence in Education , and Human-Computer Interaction . Her work bridges educational technology with hardware reverse engineering , focusing on AI-orchestrated learning environments and adaptive tools for collaboration. Her research spans AI-orchestration tools ( Pair-Up , Co-Designing Orchestration Support ), collaborative problem-solving ( Learning Alone or Together ), and hardware reverse engineering education ( Promoting Skill Acquisition ). She investigates how multimodal data and adaptive feedback enhance learning outcomes, teacher practices, and emotional regulation. Her publications highlight trends in generative AI for academic writing , collaboration scripts , and cognitive obfuscation in cybersecurity education. Collaborations with researchers like Vincent Aleven, Christof Paar, and Steffen Becker underscore her interdisciplinary approach. Nikol Rummel’s work emphasizes teacher dashboards ( What Information Should CSCL Dashboards Provide? ), dynamic student pairing ( Combining Dialog Acts and Skill Modeling ), and emotional engagement in learning ( How to Enjoy Writing Papers ).

• Knowledge Graphs
• Semantic Web
• Data Quality
• +۵ مورد دیگر

Elena Simperl is a Professor at King's College London, UK, with former affiliations at the University of Southampton and Karlsruhe Institute of Technology. Her work focuses on knowledge graphs, semantic web technologies, and AI-driven data management. She leads research in collaborative knowledge engineering, dataset search, and AI ethics, contributing to projects like the TheyBuyForYou platform for public procurement transparency. Her research interests span knowledge representation, crowdsourcing, and human-AI collaboration. Notable contributions include advancing methods for knowledge graph construction, improving data quality via crowdsourced and automated approaches, and exploring the societal impact of AI systems. She has co-edited major conferences such as ISWC and ESWC, and her work bridges technical innovation with practical applications in public policy and information systems. Key projects include developing frameworks for dataset usability, AI-ready data infrastructure, and systems for fact-checking visual content. Her collaborations span academia and industry, addressing challenges in data governance, misinformation detection, and ethical AI deployment.

• Database Systems
• Data Engineering
• Distributed Systems
• +۴ مورد دیگر

Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.

Katharina Helmingمشاهده پروفایل

استاد

• Sustainable Land Management
• Soil Conservation
• Agricultural Sustainability
• +۴ مورد دیگر

Prof. Dr. Katharina Helming is a Professor leading the Impact Assessment of Land Use Changes working group at the Leibniz Centre for Agricultural Landscape Research (ZALF), affiliated with Research Area 3 on Agricultural Landscape Systems. Her expertise spans interdisciplinary research on land use dynamics, sustainability assessment methodologies, and soil conservation. She holds a Diplom in Agricultural Engineering and a PhD in Agricultural Sciences. Her work focuses on evaluating soil functions, sustainable soil management, and socio-ecological interactions. Key contributions include developing frameworks for sustainability impact assessment and advancing FAIR data infrastructures for agroecosystems. Her research integrates climate change adaptation, digital agriculture, and participatory approaches. Projects include the BonaRes Repository for soil data standardization and protocol-based scenarios for future European agri-food systems (Eur-Agri-SSPs). She leads initiatives on soil health policy and digital transformation in agriculture. Her work emphasizes linking scientific insights with policy through tools like impact mapping frameworks and stakeholder engagement methodologies. Publications highlight innovations in soil data reuse, barriers to sustainable practices, and resilience in community agriculture. She contributes to international initiatives like MACSUR and FAIRagro, addressing global challenges in agri-food systems through interdisciplinary collaboration.

Heike Rielمشاهده پروفایل

استاد مهمان

• Nanoelectronics
• Semiconductor Technology
• Energy Harvesting
• +۴ مورد دیگر

Heike Riel is a Rudolf Diesel Industry Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS), affiliated with IBM Research-Zurich. She leads the Materials Integration & Nanoscale Devices (MIND) group, focusing on III-V semiconductors, molecular electronics, and energy-efficient nanoelectronics. Her academic career includes a PhD from the University of Bayreuth (2003) and an MBA from Henley Business School (2011). Riel’s research bridges materials science and device engineering, emphasizing nanoscale integration and energy efficiency. **Research Interests:** She pioneers novel materials and device architectures for next-generation electronics, including steep-slope transistors and nanowire-based systems. Her work addresses challenges in semiconductor heterostructures, single-molecule junctions, and thermal management at the nanoscale. **Awards & Recognition:** Riel is an IBM Fellow (highest technical honor) and Swiss Academy of Engineering Sciences member. Her innovations in nanowire devices and organic light-emitting diodes have earned global acclaim, including the MIT TR100 Young Innovator award (2003). **Key Contributions:** Her publications highlight breakthroughs in nanowire integration on silicon, tunable bandgap transitions, and molecular-scale electrical analysis. These advances underpin emerging technologies in energy-efficient computing and optoelectronics.

• Network Science
• Social Influence Modeling
• Control Systems
• +۵ مورد دیگر

Chiara Ravazzi is an accomplished researcher at Politecnico di Torino specializing in network science, social influence modeling, and control systems. Her academic career spans over 15 years with consistent high-impact publications in top-tier venues including IEEE journals and conferences. Dr. Ravazzi's research focuses on understanding complex network dynamics with applications spanning social networks, wireless communications, and pandemic modeling. Her work bridges theoretical network analysis with practical implementations, particularly examining how information and influence propagate through networks. She has developed novel methodologies for analyzing social influence, optimizing network resources, and predicting behavioral patterns in complex systems. Analysis of her recent publications (2023-2025) reveals three major research thrusts: (1) Social network dynamics and influence modeling, particularly examining innovation diffusion and popularity mechanisms; (2) Optimization of communication networks with applications to 5G/6G systems; and (3) Network-based approaches to public health challenges like pandemic intervention planning. Her interdisciplinary work demonstrates strong methodological rigor while addressing contemporary real-world problems. Dr. Ravazzi maintains extensive collaborations with leading researchers including Fabrizio Dabbene, Enrico Magli, Paolo Frasca, and Valentina Breschi. Her publication record shows consistent growth in both quantity and impact, with numerous first-author and senior-author contributions across multiple domains. She serves on program committees for major conferences in control systems and network science.

• Fuzzy Set Theory
• Fractional Calculus
• Mathematical Inequalities
• +۶ مورد دیگر

Muhammad Bilal Khan is an Assistant Professor at the Department of Electronics and Power Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Pakistan. His research spans mathematical theory and practical applications in wireless communication sensing technology. His primary research interests include Fuzzy Set Theory , Fractional Calculus , Mathematical Inequalities , and RF Sensing Applications in healthcare. His theoretical work focuses on developing new mathematical frameworks for fuzzy-number valued functions, particularly exploring pre-invexity, harmonic convexity, and related integral inequalities. On the applied side, he has made significant contributions to non-contact health monitoring systems using software-defined radio technology for detecting conditions like text neck syndrome, spinal curvature disorders, and breathing abnormalities. Analysis of his 15 most recent publications reveals a dual research trajectory: approximately 60% focus on theoretical mathematical contributions in fuzzy analysis and integral inequalities, while 40% address practical applications of wireless sensing in healthcare. His mathematical work frequently appears in journals like Axioms and Symmetry , while his applied research is published in venues including IEEE Access and Sensors . His notable scientific contributions include developing novel frameworks for (q 1 , q 2 )-Linear Diophantine Fuzzy Sets and advancing the theory of Diamond Intuitionistic Fuzzy Sets. His healthcare applications research has pioneered non-invasive monitoring techniques using RF sensing for elderly fall detection, text neck syndrome diagnosis, and breathing pattern analysis. Dr. Khan maintains active collaborations with researchers across multiple institutions, particularly working with Mohamed S. Soliman (13 joint publications), Muhammad Aslam Noor (12 publications), and Savin Treanta (8 publications) on mathematical theory, while collaborating with Najah AbuAli, Mubashir Rehman, and Xiaodong Yang on healthcare sensing applications.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Sio Kei Im is an active researcher with a focus on computer science, machine learning, and human-computer interaction. His recent work spans multiple domains including image processing, quantum computing, and virtual reality. Publications address advanced data augmentation (LogicMix), multi-modal quantum watermarking (MMQW), and efficient neural decoding algorithms (TRHyper). Research interests include time series optimization, dialogue summarization, and haptics in VR environments. Collaborations with experts in linguistics, electrical engineering, and software development indicate interdisciplinary expertise. Key contributions involve adaptive algorithms for AI model protection, speaker recognition systems, and real-time 3D rendering techniques.

• Machine Learning
• Image Retrieval
• Data Clustering
• +۳ مورد دیگر

Mohammed Lamine Kherfi is a researcher affiliated with Université de Ouargla, Algeria. His work focuses on machine learning, image retrieval, and data clustering with applications in computer vision and optimization. He has collaborated extensively with researchers like Oussama Aiadi, Mebarka Allaoui, and Djemel Ziou. His research bridges theoretical advancements in machine learning with practical applications in areas such as fruit classification, semantic image retrieval, and deep learning models. Key research areas include optimization algorithms (e.g., PSO integration with t-SNE), multi-view learning, and Bayesian methods for image representation. He has contributed to improving clustering techniques, feature extraction, and the development of lightweight neural network architectures. His work often emphasizes efficient and energy-aware solutions for real-world problems. Over 30 publications span prestigious venues like Expert Systems with Applications, IEEE Access, and Multimed Tools Appl. His collaborative network includes institutions in Algeria and international partners, reflecting a global impact in computational intelligence and computer vision.

• Data Governance
• Semantic Modeling
• Knowledge Graphs
• +۵ مورد دیگر

Zaiwen Feng is a researcher actively contributing to data governance, semantic modeling, and causal inference. His work focuses on knowledge graphs, graph-based methods, and service-oriented architectures through collaborations with institutions like the University of Queensland and universities in China. Research Focus : Graph Differential Dependencies, Entity Resolution, Causal Effect Estimation, and Ontology Alignment Methodologies : Machine Learning, Variational Autoencoders, Prompt Engineering, and Semantic Retrieval Application Areas : Biomedical Data, Property Graph Recommendation, and Process Model Repositories Key trends in his publications include automated semantic modeling , neural approaches for entity resolution , and causal inference with graph structures . He frequently collaborates with researchers like Keqing He, Wolfgang Mayer, and Selasi Kwashie across conferences such as HPCC, BIBM, and WISE.

• Virtual Reality
• Haptic Feedback
• Sonic Interaction
• +۸ مورد دیگر

Niels Christian Nilsson is a faculty member at Aalborg University , Denmark, with a research focus on Virtual Reality , Haptic Feedback , and Human-Computer Interaction . His work explores techniques for enhancing immersion and realism in VR through multimodal feedback, including tactile, auditory, and visual cues. Key research themes include redirected walking , body ownership illusions , and interactive sound synthesis , often in collaboration with Stefania Serafin and Rolf Nordahl. Nilsson's recent publications (2023-2024) address VR sickness in locomotion systems, tactile auditory augmentation for hearing-impaired users, and applications of VR in rehabilitation. He investigates novel input methods like blink-based interaction and geometric environments for mathematics education. His contributions span both practical implementations (e.g., ElboVR for post-surgery recovery) and theoretical frameworks for sensory feedback in virtual environments. Nilsson's work has been presented at premier venues such as CHI , VR , and IEEE Transactions on Visualization and Computer Graphics . While no formal awards or students are listed in this dataset, his research emphasizes interdisciplinary collaboration and user-centered design in immersive technologies, with implications for healthcare, gaming, and education.