جست‌وجوی استاد و پژوهشگر

• Network Management
• Network Protocols
• Internet of Things
• +۵ مورد دیگر

Jürgen Schönwälder is a Professor at Jacobs University Bremen, Germany, with affiliations at the University of Osnabrück and TU Braunschweig's Department of Computer Science. His research focuses on network management, protocol design, and internet infrastructure. Key research areas include network management protocols (NETCONF, RESTCONF, YANG), IPv6 performance analysis, cybersecurity, and network configuration. He has contributed extensively to standardization efforts through RFCs and collaborations with institutions like the IETF and Dagstuhl Seminars. Recent publications highlight trends in RESTCONF implementation for constrained devices, active malware analysis using Bayesian models, and metamorphic testing for cryptographic protocols. His work intersects network management, internet infrastructure, and security evaluation. Co-authors like Vaibhav Bajpai, Anuj Sehgal, and Abhilash Hota appear frequently in his research, indicating long-term collaborations. He has participated in editorial roles for journals like IEEE Communications Magazine.

• Cloud Computing Economics
• Software Service Platforms
• IT Service Economics
• +۶ مورد دیگر

Jörn Altmann is a Professor in the Department of Computer Science and Engineering at Seoul National University's College of Engineering. With a publication history spanning over three decades from 1994 to 2025, he has established himself as a leading researcher in the economics of computing services, particularly focusing on cloud computing economics, software service platforms, and IT service ecosystems. He has served as editor for the GECON (Economics of Grids, Clouds, Systems, and Services) conference proceedings for multiple years, demonstrating his leadership in the field. Altmann's research interests center on the economic aspects of computing infrastructure, with particular expertise in cloud federation models, resource allocation mechanisms, and the business dynamics of software service ecosystems. His work bridges technical computing concepts with economic theory, examining how market mechanisms can be applied to computing resource allocation. Recent research has expanded into cybersecurity economics, AI adoption frameworks, and knowledge management in distributed systems environments. His publications consistently explore the intersection of technology adoption patterns and economic incentives within computing ecosystems. Analysis of his recent publications reveals a strong focus on cloud federation architectures, trust mechanisms in cybersecurity information sharing, and the economic modeling of value creation in distributed computing environments. His work demonstrates a methodological diversity, employing game theory, agent-based modeling, systematic literature reviews, and empirical analysis of industry dynamics, particularly in the Korean technology sector. Altmann has collaborated extensively with researchers worldwide, with notable long-term collaborations with José Ángel Bañares, Omer F. Rana, Kurt Vanmechelen, and Korean researchers including Kibae Kim. His work has appeared in prestigious venues including Future Generation Computer Systems, IEEE Transactions on Engineering Management, and the GECON conference series which he has helped shape through editorial leadership. His research has practical implications for cloud service providers, enterprise IT strategy, and policy development in digital infrastructure. The consistent publication record and editorial leadership suggest active supervision of graduate students and research teams, though specific students are not documented in the provided publication records.

• Nanophotonics
• Quantum Technologies
• Computational Optics
• +۵ مورد دیگر

Dr. Sven Burger is a leading Researcher at the Zuse Institute Berlin (ZIB) within the Modeling and Simulation of Complex Processes department. His work focuses on Nanophotonics , Quantum Technologies , and Optical Resonance Computation , particularly in photonic crystals, plasmonic systems, and quantum light sources. Key projects: NanoLab GRIPS 2024 , MATH+ TES QT , MATH+ PaA-1 (perovskite solar cells), Colour Impression of Solar Cells Collaborations: MATH+ , BIFOLD , Research Campus MODAL His research spans Bayesian optimization for quantum systems, quasinormal mode expansions , chiral plasmonics , and terawatt-scale photovoltaics . Recent work emphasizes RPExpand software for resonance analysis and AAA algorithm applications in photonic design. He contributes to quantum key distribution via plug&play single-photon sources, hot carrier dynamics in plasmonic nanocrystals, and high-efficiency light extraction for deep-UV LEDs. His computational methods address non-Hermitian systems , exceptional points , and self-interference nanoparticle tracking .

Massimiliano Pontilمشاهده پروفایل

استاد

• Machine Learning
• Dynamical Systems
• Optimal Transport
• +۵ مورد دیگر

Massimiliano Pontil is a Professor at University College London's Department of Computer Science within the Faculty of Mathematical & Physical Sciences, with additional affiliation at the Italian Institute of Technology in Genoa. He leads cutting-edge research at the intersection of machine learning and dynamical systems theory. His research focuses on developing theoretical frameworks for learning transfer operators of stochastic dynamical systems using kernel methods. Pontil's work provides rigorous spectral learning bounds and addresses representation learning challenges in modeling time-evolving phenomena. His research spans both theoretical foundations and practical applications in data-driven science and engineering. Pontil's recent publications reveal a strong emphasis on Koopman operator theory, with particular attention to spectral analysis, error bounds, and efficient algorithms for large-scale dynamical systems. His work connects kernel methods with dynamical systems theory to create mathematically grounded approaches for forecasting and understanding temporal phenomena. His scientific contributions include multiple publications in top-tier venues including NeurIPS and ICLR, with recent work on invariant representations, long-term forecasting, and randomized algorithms for operator regression. Pontil collaborates extensively with researchers including Vladimir Kostic, Karim Lounici, and Pietro Novelli, leading a productive research group in this specialized area of machine learning theory.

• Emerging Technologies
• Neuromorphic Hardware
• RISC-V for AI
• +۳ مورد دیگر

Albi Mema is a researcher affiliated with the Chair of AI Processor Design (AI-Pro) at Technische Universität München (TUM). His work focuses on emerging technologies for AI applications, including neuromorphic hardware, reliability engineering, and quantum computing. University: Technische Universität München Department: Chair of AI Processor Design (AI-Pro) Key research areas include: Emerging Technologies for AI Neuromorphic Hardware Reliability in Semiconductor Devices Quantum Computing RISC-V Architecture Machine Learning Computer-Aided Design His recent publications address fault-tolerant hyperdimensional computing, analog computing for AI, FeFET-based neuromorphic systems, and compact majority gate design using FDSOI technology. No scientific awards are mentioned in the provided text.

Martim Brandãoمشاهده پروفایل

استادیار

• Robotics
• Artificial Intelligence
• Responsible AI
• +۱۲ مورد دیگر

Martim Brandão is a Lecturer (Assistant Professor) in Robotics and Autonomous Systems at King’s College London, where he leads the Responsible Robotics and AI (RRAI) Lab and serves as Co-Director of the UKRI Centre for Doctoral Training in Safe and Trusted AI. His research focuses on ethical, explainable, and safe AI and robotics, with applications in human-robot interaction, motion planning, fairness, and societal impact. His research interests include: Explainable AI and Motion Planning Fairness and Bias in AI Systems Human-Robot Interaction and Social Robotics Adversarial Robustness in Robotics Value Alignment and Ethical AI Inclusive and Participatory Robotics Design His recent publications (2023–2025) reflect a strong trend toward socially responsible robotics, focusing on fairness in navigation, explainability of planning failures, worker-centered agricultural robotics, environmental justice in drone delivery, and the dangers of bias in drowsiness detection and LLM-driven robots. His work emphasizes user understanding, societal impact, and ethical safeguards in autonomous systems. He has advised and collaborated with numerous students and researchers across diverse topics in robotics and AI. He is actively involved in shaping responsible robotics through: Leadership in the RRAI Lab Co-directing a national CDT in Safe and Trusted AI Developing fairness-aware algorithms Advocating for inclusive and ethical design practices His lab and research group focus on: Responsible Robotics and AI Explainability in Multi-Agent Planning Fairness in Coverage and Navigation Human-Centered Evaluation of AI Systems

Gyunam Parkمشاهده پروفایل

پژوهشگر

• Process Mining
• Action-Oriented Process Mining
• Object-Centric Process Analysis
• +۱۲ مورد دیگر

Gyunam Park is a Research Group Lead and Process and Data Scientist at Fraunhofer FIT and a Scientific Assistant at the Chair of Process and Data Science at RWTH Aachen University, a leading institution in computer science and engineering. He is actively involved in both research and teaching, contributing to the advancement of process mining, data science, and artificial intelligence. His work bridges academic research and industrial applications, particularly in SAP ERP systems and digital twins of organizations. Research Interests: Gyunam Park’s research focuses on Action-Oriented Process Mining (AOPM) , Object-Centric Process Analysis , and Responsible Machine Learning . He aims to transform process mining insights into actionable management decisions, ensuring transparency, fairness, and compliance. His work enables organizations to monitor operational constraints, generate corrective actions, and assess their impact using data-driven methods. Publication Trends: His recent publications emphasize object-centric approaches to process mining, predictive monitoring, constraint checking, and integration with AI planning. There is a strong trend toward preserving structural information in event logs, improving machine learning performance, and applying these techniques to real-world systems like SAP ERP and after-sales service processes. Scientific Awards: No awards are explicitly mentioned in the provided text. Advising and Grants: While no formal students are listed, Gyunam Park leads research projects and collaborates with industry partners such as Samsung Electronics and SAP. His projects involve root cause analysis, resource optimization, and educational data mining. He has developed open-source tools like ProAct and OCPA , indicating active grant or institutional support for software development and research dissemination. Labs and Teams: He is a core member of the Process and Data Science (PADS) group led by Prof. Wil van der Aalst at RWTH Aachen University and leads a research group at Fraunhofer FIT. These teams focus on cutting-edge research in process mining, data science, and AI, with strong industry collaborations and regular contributions to top conferences and journals.

Mario Kupnikمشاهده پروفایل

استاد

• Micromachined Sensors
• Ultrasonic Technology
• Non-Destructive Testing
• +۴ مورد دیگر

Prof. Mario Kupnik is a Full Professor at the Technische Universität Darmstadt , leading the Measurement and Sensor Technology Group within the Department of Electrical Engineering and Information Technology. His academic career includes roles at Stanford University (2005–2011) and Brandenburgische Technische Universität Cottbus (2011–2014). He holds a doctorate from Montanuniversität Leoben (2000–2004) and a master's in Telematics from Graz University of Technology. His research focuses on micromachined sensors and actuators , ultrasonic and electroacoustic systems , and non-destructive testing . He pioneers innovations in wearable sensors, biomedical applications, and additive manufacturing for sensor integration. Notable contributions include air-coupled ultrasonic transducers, 3D-printed ferroelectret sensors, and robotics for STEM education. Recent work emphasizes biodegradable sensors , acousto-optic modulation , and multi-parameter medical measurement systems . His projects span from fundamental material science to applied engineering solutions, often leveraging open-source hardware. Kupnik’s labs integrate interdisciplinary approaches, combining electrical engineering, materials science, and biomedical engineering.

Bernard Haasdonkمشاهده پروفایل

استاد

• Model Reduction
• Parametrized Partial Differential Equations (PDEs)
• Kernel Methods
• +۷ مورد دیگر

Bernard Haasdonk is a Professor at the University of Stuttgart, affiliated with the Institute of Applied Analysis and Numerical Simulation (IANS), part of the Faculty of Mathematics and Computer Science. His research focuses on model reduction techniques for parametrized partial differential equations (PDEs), kernel-based methods, numerical analysis, and machine learning applications in scientific computing. He leads a research group in numerical mathematics and has contributed to software tools like RBMatlab and KerMor. Affiliations: Institute of Applied Analysis and Numerical Simulation, University of Stuttgart Roles: Academic Researcher, Software Developer, Grant Principal Investigator His work bridges numerical simulation, machine learning, and reduced basis methods, addressing challenges in optimal control, fluid dynamics, and biomechanics. Haasdonk has held multiple funded projects, including those on kernel methods for model reduction and certified RB-ML-ROM surrogate models. Research Interests: Model reduction for PDEs, kernel methods, greedy algorithms, numerical analysis, optimal control, and applications in fluid dynamics and porous media. He emphasizes structure-preserving methods for Hamiltonian systems and data-driven approaches for surrogate modeling. Publications: Over 200 articles in journals like SIAM, BIT Numerical Mathematics, and Physica D, focusing on convergence analysis, kernel-based approximation, and reduced-order modeling. Recent trends include adaptive greedy algorithms, symplectic model reduction, and energy-conserving surrogates. Awards: IEEE PerCom 2017 Best Paper Award, Teaching Excellence Awards (2012-2017), and early-career research grants. Grants: DFG-funded projects on model reduction, SimTech Cluster contributions, and collaborations on fuel cells and biomechanics. Teams: Leads the Numerical Mathematics Research Group at IANS, collaborating with interdisciplinary teams on projects like MORCOS (Model Order Reduction of Coupled Systems) and KerMor (Kernel Methods for Model Reduction).

Shlomo Dubnovمشاهده پروفایل

استاد

• Music Technology
• Audio Signal Processing
• Machine Learning for Audio
• +۳ مورد دیگر

Shlomo Dubnov is a Professor in the Department of Music at the University of California, San Diego. With an h-index of 36 and over 4,803 citations, his research spans the intersection of music, audio processing, and machine learning, making significant contributions to music information retrieval, sound synthesis, and speech recognition technologies. Dr. Dubnov's primary research interests focus on Music Technology and Audio Signal Processing , with particular expertise in: Machine learning applications for music and audio analysis Sound texture synthesis and modeling Music improvisation systems and computational creativity Audio adversarial examples and security Pitch tracking in noisy environments Multimodal learning for audio-text alignment His recent work demonstrates a strong trend toward applying transformer architectures and contrastive learning techniques to audio processing tasks, with publications in top conferences like IEEE International Conference on Acoustics, Speech, and Language Processing. Dr. Dubnov's research bridges theoretical music concepts with practical audio engineering applications, creating innovative solutions for music generation, analysis, and security. Dr. Dubnov has established productive collaborations with researchers across disciplines, particularly with: G. Assayag (102 Publications • 2,475 Citations) - music technology and improvisation systems Taylor Berg-Kirkpatrick (31 Publications • 1,459 Citations) - machine learning applications K. Chen (18 Publications • 1,150 Citations) - audio transformer models Cheng-i Wang (21 Publications • 305 Citations) - music information retrieval His work is supported by grants focused on advancing the state-of-the-art in music technology, audio processing, and machine learning applications for creative domains. Dr. Dubnov leads research in the Music Technology laboratory at UC San Diego, where his team develops innovative systems for music analysis, synthesis, and human-computer interaction in musical contexts. The lab focuses on creating intelligent systems that can understand, generate, and interact with musical content in real-time applications.

• Information Retrieval
• Digital Libraries
• Informetrics
• +۵ مورد دیگر

Philipp Mayr-Schlegel is a Professor at the University of Göttingen’s Institute of Computer Science, leading the team 'Information & Data Retrieval' at GESIS - Leibniz-Institute for the Social Sciences. His research focuses on interactive information retrieval systems, scholarly recommendation mechanisms, and the integration of bibliometric methods with digital library technologies. University of Göttingen - Institute of Computer Science GESIS - Leibniz-Institute for the Social Sciences His work spans information retrieval , digital libraries , and applied informetrics , with particular emphasis on: Interactive search systems Non-textual ranking algorithms Scholarly document processing Knowledge representation Semantic search technologies User behavior analysis Recent research outputs (2025) include studies on LLMs for scholarly search, bibliometric reproducibility tools, scientific uncertainty annotation, and preprint adoption disparities. He has published extensively in Scientometrics , International Journal on Digital Libraries , and top conference proceedings like ACL and ECIR . As organizer of the International Workshop on Bibliometric-enhanced Information Retrieval (BIR) and Scholarly Document Processing (SDP) series, he has shaped academic discourse through editorial roles at ISSI , JCDL , and SocInfo conferences. His projects have attracted significant national and European funding.

Yana Boevaمشاهده پروفایل

پژوهشگر ارشد

• Science and Technology Studies
• Human-Computer Interaction
• Digital Fabrication
• +۵ مورد دیگر

Dr. Yana Boeva is a Junior Research Group Leader at the University of Stuttgart , affiliated with both the Institute for Social Sciences and the Cluster of Excellence IntCDC . Her work bridges Science and Technology Studies (STS) with Computational Design , focusing on critical examinations of digital infrastructure, human-computer interaction, and sustainability in architectural contexts. Research Interests: Algorithmic entanglements in design and construction Participatory technology production Digital fabrication and material practice Epistemic cultures of computational design Socio-political implications of Building Information Modeling (BIM) Her recent publications analyze timber construction innovation trajectories , platformization in urban environments , and ghost labor in automation . She co-edited the volume Algorithmic Regimes (2024), which investigates algorithmic knowledge production across societal domains. Scientific Awards: Recipient of the DigitalFUTURES Young Award for critical computational design research Dr. Boeva teaches courses on Algorithmic Sociology and Digital Sustainability at the MA and BA levels. Her work has been supported by projects under the Excellence Cluster IntCDC and BBSR Innovationsprogramm Zukunft Bau .

• Information Retrieval
• Large Language Models
• Evaluation Methodologies
• +۳ مورد دیگر

Charles L. A. Clarke is a Professor at the University of Waterloo, Canada, with a focus on Information Retrieval and Large Language Model evaluation . He actively contributes to research in search algorithms, human-computer interaction, and computational linguistics. Recent Research Trends : His work examines LLM limitations in relevance assessment, adversarial robustness in legal domains, and hybrid human-AI evaluation frameworks. Workshop Leadership : Co-organizer of the Search Futures Workshop (ECIR 2024/2025) and LLM4Eval@SIGIR. Collaborations : Works with researchers from NII, Microsoft, and ACM SIGIR on testbed development and evaluation methodologies. Key Article Trends : His 2024-2025 publications analyze LLM vulnerabilities, develop evidence retrieval systems, and create metrics for human-AI alignment in generative applications. Subfields include adversarial attacks, prompt sensitivity, and semantic graph frameworks. Scientific Contributions : Focuses on bridging algorithmic performance with human judgment validity, emphasizing ethical AI deployment and robust information access systems.

• Biomedical Engineering
• Machine Learning
• Wearable Technology
• +۳ مورد دیگر

Muhammad Awais Bin Altaf is a researcher specializing in biomedical engineering, machine learning, and wearable technology. His work focuses on low-power embedded systems for neurological and cardiovascular monitoring, including EEG processors for seizure detection and PPG-based blood pressure classification. He has collaborated extensively with co-authors like Wala Saadeh and Jerald Yoo on IEEE journals and conferences. His research interests include Biomedical signal processing Wearable health devices Machine learning for medical diagnostics Energy-efficient hardware design Neurological disorder detection Embedded systems for clinical applications Recent publications highlight trends in shallow neural networks, autoencoders, and hardware acceleration for real-time health monitoring. Key subfields span seizure prediction, stress detection, and impedance-adaptive sensors. Collaborations include institutions in Germany, Finland, and Pakistan. His work often integrates open-source toolflows and industry-standard chip design techniques, emphasizing practical implementations for wearable environments. Contributions to HDR imaging algorithms and biomedical SoCs demonstrate interdisciplinary expertise in signal processing and healthcare technology.

• Database Systems
• Data Mining
• Social Network Analysis
• +۳ مورد دیگر

Eric Leclercq is a researcher at the University of Burgundy, affiliated with the LE2I Lab in Dijon, France. His work spans database systems, social network analysis, and biomedical data integration. He has contributed extensively to polystore systems, tensor decompositions, and category theory applications in data modeling. Fields of Interest : Database Systems, Data Mining, Social Network Analysis, Big Data Analytics, Semantic Web Leclercq's recent research focuses on formal frameworks for data lakes using category theory, multi-level tensor decomposition for social network stratification, and schema migration in multi-model systems. He has published in venues like CAiSE, IDEAS, and RCIS. His collaborations include Annabelle Gillet, Marinette Savonnet, and Nadine Cullot. Notable works include Lambda+ architecture for data processing, polarization analysis in social networks, and tools for tweet collection and biomedical data integration.