جست‌وجوی استاد و پژوهشگر

• IoT and Sensor Networks
• Quantum Computing
• RFID Systems
• +۴ مورد دیگر

Bartolomeo Montrucchio is a full Professor at the Polytechnic University of Turin's Department of Electrical, Computer and Biomedical Engineering. His research focuses on IoT systems, quantum computing applications, RFID technologies, and sensor networks. He leads interdisciplinary projects involving environmental monitoring, cybersecurity, and embedded systems design. Key research areas include: Development of low-cost air quality monitoring systems leveraging IoT and sensor fusion Quantum algorithm design for financial risk analysis and game theory applications RFID security protocols and anti-collision mechanisms for dense networks Energy-efficient image processing techniques for mobile displays Recent work highlights quantum computing's potential in vulnerability analysis and financial modeling, while maintaining strong contributions to sensor network reliability and environmental applications. His publications span prestigious journals like IEEE Internet of Things Journal and IEEE Transactions on Dependable and Secure Computing. Collaborations with industry partners and academic institutions emphasize practical implementation of theoretical advancements, particularly in smart city technologies and healthcare monitoring systems.

• Machine Learning
• Time Series Forecasting
• Credit Card Fraud Detection
• +۳ مورد دیگر

Gianluca Bontempi is a Professor in the Department of Computer Science at the Université Libre de Bruxelles (ULB), Faculty of Sciences. His research spans machine learning, time series forecasting, and big data analytics, with significant applications in credit card fraud detection, biomedical engineering, and causal inference. He leads projects on adaptive learning systems, financial cybersecurity, and high-dimensional data modeling. Research Focus: Bontempi's work integrates theoretical machine learning with real-world challenges. Key areas include: Developing dynamic factor models for multivariate time series forecasting Designing incremental learning frameworks for fraud detection in evolving data streams Advancing causal inference methods for personalized decision-making Creating interpretable AI systems for biomedical and financial applications Publication Trends: His recent articles (2019–2026) show a strong emphasis on combating concept drift in financial fraud systems, improving fairness in feature selection, and enhancing cross-domain adaptation techniques. Methodological innovations in multi-task learning and adversarial modeling are recurrent themes. Awards & Recognition: No awards explicitly mentioned in the source text. Research Infrastructure: Bontempi collaborates with interdisciplinary teams across Europe on large-scale projects involving biomedical data (TCGAbiolinks), urban mobility (traffic forecasting), and AI ethics (CLAIRE COVID-19 initiative).

Lin Geng Fooمشاهده پروفایل

پژوهشگر

• Video Analysis
• Activity Understanding
• Diffusion Models
• +۲ مورد دیگر

Lin Geng Foo is a postdoctoral researcher at the Max-Planck Institute for Informatics under the Visual Computing and Artificial Intelligence (VCAI) Department, supervised by Prof. Christian Theobalt. He earned his PhD and Bachelor's from the Singapore University of Technology and Design (SUTD) with the President's Graduate Fellowship and top honors. His research focuses on video analysis , activity understanding , diffusion models , and dynamic neural networks , particularly for 3D human generation/editing . His work includes groundbreaking papers on adversarial attacks, human mesh recovery, and pose estimation. Key trends in his recent publications include applications of diffusion models dynamic networks 3D human modeling video analysis action recognition neural rendering for tasks like mesh recovery, controllable video editing, and sign language translation. Scientific awards include PREMIA Best Student Paper 2023 Commendation Prize Keppel Awards of Excellence (2018, 2019) EY Business Analytics Award He serves as a reviewer for major conferences (CVPR, ICCV, NeurIPS, ICML) and has presented talks at ICME 2024 Metaverse Workshop and SDSC Industry & Demo Day 2024.

• Computer Science
• Artificial Intelligence
• Image Processing
• +۶ مورد دیگر

Wei-Ting Chen is a prominent researcher in computer science and artificial intelligence, focusing on image processing, neural networks, and computer vision. With active contributions to IEEE and CVPR venues, their work spans practical applications in haze removal, snow removal, and medical imaging. Key research areas: Image restoration, Diffusion models, Neural radiance fields, Quality assessment Recent publications (2024-2025): Unified restoration models, Video quality analysis, Crowd counting in adverse weather Notable collaborative works include: CVPR 2024: RobustSAM for degraded image segmentation IEEE Access 2024: Low-cost pipelined architecture design Remote Sensing 2021: National PM2.5 estimation using MAIAC data Technical expertise evident in implementations for vehicle re-identification, depth estimation, and electrical motor dynamics modeling.

Tronje Kemenaمشاهده پروفایل

پژوهشگر

• Ocean Modeling
• Climate Dynamics
• Earth System Modeling
• +۵ مورد دیگر

Tronje Kemena is a researcher at GEOMAR Helmholtz Centre for Ocean Research Kiel, focusing on ocean modeling, data analytics, and machine learning applications in climate science. His work primarily involves support and operation of the FOCI model for the BM Group, with emphasis on understanding Earth system dynamics and climate change impacts. Dr. Kemena completed his PhD in 2017 and Master's degree in 2014, both at Christian-Albrechts-Universität Kiel. His doctoral research focused on 'Assessment of Feedbacks in the Earth System under Anthropogenic Forcing: Two Case Studies,' while his Master's thesis examined 'Impact of the carbon cycle on the development of a Snowball Earth.' His research interests span ocean modeling, climate dynamics, Earth system science, and data-intensive approaches to understanding biogeochemical cycles. Kemena specializes in analyzing carbon cycle feedbacks, ocean deoxygenation processes, and the application of machine learning to climate and oceanographic data. His work often bridges theoretical modeling with practical data analysis techniques. Kemena's publication record shows a strong focus on Earth system modeling, with particular expertise in ocean biogeochemistry and climate dynamics. His recent work has increasingly incorporated data analytics and machine learning approaches, reflecting the growing importance of these techniques in climate science. He has made significant contributions to understanding carbon cycle feedbacks, ocean deoxygenation, and the development of ocean alkalinity enhancement techniques as potential climate intervention strategies. Dr. Kemena is actively involved in several research projects related to ocean-based climate solutions, including the OceanNETs project which focuses on developing and assessing ocean-based negative emission technologies. His work combines theoretical modeling with practical applications to address pressing climate challenges.

Michael Zavrelمشاهده پروفایل

استاد

• Bioprocess Engineering
• Sustainable Biotechnology
• Fermentation Processes
• +۳ مورد دیگر

Prof. Dr. Michael Zavrel is a W3 Professor of Bioprocess Engineering at the Technical University of Munich’s TUM Campus Straubing for Biotechnology and Sustainability. He leads the Professorship Bioprocess Engineering, focusing on developing sustainable bioprocesses using non-food biogenic raw materials such as agricultural residues and waste streams. His research spans fermentation, downstream processing, and process design, emphasizing bio-based and biodegradable products. Education and Career: Zavrel earned his diploma and doctorate in Chemical Engineering from TUM and RWTH Aachen University, respectively. Before his academic role, he spent 14 years in industrial R&D at Süd-Chemie and Clariant, holding roles like Head of Development & Biomanufacturing and Site Manager. He joined TUM in 2022. Research Interests: His work addresses sustainable bioprocess development, including enzyme immobilization, soft sensor technology for bioprocess monitoring, and optimizing biorefinery systems. Key areas include fermentation optimization, bio-based material production, and process phase-dependent controls. Awards: Honors include the Borchers Plaque (RWTH Aachen, 2010), Jürgen Manchot Prize (TUM, 2006), and participation in the 2006 Lindau Nobel Laureate Meeting. In 2024, he won the Study Awards. Lab and Collaborations: His team works on industrial bioprocess challenges, collaborating with companies like Clariant. Research themes include aroma preservation in beverages, biomass hydrolysis, and PAT (Process Analytical Technology) for sustainable enzyme production.

Nikolaos Koutsoulerisمشاهده پروفایل

استاد

• Psychiatry
• Machine Learning
• Predictive Modelling
• +۸ مورد دیگر

Prof. Dr. Nikolaos Koutsouleris serves as Professor and Chair in Precision Psychiatry at Ludwig Maximilian University of Munich (LMU), heading the Section of Precision Psychiatry within the Department of Psychiatry and Psychotherapy in the Faculty of Medicine. His work focuses on developing clinically deployable machine learning tools for psychiatric diagnostics and outcome prediction across European research networks. His research integrates Psychiatry , Machine Learning , and Multimodal Data Fusion to address psychotic and affective disorders through neuroimaging, neurocognition, and omics approaches. Key interests include early recognition systems for youth mental health crises and computational frameworks for individualized risk stratification in clinical high-risk populations. The team's translational focus bridges algorithmic development with real-world clinical implementation. Analysis of recent publications reveals a consistent trajectory toward multi-modal prediction frameworks across psychosis and depression spectra. His work emphasizes multi-site validation of machine learning models using clinically heterogeneous cohorts, with increasing sophistication in functional outcome prediction and treatment response forecasting . The research demonstrates progressive integration of biological, cognitive, and clinical data streams for precision psychiatry applications. Scientific awards were not explicitly mentioned in the provided text. Koutsouleris currently advises GSN student Clara Weyer while leading major collaborative initiatives including the EU-funded PRONIA project (FP7), which established a pan-European cohort of 1,974 clinically characterized individuals. His grant portfolio demonstrates sustained funding for translational machine learning applications in early psychosis management, with emphasis on tool deployment through platforms like proniapredictors.eu. He directs the Precision Psychiatry Section (founded 2013 as Neurodiagnostic Applications Section), a multidisciplinary team spanning medicine, psychology, and computer science. The group maintains active development of open-source software tools on GitHub (neurominer-git) and has established clinical validation pipelines for machine learning predictors across multiple European healthcare systems.

Eirini Ntoutsiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Eirini Ntoutsi is an Associate Professor at the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover and a member of the L3S Research Center. Her academic journey includes a post-doctoral position at LMU Munich with an Alexander von Humboldt Foundation fellowship, and she earned her PhD from the University of Piraeus, Athens under the supervision of Y. Theodoridis. She holds a diploma and M.Sc. in Computer Engineering & Informatics from the University of Patras, Greece. Her research lies at the intersection of Artificial Intelligence and Machine Learning, focusing on two main pillars: learning over complex data and data streams (covering adaptive learning, change detection, and model stability), and responsible Artificial Intelligence (covering fairness-aware learning, data quality, and proper evaluation of AI/ML methods). Her work addresses critical societal challenges related to bias in algorithmic decision-making systems. Dr. Ntoutsi's recent publications demonstrate a strong focus on drift-aware learning for imbalanced data streams and fairness in AI systems. Her research shows a clear progression from foundational work in pattern management during her PhD to cutting-edge applications addressing real-world challenges in social streams, sensor data, and recommendation systems. Alexander von Humboldt fellowship for postdocs Best-student paper award at ICBK 2018 1st place in the 2006 innovation competition in Greece Dr. Ntoutsi actively mentors PhD and master's students while leading major research initiatives including NoBIAS (EU-funded, as Network coordinator), BIAS (Volkswagen Stiftung-funded), and OSCAR (DFG-funded). Her interdisciplinary approach combines technical AI solutions with philosophical and legal considerations to develop more equitable algorithmic systems.

• Data Stream Processing
• In-Network Computing
• Industrial Cyber-Physical Systems
• +۷ مورد دیگر

Liam Tirpitz is a researcher and PhD candidate at the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University. He is affiliated with the Chair of Computer Science i5 (Information Systems and Databases) and the Data Stream Management and Analysis Group, where he has been working since February 2022. Education : Master's degree in Computer Science from RWTH Aachen University (2021). His research focuses on data stream processing, in-network computing, and FAIR data ecosystems, with applications in industrial and cyber-physical systems. He explores methods for efficient data aggregation, stability detection, and cross-organizational data sharing, often leveraging technologies like WebAssembly and knowledge graphs. His work integrates distributed systems principles with database technologies to enable scalable and secure data processing in industrial environments. The recent publications of Liam Tirpitz reflect a strong focus on industrial data management, with trends toward in-network computing, edge-based stream processing, and FAIR principles. His work spans both theoretical architectures (e.g., GALOIS) and applied implementations in domains like manufacturing and health data, demonstrating interdisciplinary collaboration and real-world impact. Liam Tirpitz is actively involved in teaching and academic supervision. He has taught courses such as Data Ecosystems Lab and Implementation of Databases, and is currently supervising multiple thesis projects on topics including WebAssembly-based query functions, anomaly detection, and hybrid stream processing. He participates in research projects like StreamFröst and the Cluster of Excellence Internet of Production, contributing to the development of digital infrastructure for sustainable industrial systems. He is based at the Chair of Computer Science i5, a leading research group in information systems and databases at RWTH Aachen, which supports his work in data-intensive applications and distributed computing environments.

Bernhard Weigandمشاهده پروفایل

استاد

• Heat Transfer
• Droplet Dynamics
• Supersonic Combustion
• +۳ مورد دیگر

Bernhard Weigand is a Professor and Director of the Institute of Aerospace Thermodynamics (ITLR) at the University of Stuttgart's Faculty of Aerospace Engineering and Geodesy. His work focuses on heat transfer, droplet dynamics, and supersonic combustion. He leads research initiatives such as the Collaborative Research Center (Transregio 75) on droplet dynamic processes under extreme conditions. Prof. Weigand has contributed to advancements in fluid mechanics, multiphase flow, and computational modeling, with particular expertise in droplet impact dynamics, microstructured surfaces, and data-driven turbulence models. His research also involves experimental and numerical investigations of aerothermal phenomena in turbine cooling systems and high-speed flows. He has published extensively in top journals and conferences, addressing topics like evaporation, phase change, and the application of machine learning in turbulence modeling. As director, he oversees the ITLR's facilities, including the Multiphase Flow Lab, and collaborates internationally on projects related to aerospace engineering and energy systems. His contributions have been recognized through leadership roles and impactful publications in the field.

Mohammad Sadoghiمشاهده پروفایل

استاد

• Database Systems
• Distributed Systems
• Blockchain Technology
• +۵ مورد دیگر

Mohammad Sadoghi is a Professor in the Department of Computer Science at University of California, Davis, where he leads the Exploratory Systems Lab. His research spans database systems, distributed computing, and blockchain technologies with over 54 publications from 2007-2025 and more than 900 citations. His primary research domains include: Distributed database transactions Byzantine fault tolerance Consensus protocols Event processing systems Blockchain applications Database indexing techniques Prof. Sadoghi's publication trajectory shows evolution from foundational work on boolean expression indexing and event processing to cutting-edge research on blockchain consensus mechanisms. His recent work (2023-2025) demonstrates significant contributions to understanding BFT protocols, with publications in top venues like VLDB, EuroSys, and IEEE TKDE. His research bridges theoretical analysis with practical implementations, particularly focusing on performance optimization and security in distributed environments. His notable recognition includes: ACM Senior Member (2020) Prof. Sadoghi has advised multiple doctoral students who have become active researchers in distributed systems, including Suyash Gupta and Thamir M. Qadah. His lab has secured research funding for projects spanning database engines, consensus protocols, and blockchain infrastructure. The Exploratory Systems Lab maintains strong industry and academic collaborations worldwide, with recent work focusing on edge-cloud consensus applications and high-performance data management systems.

• Computer Science
• Distributed Systems
• Blockchain Technology
• +۳ مورد دیگر

Hans-Arno Jacobsen is a Professor at the Faculty of Computer Science (Technische Universität München, TU Munich) and affiliated with the Department of Electrical and Computer Engineering at the University of Toronto. His work spans Computer Science , Distributed Systems , and Artificial Intelligence . Research interests include Blockchain Technology , Consensus Algorithms , Graph Neural Networks , and Quantum Computing . Recent projects focus on decentralized consensus , energy-efficient databases , and federated learning in edge environments. His 15 most recent articles (2024–2025) explore topics such as dynamic resource orchestration , CRDT-based blockchains , and multimodal depression recognition . Collaborates with researchers like Ruben Mayer , Gengrui Zhang , and Shiqiang Wang on systems for federated computing , blockchain benchmarking , and distributed GNN training .

• Computer Networking
• Bio-inspired Computing
• Network Management
• +۶ مورد دیگر

William Donnelly is a professor at Waterford Institute of Technology in Waterford, Ireland, with a distinguished research career spanning over three decades. His work demonstrates a clear evolution from traditional telecommunications and network management to bio-inspired computing approaches, and more recently to precision agriculture applications and computer graphics. His primary research interests focus on Computer Networking , Bio-inspired Computing , and Precision Agriculture . Early in his career, he specialized in telecommunications management networks (TMN) and service management. He then pioneered work applying biological concepts like chemotaxis and quorum sensing to networking problems, developing bio-inspired routing protocols and service management frameworks. In the 2010s, his research shifted toward precision agriculture applications, particularly dairy farming, where he applied wireless sensor networks, fog computing, and edge analytics to monitor animal behavior and optimize farming practices. Most recently, he has transitioned into computer graphics, focusing on real-time rendering techniques with publications in 2023-2024 on spatiotemporal sampling methods. Analysis of his publication trends reveals a researcher who consistently identifies emerging technological challenges and applies innovative cross-disciplinary approaches to solve them. His work demonstrates remarkable adaptability, moving from telecommunications standards to biological metaphors, then to agricultural technology applications, and finally to computer graphics - always maintaining a focus on optimization, efficiency, and practical implementation. Throughout his career, Donnelly has maintained strong collaborative relationships, particularly with Sasitharan Balasubramaniam and Dmitri Botvich, with whom he has co-authored numerous papers exploring bio-inspired networking approaches. His recent collaborations in computer graphics include Alan Wolfe, Judith Bütepage, and Jon Valdés.

• Network Security
• Cloud Computing
• Internet of Things
• +۳ مورد دیگر

Olivier Festor is a Researcher at INRIA (French National Institute for Research in Digital Science and Technology), specializing in network security, cloud computing, and IoT. His work focuses on developing scalable solutions for modern network challenges, including in-network computation, cloud service security, and anomaly detection. Research Interests: Dr. Festor investigates vulnerabilities in distributed systems, designs protocols for efficient data processing (e.g., stateful in-network computation), and pioneers frameworks for IoT threat emulation. His recent work emphasizes cloud gaming optimization, automated security for service migrations, and darknet-based threat intelligence. Publication Trends: Over 200 publications (1993–2024) reflect a shift toward cloud/IoT security and programmable networks. Recent articles prioritize machine learning for traffic classification, TOSCA-based cloud orchestration, and P4-enabled data planes, highlighting applied research with industry relevance.

• Semantic Web
• Internet of Things
• Ontology Reasoning
• +۵ مورد دیگر

Floriano Scioscia is a researcher at the Polytechnic University of Bari, Department of Electrical Engineering and Information Technology, with extensive contributions to the Semantic Web, Internet of Things, and knowledge-based systems. His work focuses on developing frameworks for semantic reasoning, resource discovery, and intelligent systems in ubiquitous computing environments. His research interests span multiple domains within computer science: Semantic Web technologies and ontology reasoning Internet of Things and Cyber-Physical Systems Cloud-Edge computing architectures Knowledge representation and semantic matchmaker systems Mobile and ubiquitous computing applications Analysis of his recent publications (2023-2025) reveals a strong focus on edge-based semantic reasoning, with significant work on the Tiny-ME and Cowl frameworks for lightweight OWL reasoning on resource-constrained devices. His research has increasingly incorporated blockchain technologies into IoT systems and explored the concept of "Internet of Conscious Things" with social capabilities for smart objects. The interdisciplinary nature of his work bridges computer science with healthcare applications, particularly in clinical decision support systems. Dr. Scioscia has collaborated extensively with researchers including Michele Ruta, Eugenio Di Sciascio, Giuseppe Loseto, and Filippo Gramegna across numerous projects spanning more than 15 years of research output.