جست‌وجوی استاد و پژوهشگر

Valderi Reis Quietinho Leithardtمشاهده پروفایل

استادیار

• Distributed Systems
• Data Privacy
• Internet of Things
• +۱۱ مورد دیگر

Valderi Reis Quietinho Leithardt is an Assistant Professor at the Department of Information Science and Technology, Iscte – University Institute of Lisbon , Portugal, where he holds a full-time position with exclusive dedication. He is an integrated researcher at ISTAR-Iscte (Research Center in Information Sciences, Technologies and Architecture) and a Senior Member of the IEEE . His academic affiliations also include collaborations with the University of Coimbra, University of Salamanca, and Fondazione Bruno Kessler. Education: Post-Doctorate , University of Salamanca, Spain (2019–2021) Post-Doctorate , University of Coimbra, Portugal (2017–2019) PhD in Computer Science , Federal University of Rio Grande do Sul, Brazil (2011–2015) Master’s in Computer Science , Pontifical Catholic University of Rio Grande do Sul, Brazil (2006–2008) Bachelor’s in Data Processing Technology , Higher Education Center of Foz do Iguaçu, Brazil (1999–2002) Research Interests: Valderi's research focuses on Distributed Systems, Data Privacy, Internet of Things (IoT), Cloud Computing, and Intelligent Systems . He explores algorithmic solutions for secure and efficient data management in heterogeneous environments, with applications in smart cities, precision agriculture, healthcare, and energy systems. His work integrates machine learning, blockchain, and federated learning to enhance privacy, security, and system performance. Publication Trends: His recent publications (2024–2025) emphasize time series forecasting, anomaly detection, JVM optimization, and privacy-preserving AI . A strong trend is observed in applying machine learning to power grid fault prediction, blockchain-based healthcare data privacy, and data quality in federated learning. His work bridges theoretical computer science with real-world applications in infrastructure, sustainability, and digital security. Scientific Contributions: Senior IEEE Member Active contributor to open science and reproducibility Involved in interdisciplinary research networks: Embedded and Distributed Systems Laboratory, COPELABS, CTS, CANDEIIA Member of professional societies: IEEE (since 2011), Brazilian Computer Society (since 2003) Academic Service and Leadership: He has held leadership roles in academic programs, including Director and Coordinator of the Master's in Computer Science and Management at Iscte (2025–2027). He actively organizes and participates in scientific events such as IEEE CIoT, DiTTEt, SBSeg, and MobiSPC, serving on organizing and scientific committees. He has coordinated workshops like WTTFC 2024 and 2025, promoting technological trends in future computing. Labs and Research Groups: He is a collaborator in several research networks, including: Embedded and Distributed Systems Laboratory (since 2016) Expert Systems and Applications Laboratory (since 2019) COPELABS – Human-Centered Computing and Cognition (since 2020) Fondazione Bruno Kessler (2021–2025) Center for Technology and Systems (CTS) (since 2023) Advanced Center for Development of Intelligent Systems and Artificial Intelligence (CANDEIIA) (since 2024)

• High-Performance Computing
• Distributed Systems
• Parallel Computing
• +۱۲ مورد دیگر

Juan Jose Costa Prats is a researcher in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), affiliated with the Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona. He is a key member of the CRAAX - Centre de Recerca d'Arquitectures Avançades de Xarxes, contributing to advanced research in computing systems and network architectures. His work is deeply integrated into competitive R&D projects, including Horizon Europe and national initiatives focused on digital innovation and infrastructure. Research Interests: Costa Prats specializes in high-performance and distributed computing, with a strong focus on parallel programming models (especially OpenMP), software distributed shared memory (SDSM), cloud computing, virtualization, and cybersecurity. His recent work emphasizes secure and resilient computing systems, particularly through RISC-V-based architectures, hypervisor extensions, and runtime malware detection using opcode analysis and machine learning. He also contributes to educational innovation through remote datacenter laboratories. Publication Trends: His recent publications (2021–2024) reflect a shift toward secure, trustworthy computing environments, especially in cloud and IoT contexts. Themes include RISC-V virtualization, intrusion detection using transfer learning, GPS spoofing detection, and secure development frameworks like Vitamin-V. These works highlight a convergence of system architecture, security, and machine learning. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: While no formal students are listed, Costa Prats collaborates extensively with leading researchers such as Beatriz Otero, Ramon Canal, and Javier Verdú. He participates in numerous competitive R&D+i projects funded by the Spanish government and the European Union, including Horizon Europe, RIS3CAT, and national research programs, focusing on cloud resilience, intelligent cloud management, and RISC-V ecosystem development. Labs and Teams: He is an active member of the CRAAX research group at UPC, which works on advanced computing architectures. His research often involves collaboration with the Barcelona Supercomputing Center. His projects, such as Vitamin-V and Remote DasaLAB, indicate involvement in both secure computing environments and innovative educational platforms.

Ramon Canal Corretgerمشاهده پروفایل

استاد

• Microarchitecture
• Processor Design
• Memory Design
• +۱۵ مورد دیگر

Ramon Canal Corretger is a Full Professor in the Department of Computer Architecture at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC). He previously served as Vice Dean of Postgraduate Studies at FIB and leads the VirtuOS (Virtualization and Operating Systems) research group. His work bridges computer architecture, hardware security, and system-level reliability. Doctorate from UPC, co-supervised at the University of Wisconsin-Madison Sabbaticals at Harvard University (2006–2007) and the University of Cyprus (2019–2020) Active leadership in EU-funded projects such as Vitamin-V (Horizon Europe) His research focuses on microarchitecture, processor and memory design, reliability under variability, and security at the hardware-software interface. He explores low-power multicore architectures, virtualization optimizations, and secure RISC-V-based systems. His recent work integrates AI for intrusion detection and privacy-preserving federated learning in fog computing environments. The most recent publications demonstrate a strong trend toward security, reliability, and trustworthy computing , particularly in RISC-V ecosystems and cloud/edge infrastructures. There is increasing emphasis on hardware-software co-design , attack detection via performance monitoring , and energy-efficient secure accelerators using emerging technologies like neuromorphic and photonic computing. Scientific Awards: HiPEAC Paper Awards (2017, 2010) IEEE Senior Member (2016) Fulbright Award (2006) IBM Faculty Award (2000) Best Student Paper at HPCA-6 (2000) Multiple teaching excellence recognitions from UPC and AQU Catalunya First Prize in Epson Foundation Rosina Ribalta Award (2001) He has advised several PhD students including Manish Rana, Zoran Jaksic, and Shrikanth Ganapathy, many of whom received honors such as the Intel Doctoral Student Programme recognition. His research is supported by competitive grants from the Spanish government, EU Horizon programs, and industry collaborations. He is actively involved in the design of secure, reliable, and efficient computing systems for future cloud and embedded applications. He leads the VirtuOS research group, which focuses on virtualization, operating systems, and hardware-software interface optimization. The group contributes to open-source RISC-V initiatives and participates in large-scale European R&D projects targeting trustworthy computing infrastructures.

Markus Kleyمشاهده پروفایل

استاد

• Drive Technology
• Mechanical Engineering
• Condition Monitoring
• +۱۲ مورد دیگر

Prof. Dr. Markus Kley is a Professor at Aalen University in the Faculty of Mechanical Engineering and Materials Science, where he serves as the contact person for the Institute for Drive Technology Aalen (IAA). His research and teaching focus on drive technology and waste heat utilization, with a strong emphasis on applied mechanical engineering and system diagnostics. He is actively involved in research projects related to electrified powertrains, condition monitoring, and machine learning applications in mechanical systems. His research interests include vibration analysis, digital twin development, sensor integration, efficiency modeling of electric drive units, and fault detection in electromechanical systems. He applies machine learning and simulation techniques to improve the performance and reliability of drive systems, particularly in off-highway and special vehicles. His work bridges mechanical engineering with data science, focusing on real-world industrial applications. The trend in his recent publications (2019–2022) shows a strong focus on intelligent condition monitoring, using vibration data and machine learning for fault diagnosis in bearings and gearboxes. He also investigates efficiency optimization in electrified transmissions and develops digital twins for motor and drivetrain simulation. His work frequently appears in engineering conferences and journals related to mechanical systems, measurement technology, and applied computing. Prof. Kley collaborates with researchers across Germany and has contributed to numerous peer-reviewed publications and conference proceedings. While no scientific awards are explicitly mentioned, his sustained research output and leadership in projects indicate recognition in his field. He advises students on theses and dissertations and leads research projects in drive technology. His work involves experimental validation, simulation, and industrial collaboration, particularly in the development of advanced drivetrain components and diagnostic systems. He is affiliated with the Institute for Drive Technology Aalen (IAA), which serves as a hub for applied research in mechanical drive systems, partnering with industry to develop innovative solutions in power transmission and energy efficiency.

• 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 .

Alexandre Daniel Batista Martinsمشاهده پروفایل

پژوهشگر

• Predictive Maintenance
• Deep Learning
• Hidden Markov Models
• +۴ مورد دیگر

Alexandre Daniel Batista Martins is a researcher at the Faculty of Engineering, affiliated with the Research Center for Asset Management and Systems Engineering (RCM2+). His work focuses on predictive maintenance, deep learning, and hidden Markov models for industrial systems. Key Research Areas: Predictive Maintenance, Deep Learning, Hidden Markov Models, Industrial Equipment, Vibration Analysis, Condition-Based Maintenance His recent studies include vibration analysis for drying presses, sensor calibration monitoring in industrial systems, and risk assessment algorithms for industrial assets. Notably, his work integrates time-series data with deep neural networks and unsupervised learning techniques. Scientific recognition includes the Young Engineer Innovation Award 2022 . Collaborations span energy systems, pulp paper industries, and industrial asset management.

• Internet of Things
• Body Sensor Networks
• Edge Computing
• +۴ مورد دیگر

G. Fortino is a Professor at the University of Calabria, Italy, with 643 publications, an h-index of 74, and over 21,592 citations. His research spans critical domains including: Internet of Things Body Sensor Networks Edge and Fog Computing Healthcare Applications Machine Learning and Deep Learning Security and Trust Frameworks His work establishes foundational architectures for healthcare industry 4.0 and IoT security, particularly through multi-sensor fusion techniques and edge-based processing. Recent publications demonstrate a pronounced shift toward medical applications, with deep learning models for blood pressure monitoring, brain tumor classification, and emotion recognition integrated with real-time sensor networks. Analysis of his 2017-2021 publications reveals dominant trends in human activity recognition (50% of recent work), security for distributed systems (30%), and medical diagnostics (20%), consistently leveraging machine learning for real-time processing in resource-constrained environments. His most influential papers address IoT security taxonomies and body sensor network fusion, with citation counts exceeding 500. No information regarding scientific awards, student advising, grants, or dedicated research laboratories was found in the provided materials.

• Cybersecurity
• Digital Rights Management
• Internet of Things Security
• +۵ مورد دیگر

Eva Rodríguez is an Associate Professor in the Department of Network Engineering at Pompeu Fabra University's School of Engineering, specializing in cybersecurity, digital rights management, and IoT security. With over 50 publications since 2003, she has established herself as a leading researcher in secure network architectures and privacy-preserving technologies. Her research focuses on applying machine learning techniques to cybersecurity challenges, particularly in mobile and IoT environments. Rodríguez has published extensively on deep learning for intrusion detection, privacy protection mechanisms, and security frameworks for next-generation networks (5G/6G). She has also made significant contributions to RISC-V processor security through the Horizon Europe Vitamin-V project. Recent work shows a strong emphasis on federated learning approaches for privacy preservation in fog computing environments and the development of security management architectures for resilient wireless ecosystems. Her publication record demonstrates consistent leadership in both theoretical frameworks and practical implementations of security solutions. Among her notable contributions are comprehensive surveys on deep learning techniques for mobile network security and machine learning methods for IoT privacy protection, which have become important references in these rapidly evolving fields. As a research supervisor, she has mentored several doctoral students including Norma Gutiérrez and Beatriz Otero, who now appear as co-authors on her recent publications. Her collaborative work extends across multiple European research projects, demonstrating strong integration within the international cybersecurity research community.

• Artificial Intelligence
• Natural Language Processing
• Knowledge Management
• +۷ مورد دیگر

Jorjeta Jetcheva is an Assistant Professor in the Computer Engineering Department at San José State University, part of the Charles W. Davidson College of Engineering. She brings extensive industry experience from leadership roles at Accenture, Fujitsu Laboratories of America, Itron, and Firetide, where she led innovations in AI, smart grids, and wireless networking. Her research focuses on Artificial Intelligence-based Personal Assistants, Natural Language Processing, and Knowledge Management . Her work bridges human-centered AI and enterprise applications, with emphasis on intelligent agents, knowledge platforms, and ethical AI systems. She explores how AI can enhance user productivity, decision-making, and system security across domains such as healthcare, energy, and customer service. The recent publications highlight a strong trend in Natural Language Processing applied to real-world problems like fake health news detection, agent assist systems, and knowledge graph integration. Her research also spans Smart Grid analytics, cybersecurity, and AI ethics , reflecting a multidisciplinary approach grounded in both theoretical rigor and practical deployment. Scientific Awards: Best Application Paper Award at IEEE Big Data Service 2022 Best Paper Award at SmartGridComm 2014 Top 10 Finalist, Fujitsu Next Generation Product Idea Contest 2016 ('Robo Butler') CSU STEM-NET Faculty Fellow (2023) Advising and Grants: Professor Jetcheva is the Principal Investigator of a $2.5 million NSF S-STEM grant titled 'Empowering Students to Succeed in Engineering and Computer Science'. She actively mentors graduate students, including Master’s student Garima Chaphekar, with whom she co-authored an award-winning paper on fake health news detection. She serves on the SJSU AI Advisory Committee and leads the AI Research Cluster, fostering interdisciplinary collaboration and student success. Labs and Teams: She leads the AI Research Cluster at SJSU, promoting innovation in artificial intelligence across departments. Her work involves close collaboration with students, industry partners, and interdisciplinary teams focused on deploying scalable, ethical AI solutions.

• heterogeneous porous media
• flow and transport
• Geothermal systems
• +۳ مورد دیگر

Jerry P. Fairley serves as a Full Professor in the Department of Earth and Spatial Sciences within the College of Science at the University of Idaho. His academic career spans three decades with continuous research contributions in hydrogeological systems and geothermal energy applications. Education: B.S. in Geology (1984) from State University of New York College at Cortland M.S. in Geosciences (1991) from University of Nevada, Las Vegas Ph.D. in Earth Resources Engineering (2000) from University of California, Berkeley Professor Fairley's research centers on fluid dynamics in complex geological media , with emphasis on heterogeneous porous systems, geothermal reservoir characterization, and environmental applications including carbon sequestration and nuclear waste disposal. His work integrates field measurements, geospatial analysis, and numerical modeling to address challenges in hydrothermal systems and arid-region groundwater management. Recent projects demonstrate particular expertise in Yellowstone hydrothermal dynamics and Chilean mining district hydrology. Analysis of his 15 most recent publications (2017-2024) reveals consistent focus on hydrothermal system behavior (particularly Yellowstone), groundwater recharge in arid environments (notably Chilean Andes), and geothermal resource assessment using geostatistical methods. His work bridges fundamental fluid mechanics with practical energy and water resource applications, often employing innovative field measurement techniques like ice box calorimetry and thermal anomaly mapping. Professor Fairley has led significant collaborative research initiatives including the NSF-funded project Constraining Heat Flux from the Shallow Geothermal System, Yellowstone Caldera (2013), demonstrating sustained external funding for his geothermal investigations. His fieldwork spans diverse geological settings from Idaho's Snake River Plain to active volcanic zones in Japan and Chile.

Grzegorz Liśkiewiczمشاهده پروفایل

استاد

• Fluid Dynamics
• Turbomachinery
• Compressor Stability
• +۳ مورد دیگر

Grzegorz Liśkiewicz serves as Professor and Rector's Representative for Academic Entrepreneurship at Lodz University of Technology's Institute of Fluid-Flow Machinery. His work bridges academic research with industrial applications in turbomachinery, compressor systems, and Industry 4.0 implementations. Active since at least 2013, he maintains significant research output while holding university leadership roles. Liśkiewicz's research centers on fluid dynamics phenomena in rotating machinery, particularly surge inception, rotating stall, and inlet recirculation in centrifugal compressors. Recent work expands into nanofluid heat transfer, magnetohydrodynamics, and machine learning applications for predictive maintenance. His interdisciplinary approach connects mechanical engineering fundamentals with sustainability initiatives including circular economy frameworks and microalgae biotechnology. Current publication trends (2021-2025) reveal three dominant threads: 1) Advanced compressor stability analysis using empirical mode decomposition and image recognition ML models; 2) Nanofluid/MHD investigations for thermal systems; 3) Renewable energy applications like vertical-axis wind turbine aerodynamics. This evolution demonstrates increasing methodological sophistication while maintaining core focus on fluid machinery reliability. As a key researcher at Lodz University of Technology's Institute of Fluid-Flow Machinery, Liśkiewicz collaborates with international institutions including General Electric, Oxford, and Cambridge. His work environment integrates experimental facilities like PIV measurement systems with computational modeling for comprehensive turbomachinery analysis, supporting both academic inquiry and industrial problem-solving.

• Embedded Systems
• Fault Tolerance
• Precision Agriculture
• +۵ مورد دیگر

Maurizio Rebaudengo is a Full Professor at the Department of Control and Computer Science (DAUIN) of the Polytechnic University of Turin. He is a member of the PIC4SeR Interdepartmental Center for Service Robotics and holds expertise in embedded systems, fault tolerance, and sensor network applications. Research Interests: Embedded systems, fault tolerance, precision agriculture, RFID technology, wireless sensor networks, and system dependability Scientific Awards: Ramamoorthy Best Paper Award (1997) Leadership Roles: Program Chair for the 4th International EURASIP Workshop on RFID, Committee Member for Public Administration Agreements (2020-2024) Projects: Scientific Director for HONEY (Hybrid ONline tEchnologY for particle therapy), FDM (Food Digital Monitoring), IDEM (Internet of Data for Environmental Monitoring), and OPLON (Healthy Longevity Opportunities). Teaching: Course lecturer for Electronic Calculators, Computer Science, Systems Programming, and Cybersecurity at both undergraduate and postgraduate levels. His work spans IoT applications in agriculture and healthcare, focusing on low-cost sensor networks , RFID-based traceability , and cyber-physical system resilience . Recent publications emphasize particulate matter monitoring and secure communication protocols in wireless environments.

Erhun Kundakcıoğluمشاهده پروفایل

استاد

• Combinatorial Optimization
• Decision Analytics
• Supply Chain Management
• +۹ مورد دیگر

Erhun Kundakcıoğlu is a Professor in the Department of Industrial Engineering at Ozyegin University's Faculty of Engineering. He received his Ph.D. in Industrial and Systems Engineering from the University of Florida (2009) with a minor in Computer and Information Science and Engineering, following an M.S. in Industrial Engineering at Sabancı University (2004) and B.S. at Bilkent University (2002). He served as Assistant Professor at University of Houston (2009-2013) and Director/Distinguished Scientist at Optym (2019-2021). Ph.D.: Industrial and Systems Engineering, University of Florida (2009) M.S.: Industrial Engineering, Sabancı University (2004) B.S.: Industrial Engineering, Bilkent University (2002) His research focuses on combinatorial optimization and decision making under uncertainty , with applications in healthcare analytics , sustainable energy systems , supply chain management , and data science . He has developed optimization models for inventory control, lot sizing, and routing problems under uncertain demand/supply conditions, particularly in healthcare and humanitarian contexts. His work with the Datart Lab integrates mathematical programming into practical solutions for industry partners. Recent publications highlight his expertise in disaster relief inventory simulation, healthcare inventory management, and time series decomposition optimization. He supervises active graduate students including Deniz N. Yoltay (Ph.D.) and Buket İpek Akbal (M.S.). Early Career Award, TUBITAK Teaching Excellence Award, University of Houston Florida Chapter Scholarship, HIMSS Foundation As Associate Editor for the Journal of Global Optimization , Optimization Letters , and SN Operations Research Forum , he contributes to academic discourse in optimization and analytics. His consulting firm Datart R&D Management Consulting bridges academic research with industry applications in Turkey and abroad.

Manuel Fernández Delgadoمشاهده پروفایل

دانشیار

• Machine Learning
• Pattern Recognition
• Computer Vision
• +۳ مورد دیگر

Manuel Fernández Delgado is an Associate Professor at the University of Santiago de Compostela (manuel.fernandez.delgado@usc.es). His research spans Machine Learning , Pattern Recognition , and Computer Vision with applications in medical diagnostics, agricultural monitoring, and gender-inclusive education. PhD in Computer Science (1999) Developed software tools: Govocitos , CystAnalyser , STERapp Research highlights: Medical Imaging : Breast cancer and oral leukoplakia analysis via image segmentation/classification Agricultural AI : Nutrient deficiency detection in wheat, marbling analysis in ham Gender Equality : Pioneering computational thinking education with gender perspectives Robotics : Fault diagnosis systems for antenna arrays Key methodologies include Extreme Learning Machines, Support Vector Machines, and Deep Learning. He collaborates with teams in TELGalicia and TecAnDaLi networks.

Anita Torabiمشاهده پروفایل

استاد

• Structural Geology
• Fault Mechanics
• Fluid Flow in Deformed Reservoirs
• +۶ مورد دیگر

Anita Torabi is a Professor in Structural Geology at the University of Oslo , affiliated with the Department of Geosciences. Her research focuses on structural geology, fault mechanics, fluid flow in deformed reservoirs, CO2 storage, geothermal reservoirs, and seismicity. She employs advanced methods such as 3D seismic analysis, fault asperity studies, and machine learning for geological applications. Key affiliations: University of Oslo, NORCE (2015-2018), Uni Research CIPR (2008-2018) Education: PhD in Geosciences (2008) - University of Bergen Research Interests include: Fault mechanics and deformation processes Fluid flow in faulted reservoirs CO2 storage and green energy solutions Geomechanics and seismic risk assessment Machine learning in structural geology Basement rock fracture analysis Recent Publications highlight trends in: 3D fault characterization using deep learning Fluid dynamics in siliciclastic-carbonate systems Seismic attribute analysis for fault geometry Compaction bands in Tertiary-Quaternary sequences Ontology-driven geological modeling Machine learning applications for CO2 storage Research Leadership includes projects such as: CO2 Seal and Bypass – COPASS CO2SafeQuest (fault behavior for CO2 storage) gigaCCS@UiO (Center of Excellence in Carbon Capture) VICCO (volcanic-sedimentary CO2 storage) Pool Studies CO2 Storage research group