جست‌وجوی استاد و پژوهشگر

Lisa Ehrlingerمشاهده پروفایل

پژوهشگر

• Data quality
• Knowledge graphs
• Ontologies
• +۵ مورد دیگر

Lisa Ehrlinger is a Senior Researcher at the Information Systems Research Group within the Digital Engineering Faculty of the University of Potsdam. She holds a PhD in Engineering Sciences ("passed with distinction") and an MSc in Computer Science from Johannes Kepler University Linz (JKU), both with distinction. Her work bridges academic research and industrial application. Her research focuses on Data Quality , Knowledge Graphs , Ontologies , Semantic Technology , Data Catalogs , and Data Integration , particularly with NoSQL databases. She has over 7 years of applied research experience at the Software Competence Center Hagenberg GmbH (SCCH), including project management and team leadership. Her recent publications emphasize SHACL validation , Data Stream Pollution , Dashboard Design , and Ontology-Driven Analysis , reflecting trends in data quality automation , semantic modeling , and industrial data governance . She co-chaired the Quality of Databases (QDB) workshop (2023, 2024) and serves on the MIT CDOIQ Symposium review board (since 2023). Her outreach includes lecturing at JKU and reviewing for journals like ACM Journal of Data and Information Quality (JDIQ) and Elsevier Journal of Information Fusion .

Bartosz Baliśمشاهده پروفایل

دانشیار

• scientific workflows
• data science
• e-Science
• +۲ مورد دیگر

Bartosz Baliś serves as Associate Professor at the Institute of Computer Science, AGH University of Science and Technology, and holds a Senior Postdoctoral position in Extreme-scale Data and Computing at the Sano Centre for Computational Medicine. He actively contributes to the CERN ALICE experiment as a core team member. His educational foundation includes dual degrees from AGH University and Jagiellonian University, culminating in both PhD and DSc (habilitation) qualifications in Computer Science from AGH University. These advanced credentials underpin his specialized expertise in computational methodologies. Dr. Baliś pioneers research in scientific workflows, data science, and distributed computing systems , with particular emphasis on cloud-native architectures and e-Science infrastructures. His innovative work bridges theoretical computer science with practical applications in large-scale data processing, advancing methodologies for scientific computing environments through novel workflow management solutions. Recent publications demonstrate a clear trajectory toward serverless computing paradigms and comprehensive observability frameworks for scientific applications, reflecting his commitment to solving scalability challenges in modern computational research through cutting-edge cloud technologies. He has secured substantial research funding through participation in major European initiatives including CrossGrid, CoreGRID, K-Wf Grid, ViroLab, Gredia, UrbanFlood, PaaSage, and WATERLINE across FP5-FP7 and Horizon 2020 programs. His leadership extends to conference organization as Euro-Par 2020 General Co-chair, HPCS 2018-19 Tutorials Co-chair, and IEEE/ACM SC committee roles. Within institutional frameworks, Dr. Baliś contributes to the Extreme-scale Data and Computing research group at Sano Centre while maintaining active collaboration with the international CERN ALICE collaboration, where his computational expertise supports high-energy physics research infrastructure.

James Martinمشاهده پروفایل

استاد

• Geophysics
• Geotechnical Engineering
• Sensor Technology
• +۵ مورد دیگر

Professor James Martin is a faculty member in the Department of Mechanical and Construction Engineering at Northumbria University. His expertise lies in geophysical and geotechnical methods, with a focus on developing innovative exploration, monitoring, and characterization techniques. He specializes in sensor technology, signal processing, and robust measurement systems for harsh environmental conditions. His research integrates advanced instrumentation like IoT platforms, surface acoustic wave sensors, and ground-penetrating radar to address challenges in infrastructure monitoring, cold environment engineering, and material durability. Key research areas include permafrost degradation analysis, pavement structural health assessment, and landslide detection systems. His work often bridges academic and industrial applications, leveraging his experience in oil and gas project management to solve complex engineering problems. Recent projects involve low-cost IoT data capturing systems, anti-icing technologies using propagating surface acoustic waves, and CLT panel moisture monitoring for sustainable construction. His publications span journals such as Permafrost and Periglacial Processes , IEEE Transactions on Instrumentation and Measurement , and Langmuir , reflecting contributions to geophysics, materials science, and civil engineering. His research emphasizes real-time environmental monitoring, subsurface characterization, and holistic infrastructure solutions. James Martin has collaborated on interdisciplinary projects involving cold climate engineering, structural health monitoring, and geotechnical hazard mitigation. His work highlights the application of advanced sensors and data analytics to address global challenges in environmental sustainability and infrastructure resilience.

• Computer Science
• Database Systems
• Business Process Management
• +۵ مورد دیگر

Jianwen Su is a Professor at the University of California, Santa Barbara, USA, with a distinguished career spanning over three decades in Computer Science , particularly in Database Systems , Business Process Management , and Web Services . Their research bridges theoretical foundations with practical applications, focusing on data-centric process modeling , formal verification , and spatio-temporal data analysis . Key contributions include the design of artifact-centric workflow models , temporal constraint languages , and query systems for uncertain data . Recent work integrates machine learning with proteomic analysis for stress biomarker discovery and LLM-based extraction of structured data from clinical reports. Notable scientific recognition includes the 2019 ACM PODS Alberto O. Mendelzon Test-of-Time Award . Collaborations span institutions globally, with frequent co-authorship in journals like Information Systems , ACM Transactions on Management Information Systems , and conferences such as BIBM and TIME .

• Process Mining
• Computer Science
• Data Science
• +۵ مورد دیگر

Wil M. P. van der Aalst is a Professor at RWTH Aachen University's Chair of Process and Data Science, Germany. Formerly affiliated with Eindhoven University of Technology's Department of Mathematics and Computer Science, he is a leading expert in Process Mining, a field he has significantly advanced through foundational research and standardization efforts. His work bridges computer science, data science, and business process optimization. Research Interests: Dr. van der Aalst focuses on Process Mining , exploring topics like privacy-preserving analytics, object-centric event logs (OCEL), and integration of AI techniques (e.g., LLMs) into process discovery. He emphasizes practical applications in healthcare, manufacturing, and cybersecurity. Recent trends in his work include federated process mining, partial order-based analysis, and standardization of process data formats. Publications: Over 1,200 publications demonstrate his contributions to understanding process behavior, conformance checking, and workflow optimization. Notable directions include privacy-enhanced event log publishing, blockchain process analysis, and federated learning approaches for distributed systems. Awards: While specific awards are not listed, his extensive publication record and leadership in Process Mining communities (e.g., founder of the ICPM conference) highlight his global recognition. Advising & Grants: Collaborates widely with industry and academia on projects like the Medical Informatics Initiative and Smart Manufacturing standards. Leads the development of open-source tools like Rust4PM and the OCEL standard. Labs/Teams: Directs the Process and Data Science group at RWTH Aachen, fostering interdisciplinary research in process intelligence and AI-driven analytics.

• Process Mining
• Blockchain Technology
• Declarative Process Models
• +۳ مورد دیگر

Tijs Slaats is an academic affiliated with the University of Copenhagen, Denmark. His work focuses on process mining, blockchain technology, and declarative process modeling. He has contributed to advancements in declarative frameworks like Dynamic Condition Response Graphs (DCRGs) and their applications in adaptive case management, smart contracts, and legal adjudication systems. His research bridges computer science, distributed systems, and business process management. Research interests include declarative process models, blockchain sharding protocols, formal verification of event-driven systems, and the integration of machine learning with legal processes. His work emphasizes practical applications in finance, healthcare, and public administration through collaborative projects like BRFkredit and the EcoKnow initiative. Recent articles highlight innovations in process discovery algorithms, smart contract security, and scalable blockchain solutions. His contributions span theoretical frameworks and real-world implementations, often involving interdisciplinary collaborations with institutions like Algorand and the Danish Refugee Council.

• Business Process Management
• Process Mining
• Blockchain Technology
• +۴ مورد دیگر

Mathias Weske is a Professor at the Hasso Plattner Institute in Potsdam, Germany. His research focuses on Business Process Management (BPM), Process Mining, Blockchain-based process execution, and Robotic Process Automation (RPA). He has contributed extensively to advancing methodologies for resource allocation in processes, decision support systems, and integrating blockchain technology into collaborative processes. Key research areas include business process analysis, data-driven case management, and healthcare process optimization. His work spans theoretical frameworks (e.g., BPMN extensions, choreography models) and practical tools (e.g., OpenBPT platform). Recent efforts emphasize RPA complexity metrics, accessibility in process modeling for visually impaired users, and democratizing RPA mining techniques. Publications frequently explore interdisciplinary applications like healthcare process mining and blockchain transaction ordering. His contributions bridge theory and practice, addressing challenges in automation, collaboration, and process intelligence across industries.

• Process Mining
• IoT Integration
• Business Process Management
• +۳ مورد دیگر

Estefanía Serral is a prominent researcher affiliated with KU Leuven, Belgium, specializing in interdisciplinary areas at the intersection of Process Mining, Internet of Things (IoT), and Business Process Management. Her work focuses on integrating IoT data with business processes, enhancing process analytics through machine learning, and developing adaptive systems for smart environments. Research Contributions: Pioneered methods for IoT-enhanced process mining, including frameworks for data quality management and event log enrichment. Developed techniques for bridging IoT sensor data with traditional process models, enabling context-aware process analysis. Contributed to the design of adaptive business processes using context-aware Petri nets and decision-driven architectures. Publications: Her most recent works (2023-2025) emphasize digital twins of business processes, deep learning applications in agriculture, and systematic reviews on Industry 4.0 process mining. Early contributions include foundational work on context-adaptive systems and decision service architectures. Awards & Recognition: No explicit awards mentioned, but her extensive publication record in top venues (e.g., BPM, ER, CEUR Workshop Proceedings) reflects her scholarly impact. Technical Contributions: Co-developed the NICE IoT-centric event log model and SensorStream XES extension for process mining. Authored frameworks like Decision as a Service (DaaS) for decision integration in processes.

Éva Hajnalمشاهده پروفایل

دانشیار

• Environmental Science
• Computer Science
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Éva Hajnal is an Associate Professor and Vice Dean for Research at the Alba Regia Technical Faculty of Óbuda University. Her work focuses on interdisciplinary research spanning environmental monitoring technologies, artificial intelligence applications in agriculture, and sensor development. She leads efforts in livestock health monitoring using rumen bolus sensors and has contributed to ecological assessment methodologies using diatoms. Her email is hajnal.eva@amk.uni-obuda.hu , and she is located at Room 25 in Building K on Budai út 45. Her research integrates computer science with ecological challenges, addressing issues like groundwater quality and wind energy modeling. Research interests include: Biosensor development for livestock health AI-driven environmental data analysis Ecological status assessment using diatoms Virtual reality and motion tracking systems Online mapserver technologies Recent work emphasizes long-term rumen temperature analysis, low-computational algorithms for cattle heart rate estimation, and multilingual handwritten character datasets. Her research trends show strong focus on applying computational methods to ecological and agricultural challenges. She has contributed to projects like wastewater management control systems and biodiversity indices for freshwater ecosystems. No specific scientific awards are listed. Her administrative role as Vice Dean for Research involves overseeing research strategy and collaboration at the faculty level. No advising or grant details are provided in available texts. Her work is associated with the university's technical faculty and environmental engineering teams, contributing to both academic and applied research initiatives.

• Wireless Communications
• Millimeter-Wave Networks
• 5G/6G Networking
• +۵ مورد دیگر

Jörg Widmer is a leading researcher at IMDEA Networks Institute in Madrid, Spain, specializing in wireless communications, mobile networking, and next-generation network technologies. With over 297 publications spanning from 2000 to the present, his work has significantly contributed to the fields of millimeter-wave communications, 5G/6G networking, and integrated sensing and communication systems. Widmer's research interests span wireless communications, millimeter-wave networks, 5G/6G networking, mobile systems, integrated sensing and communication (ISAC), and AI/ML applications for networking. His recent work (2023-2025) demonstrates a strong focus on 5G performance analysis, explainable AI for network forecasting, millimeter-wave technologies, and the integration of sensing capabilities with communication systems. He has pioneered research in directional communications, channel modeling, and network optimization for next-generation wireless systems. His extensive publication record shows a clear evolution from fundamental wireless networking research toward more complex integrated systems that combine communication with sensing capabilities. Recent publications reveal a strong emphasis on making AI applications in networking more transparent and explainable, while also addressing practical deployment challenges in operational 5G networks. The breadth of his work spans theoretical modeling, experimental validation, and practical system implementation. Widmer has collaborated extensively with researchers across Europe and internationally, particularly with Claudio Fiandrino (34 co-authored papers), Adrian Loch (30 papers), Jesus Omar Lacruz (21 papers), and Joan Palacios Beltran (20 papers). His work appears in top-tier venues including IEEE INFOCOM, MobiCom, IEEE Transactions on Mobile Computing, and IEEE Transactions on Wireless Communications.

• Business Process Management
• Process Mining
• Data Science
• +۳ مورد دیگر

Pnina Soffer is a Professor at the University of Haifa. She specializes in Business Process Management (BPM), Process Mining, and Information Systems. Her research focuses on process-aware systems, data impact analysis, and decision support mechanisms. She has contributed to advancing techniques for analyzing business processes, including workarounds detection, data inaccuracy mitigation, and process model quality improvement. Her work integrates interdisciplinary approaches, combining computer science with organizational behavior and healthcare informatics. She frequently collaborates with leading researchers in BPM and process mining, contributing to conferences like CAiSE and BPMDS. Her recent efforts emphasize the intersection of process mining with cybersecurity and human factors, exploring how cyber risks propagate through business processes and how facial expressions can predict task performance in process mining tasks. Key research areas include conceptual modeling, event log analysis, and the application of database principles to normalize process logs. Her work aims to bridge theoretical frameworks with practical applications, such as improving clinical decision support systems through layered guideline models and enhancing process robustness against data inaccuracies.

Thomas T. Johnمشاهده پروفایل

استاد آموزشی

• Statistical methods and applications
• Econometric (especially Discrete Choice) Models
• Associated Experimental Designs
• +۳ مورد دیگر

Thomas T. John is an Assistant Teaching Professor in the Department of Mathematics at Syracuse University. He specializes in statistical methods, econometric models, and data science applications in clinical research. His work focuses on improving healthcare efficiency through statistical computing and censored data analysis. He maintains an active teaching portfolio and contributes to research in survival analysis and medical informatics. Education: Ph.D., Mathematics (Statistics), Syracuse University, 2004 M.S., Mathematics, Syracuse University, 2000 B.A., Mathematics, City University of New York, Queens College, 1996 Research Interests: John’s research integrates statistical methodologies with practical applications in econometrics, healthcare systems, and reliability engineering. He explores log-location-scale distributions in survival analysis and develops experimental designs for low-acuity clinical settings. Recent Research Trends: His work bridges statistical theory and clinical practice, emphasizing workflow optimization in healthcare and the adoption of IT solutions to enhance operational efficiency. Collaborations include designing vital signs monitors and evaluating single sign-on systems in hospitals. Teaching & Service: John has taught a wide range of courses, including Probability, Mathematical Statistics, and Data Science. He served as a Portfolio Reviewer for Women in Science and Engineering (WiSE) at Syracuse University (2020–2021). His personal research website is here .

• Cybersecurity
• Machine Learning
• Internet of Things (IoT)
• +۵ مورد دیگر

Qusay H. Mahmoud is affiliated with the University of Ontario Institute of Technology, Canada, and previously held roles at the University of Guelph (2002–2013) and completed his PhD at Middlesex University, London (2002). His research focuses on cybersecurity, machine learning, IoT systems, blockchain, and smart policing. He has authored over 200 peer-reviewed publications in journals like IEEE Access, Sensors, and Future Internet, and conferences such as Canadian AI, IEEE CCWC, and SMC. His work spans anomaly detection in IoT networks, adversarial machine learning, cryptocurrency fraud analysis, and reinforcement learning applications. Research Interests: Dr. Mahmoud’s expertise includes developing AI-driven solutions for cybersecurity challenges, IoT security frameworks, blockchain applications in healthcare and finance, and enhancing smart policing through machine learning. He also explores adversarial training for robust deep learning models and reinforcement learning for bridging virtual-physical environment gaps. His contributions emphasize real-world applications, such as phishing detection, fault-injection attack mitigation, and cryptocurrency market analysis. Articles Trends: Recent work highlights the use of large language models (LLMs) in crime prediction, adversarial robustness in GANs, and reinforcement learning for robotics. His publications often intersect machine learning with cybersecurity, IoT, and blockchain, addressing practical challenges like anomaly detection, fraud prevention, and system reliability. Grants & Advising: While specific grants are not detailed, his extensive publication record suggests sustained research activity. No formal student advisees are listed, though collaborations with co-authors like Akramul Azim and Michael Lescisin indicate active teamwork. Labs/Teams: Engaged in interdisciplinary projects at the University of Ontario Institute of Technology, focusing on AI, cybersecurity, and IoT systems. Collaborations extend to institutions globally, addressing emerging tech challenges like blockchain in healthcare and edge computing security.

André van Hoornمشاهده پروفایل

استاد

• Software Performance Engineering
• Distributed Systems
• Software Architecture
• +۴ مورد دیگر

Prof. Dr.-Ing. André van Hoorn was a distinguished academic at the University of Hamburg , leading the Software Development and Construction Methods group in the Department of Computer Science since 2023. His research focused on quantitative analysis of performance and quality attributes in complex distributed systems, contributing significantly to software engineering and architecture communities. He held leadership roles in SPEC Research and organized ICPE and ECSA conferences. Education and Career: He previously worked at Oldenburg, Kiel, and Stuttgart universities. His academic rank was Professor, reflecting his expertise and leadership in software systems research. Research Interests: His work emphasized resilience engineering, microservices, cloud computing, and performance optimization. He developed tools like Kieker for performance monitoring and frameworks like Radon for serverless computing analysis. Teaching and Outreach: He actively mentored students and pioneered the SeaSchool project to introduce students to non-programming aspects of computer science. His pedagogical innovations included tablet-based e-exams in large courses. Legacy: His contributions span academic leadership, impactful research, and community engagement. Condolence books are available at the University of Hamburg’s Department of Computer Science, and a fundraising campaign supports his family.

Shane McIntoshمشاهده پروفایل

دانشیار

• Empirical Software Engineering
• Software Delivery
• Release Engineering
• +۲ مورد دیگر

Shane McIntosh is an Associate Professor and Associate Director of Graduate Studies at the Cheriton School of Computer Science, University of Waterloo. He holds a Ph.D. from Queen's University (2015) and specializes in empirical software engineering, software delivery processes, and software quality improvement through data mining techniques. His research focuses on analyzing historical and operational data from large-scale software systems to derive actionable insights. Key areas include release engineering, DevOps optimization, and software quality enhancement. McIntosh employs empirical methods to study build systems, continuous integration practices, and defect prediction. McIntosh's recent publications demonstrate a strong focus on improving software development workflows through empirical analysis of build systems, CI/CD pipelines, and code quality metrics. His work frequently addresses efficiency optimization in development operations and vulnerability detection in machine learning systems.