Find Academic Supervisors

• Process Mining
• Blockchain Technology
• Declarative Process Models
• +3 more

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
• +4 more

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
• +3 more

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 HajnalView profile

Associate Professor

• Environmental Science
• Computer Science
• Artificial Intelligence
• +3 more

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
• +5 more

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
• +3 more

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. JohnView profile

Teaching Professor

• Statistical methods and applications
• Econometric (especially Discrete Choice) Models
• Associated Experimental Designs
• +3 more

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)
• +5 more

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 HoornView profile

Professor

• Software Performance Engineering
• Distributed Systems
• Software Architecture
• +4 more

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 McIntoshView profile

Associate Professor

• Empirical Software Engineering
• Software Delivery
• Release Engineering
• +2 more

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.

Stefan BrucknerView profile

Professor

• Biomedical Visualization
• Volume Rendering
• Visual Data Exploration
• +1 more

Stefan Bruckner is Professor of Visualization at the University of Bergen, specializing in biomedical visualization, volume rendering, and visual data exploration. His work develops novel techniques for analyzing complex scientific datasets across meteorology, medicine, and materials science. Dr. Bruckner's research group develops interactive visual analytics tools for weather forecasting, medical diagnostics, and ensemble data analysis. His methodological innovations include GPU-accelerated rendering, visual parameter exploration, and uncertainty visualization. He received the 2011 Eurographics Young Researcher Award for contributions to illustrative visualization. Professional service includes program committee roles for IEEE VIS, Eurographics, and ECRTS conferences. His pedagogical contributions span visualization, computer graphics, and programming languages at institutions including École normale supérieure and École polytechnique.

• Cybersecurity
• Database Forensics
• Machine Learning Applications in Security
• +4 more

Peter Frühwirt is an External Lecturer in the Department of Information Systems Engineering at Vienna University of Technology (E194). His research focuses on cybersecurity, database forensics, and usable security, with a particular emphasis on tamper-proof systems, QR code vulnerabilities, and forensic-aware database solutions. Education : Dipl.-Ing. Dr.techn., BSc BSc Research Interests : Cybersecurity, Database Forensics, Machine Learning Applications in Security, QR Code Security, Web Application Security, Traffic Classification His recent publications analyze forensic-aware database protocols, QR code threats, and machine learning applications for attacker profiling. He teaches courses including Advanced Software Engineering, Software Engineering Project, and Software Quality Assurance.

Jim DaviesView profile

Professor

• Software Engineering
• Health Informatics
• Model-Driven Engineering
• +4 more

Jim Davies is a Professor of Software Engineering and Director of the Oxford EPSRC Centre for Doctoral Training in Health Data Science at the University of Oxford's Department of Computer Science. He leads research in model-driven engineering, semantics-driven technology for healthcare and governance, and data management. His work focuses on advancing software engineering practices for complex systems and large-scale health data challenges. He has directed major initiatives like the UK 100,000 Genomes Project and chairs key research consortia in health informatics. Education: DPhil, MSc, MA (Oxford), with early training in mathematics at New College, Oxford. His career spans roles as lecturer, programme leader (Software Engineering Programme since 2000), and academic leadership in formal methods and software engineering. Research interests include formal methods, model-driven systems, clinical trial software frameworks, and semantic interoperability. He has pioneered metadata-based architectures for cancer research (CancerGrid) and vaccine studies. His recent work addresses big data challenges in healthcare, including AI-driven analytics and electronic governance. External roles include Programme Committee memberships in conferences like SBMF, IFM, and RAISE, reflecting his contributions to formal methods and software engineering education. Collaborations span clinical, biomedical, and governmental domains, emphasizing translational research. Lab/Team: Leads the Software Engineering Research Group and collaborates with the Big Data Institute and Li Ka Shing Centre for Health Information and Discovery. Current projects include semantics-driven health informatics and agile big-data systems.

Lucien Nana YoboView profile

Assistant Professor

• Isotope Geochemistry
• Paleoclimate
• Paleoceanography
• +1 more

Lucien Nana Yobo is an Assistant Professor at Texas A&M University, specializing in Isotope Geochemistry, Paleoclimate, and Paleoceanography. His research focuses on reconstructing Earth’s past environments using non-traditional stable isotopes (Ca, Sr, Cr, Mg, Fe) and numerical models. He actively seeks motivated undergraduate and graduate students/postdocs to join his group. Contact him via email for opportunities. Education: Ph.D. in Geology, University of Houston (2021) M.S. in Geology, University of Nebraska-Lincoln (2014) B.S. in Geology, California State University, Fresno (2012) Research Interests: Dr. Yobo investigates ocean anoxic events (e.g., OAE 2) and paleoclimate dynamics. He uses Cr and Fe isotopes to trace volcanic impacts and continental weathering, while integrating numerical box models to understand biogeochemical cycles. His work spans carbonate sedimentology and stratigraphy, with recent emphasis on the Cenomanian-Turonian boundary and Ediacaran Shuram Excursion. Publications Overview: His recent work explores continental weathering drivers of ancient climate shifts, LIP volcanism’s role in ocean anoxia, and isotopic tracers for reconstructing environmental changes. Notable studies include the Agulhas Plateau and IODP Expedition 397 datasets. Awards: Fellow, National GEM Consortium (2019–2021) Advising & Grants: Dr. Yobo mentors students in field and lab-based research. His current projects include analyzing sediment cores from the Iberian Margin and Pacific High regions. He emphasizes interdisciplinary approaches to paleoenvironmental reconstruction. Labs/Teams: Collaborates with international teams on IODP expeditions and geochemical data initiatives, fostering early-career scientific ocean drilling communities.

Guillaume PirotView profile

Research Fellow

• Subsurface Characterization
• Spatial Statistics
• Bayesian Inference
• +4 more

Guillaume Pirot is a Research Fellow at the School of Earth and Oceans, University of Western Australia. He specializes in subsurface characterization, spatial statistics, and Bayesian inference, with a focus on uncertainty quantification in geological models. His work integrates geophysical data inversion and computational methods to address challenges in hydrogeology and resource exploration. Currently, he co-coordinates Work Package 5 of the Loop project, focusing on uncertainty management in multi-disciplinary modeling frameworks. Education: PhD in Hydrogeology (University of Neuchâtel, 2015), focusing on stochastic heterogeneity modeling of braided river aquifers. Prior experience includes 7 years as an IT consultant in retail before transitioning to academia. Research interests span 3D geological modeling, spatial statistics, Bayesian inversion, and the rigorous treatment of uncertainty across data acquisition to predictive modeling. His contributions include open-source tools like dh2loop and Blockworlds for geological data processing and uncertainty quantification. Scientific awards include the MinEx CRC 2022-2023 Publication Prize and Prix Adrien Guebhard-Severine (2015). He collaborates extensively on projects involving geophysical integration, probabilistic inversion, and multi-disciplinary modeling platforms like Loop. Advising and grants: Co-supervises PhD students within the Loop project framework. Involved in grants such as the ARC-funded Loop 3D Linkage-2 project (2023-2026). His work emphasizes translational research bridging computational methods with practical geological exploration challenges. Labs/Teams: Active contributor to the Loop project team, developing tools for uncertainty-aware geological modeling. Collaborates with interdisciplinary groups in geophysics, hydrology, and computational science.