جست‌وجوی استاد و پژوهشگر

Jana-Rebecca Rehseمشاهده پروفایل

استادیار

• Business Process Management
• Process Mining
• User Behavior Mining
• +۵ مورد دیگر

Prof. Dr. Jana-Rebecca Rehse serves as Assistant Professor for Management Analytics at the University of Mannheim Business School, where she leads the Chair of Management Analytics within the Information Systems department. Her academic work bridges theoretical research with practical business applications, focusing on data-driven approaches to business process optimization. Her primary research interests encompass User Behavior Mining , Process Mining , and AI applications in business process management . Rehse investigates how organizations can leverage process mining techniques to extract meaningful insights from event logs, with particular attention to conformance checking, process resilience assessment, and the practical implementation challenges businesses face when adopting these technologies. Her work frequently addresses the intersection of human behavior and process execution, examining how user interactions with IT systems can be analyzed to improve process design and user experience. Analysis of her recent publications reveals a clear research trajectory toward increasingly sophisticated integration of artificial intelligence with traditional process mining techniques. Starting with foundational work on reference model mining and process discovery methodology, her research has evolved to address cutting-edge applications of generative AI, explainable AI, and predictive analytics in business process contexts. The majority of her work appears in top-tier information systems and business process management journals including Information Systems, Process Science, and ACM Transactions publications, demonstrating her significant contributions to the field. Professor Rehse actively collaborates with industry partners including Siemens and MEHRWERK, offering thesis opportunities and research projects that address real-world business challenges. Her current call for applications includes work-study programs at Siemens and master thesis topics focused on conformance checking in cooperation with MEHRWERK. She has recently introduced innovative thesis topics exploring the use of Generative AI for Emotion Identification, reflecting her forward-looking research agenda that anticipates emerging technological trends and their business implications.

Rishabh Dabralمشاهده پروفایل

پژوهشگر

• 3D Computer Vision
• Computer Graphics
• Multi-Modal Interactions
• +۶ مورد دیگر

Rishabh Dabral is a Research Group Leader at the Max Planck Institute for Informatics since August 2024, leading the "3D Visual Intelligence" group. He is also affiliated with the Research Training Group on Neuro-Explicit Models of Language, Vision, and Action at Saarland University. Expertise: 3D computer vision, computer graphics, human-object interaction modeling, and motion synthesis. Leadership: Conducts cutting-edge research on 3D human performance capture and physical plausibility in motion. His research focuses on: 3D human pose estimation under gravity constraints Multi-modal gesture synthesis using neural architectures Quantum auto-encoding for 3D representations Wearable robotics informed by human behavior Temporal dynamics in human-object interaction Recent publications at top venues like SIGGRAPH , CVPR , and ICCV demonstrate his work on: Music-driven motion synthesis Egocentric motion capture systems Reactive two-person interaction models Diffusion-based gesture generation Object-aware motion prediction Wearable robotic limb design

Mitra Baratchiمشاهده پروفایل

دانشیار

• Spatio-Temporal Data Analysis
• Machine Learning
• Mobility Data Modeling
• +۵ مورد دیگر

Mitra Baratchi is an Associate Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University. She leads the Spatio-temporal data Analysis and Reasoning (STAR) research group, co-leads the Automated Design of Algorithms (ADA) group, and founded the Special Interest Group on Spatio-Temporal Data Mining (SIG-SDTM) . PhD from University of Twente (Mobility Data) Master’s/Bachelor’s in Computer Engineering, Iran Research Interests focus on automated pattern extraction from spatio-temporal data across urban, environmental, and industrial domains. Key applications include: Automated Machine Learning (AutoML) for Earth Observations Time-Series Forecasting for public health (e.g., pandemic modeling) Urban Mobility Optimization with ESA, Honda, and municipalities Reliable Vehicular Communication Systems Smart Garments for Health Risk Detection Geocast Protocols for Internet-wide Communication Grant Highlights include €120K NWO-Aspasia, €2.9M Marie Skłodowska-Curie, €350K NWO-KLEIN, and €135K Center for BOLD Cities funding. She has supervised 12 PhD students and 4 current Master’s students since 2011, with notable best paper award at WWIC'16. Teaching includes Machine Learning (2020-present) and Urban Computing (2018-present) at Leiden, plus past courses in Data Visualization, Software Engineering, and Research Methods.

• Digital Archiving
• Conventional Archiving
• Digital Preservation
• +۳ مورد دیگر

Christian Keitel is an Honorary Professor for Archival Science and Digital Archiving at the University of Applied Sciences Potsdam. He works at the Landesarchiv Baden-Württemberg, where he has served since 2000, currently as Deputy Head of Department 2 (Specialized Programs and Educational Work). His research focuses on conceptual principles of conventional and digital archiving, DIMAG systems, certification of digital archives, authenticity of digital records, and cooperative learning processes in digital archiving. University of Applied Sciences Potsdam – Honorary Professor (2015–) Landesarchiv Baden-Württemberg – Deputy Head of Department (2011–), Senior Archivist (2000–) Keitel’s scholarly work emphasizes digital preservation, metadata standards, and long-term archive certification. His publications address challenges in trustworthiness of digital repositories, significant properties of electronic records, and collaborative digital archiving frameworks. He co-authored DIN standards 31644 and 31645 for trustworthy digital archives. Recent articles highlight his contributions to digital ingest workflows, metadata extraction, and municipal archive solutions. His work explores intersections between traditional archival principles and modern digital challenges, including authenticity verification, system development, and cross-institutional cooperation.

Muhammad Bilal Zafarمشاهده پروفایل

استاد

• Human-Centric AI/ML
• Fairness in Machine Learning
• Explainable Artificial Intelligence
• +۳ مورد دیگر

Professor Muhammad Bilal Zafar holds a faculty position at Ruhr University Bochum (RUB), where he heads the Chair of AI and Society within the Research Center for Trustworthy Data Science and Security. He is also a Principal Investigator (PI) at the Cluster of Excellence CASA and a member of the Horst Görtz Institute for IT Security. Previously, he worked as a Senior Applied Scientist at Amazon Web Services and a Research Scientist at Bosch Center for Artificial Intelligence. He completed his PhD at the Max Planck Institute for Software Systems (MPI-SWS) and Saarland University, advised by Krishna P. Gummadi and Manuel Gomez Rodriguez. His research focuses on human-centric AI/ML, emphasizing fairness, explainability, and robustness of AI systems. Key contributions include mechanisms for fair classification, bias detection in algorithms, and trustworthy AI applications. He actively engages in mentoring PhD and Master’s students, with current openings for candidates in Computer Science, Mathematics, and related fields. His work bridges theoretical advancements with practical solutions for ethical AI deployment. Notable collaborations include projects with AWS and Bosch, addressing challenges in algorithmic fairness and AI transparency. His publications span top venues like ACL, KDD, NeurIPS, and WWW, highlighting contributions to both foundational and applied aspects of AI ethics.

• Blockchain Technology
• Business Process Management
• Process Mining
• +۳ مورد دیگر

Ingo Weber is a Professor affiliated with Technische Universität München (TU Munich) and Fraunhofer Gesellschaft. His research focuses on blockchain technology, business process management (BPM), and artificial intelligence (AI), with a particular emphasis on integrating these fields. He has held former positions at TU Berlin, CSIRO Data61, and other institutions. Current affiliations: TU Munich and Fraunhofer Gesellschaft Former affiliations: TU Berlin, CSIRO Sydney, University of New South Wales, SAP Research, and University of Massachusetts Amherst Research interests include blockchain applications in business processes, process mining, AI-driven systems, and sustainability-oriented process analysis. His work explores topics such as blockchain scalability, data confidentiality, and cost-efficient process execution on next-generation blockchains like Algorand. He has pioneered frameworks like SOPA for sustainability analysis and FhGenie for confidentiality-preserving AI. Key contributions include over 200 publications in journals like IEEE Access, Future Generation Computer Systems, and ACM Transactions on Management Information Systems. Recent work emphasizes AI-augmented BPM systems and the application of large language models (LLMs) in scientific contexts. Notable projects include blockchain-based process execution engines (e.g., Caterpillar), platform architectures for multi-tenant blockchain systems, and frameworks for evaluating payment channel networks. He has collaborated extensively with industry partners and academic institutions globally.

Gyunam Parkمشاهده پروفایل

پژوهشگر

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

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

Markus Stockerمشاهده پروفایل

پژوهشگر

• Knowledge Synthesis
• Data Science
• Artificial Intelligence
• +۴ مورد دیگر

Markus Stocker is a researcher leading the Knowledge Infrastructures Lab at TIB - Leibniz Information Centre for Science and Technology. He holds a PhD in Environmental Informatics from the University of Eastern Finland, an MSc in Environmental Sciences from the same university, and an MSc in Computer Science from the University of Zurich. His work focuses on research infrastructures, knowledge synthesis, and FAIR data principles, with a strong emphasis on environmental and earth sciences. He collaborates with major European infrastructures like ACTRIS, ICOS, and NEON. His research interests include neurosymbolic systems, digital scholarship, and the integration of research data across disciplines. Prior roles include a postdoc at PANGAEA (University of Bremen) and positions at Hewlett Packard Labs and Clark & Parsia. He actively contributes to the Research Data Alliance, co-chairing the WG Persistent Identification of Instruments. Markus has pioneered projects like the Open Research Knowledge Graph (ORKG) and the sciqa benchmark for scientific question answering. His work emphasizes interoperability, automation, and community-driven knowledge curation, addressing challenges in data management and scholarly communication.

Bo Yangمشاهده پروفایل

استادیار

• Machine Learning
• Computer Vision
• Robotics
• +۶ مورد دیگر

Bo Yang is an Assistant Professor in the Department of Computing at The Hong Kong Polytechnic University , leading the Visual Learning and Reasoning (vLAR) Group . His work focuses on machine learning, computer vision, and robotics for 3D scene understanding. D.Phil (University of Oxford, 2020) M.Phil (The University of Hong Kong, 2016) B.Eng (Beijing University of Posts and Telecommunications, 2014) Research spans: 3D Vision : Point cloud understanding, semantic segmentation, neural rendering Machine Learning : Unsupervised/disentangled representation learning Robotics : Scene interaction, autonomous navigation Recent publications analyze dynamic 3D scenes (NeurIPS 2023), infinite scene representations (ICML 2024), and unsupervised object segmentation (NeurIPS 2022). Key contributions include RandLA-Net (CVPR 2020), SpinNet (CVPR 2021), and GRF (ICCV 2021). Current teaching includes: AI and Big Data Computing (Spring 2024-2025) Machine Learning and Data Analytics (Fall 2023-2025) Creative Digital Media Design (Spring 2023-2025)

• Explainable Artificial Intelligence
• Type-2 Fuzzy Logic Systems
• Computational Intelligence
• +۱۲ مورد دیگر

Hani Hagras is a Professor in the School of Computer Science and Electronic Engineering at the University of Essex, UK , where he leads pioneering research in type-2 fuzzy logic systems and explainable artificial intelligence (XAI) . His work bridges theoretical advances with real-world applications in smart infrastructure, healthcare, telecommunications, and ambient intelligence. His research interests include explainable AI, type-2 fuzzy logic, computational intelligence, machine learning, human-computer interaction, and intelligent systems. He investigates how fuzzy systems can enhance the transparency, robustness, and adaptability of AI models, particularly in uncertain and dynamic environments. His recent work explores applications in genomics, predictive maintenance, and smart grid optimization. The publication trends show a strong focus on integrating fuzzy logic with deep learning and optimization techniques to create interpretable models. His work frequently appears in top-tier journals such as IEEE Transactions on Fuzzy Systems , IEEE Transactions on Artificial Intelligence , and Neurocomputing . He emphasizes real-world applicability, often collaborating with industry partners like BT. ORCID: 0000-0002-2818-5292 He has advised numerous PhD and Master’s students, including Mehrin Kiani, Ashish Bhatia, and Hugo Leon-Garza, many of whose research projects focus on workforce optimization, healthcare AI, and intelligent systems. His research has been supported by major grants, particularly in smart environments and telecommunications. Hani Hagras is actively involved in leading research labs and teams at the University of Essex, including work on augmented reality for field service support, georeferenced data visualization , and ambient-assisted living systems. His team develops fuzzy logic frameworks for real-time decision-making in complex environments.

Timo Gerkmannمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Speech and Audio Processing
• +۳ مورد دیگر

Prof. Timo Gerkmann is a Professor at the University of Hamburg's Department of Informatics, leading the Signal Processing Research Group. His research focuses on statistical signal processing and machine learning for speech and audio applications, including communication devices, hearing aids, audiovisual media, and human-machine interfaces. He previously held roles at Technicolor Research & Innovation, KTH Royal Institute of Technology, and Siemens Corporate Research. His work emphasizes generative models, diffusion-based approaches, and acoustic signal enhancement. He currently serves as Senior Area Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing. Research Interests: Statistical Signal Processing for Speech and Audio Machine Learning Applications in Acoustic Environments Diffusion Models for Audio Restoration Audio-Visual Speech Enhancement Human-Machine Interaction Systems Acoustic Scene Analysis Publications Highlight Trends: Recent works focus on diffusion models for speech enhancement, generative approaches to dereverberation, and audiovisual multimodal analysis. He has pioneered frameworks like ReverbFX datasets and FlowDec codecs, emphasizing perceptual quality and unsupervised domain adaptation. Advising & Grants: While no specific students or grants are listed, his research group actively publishes in top venues, indicating sustained academic contributions. His work bridges theoretical signal processing with applied systems engineering. Labs/Teams: Leads the Signal Processing (SP) Research Group at UHH, specializing in cutting-edge audio technologies and human-centric signal processing solutions.

Lutz Ecksteinمشاهده پروفایل

استاد

• Automotive Engineering
• Automated Driving Systems
• Vehicle Dynamics
• +۴ مورد دیگر

Univ.-Prof. Dr.-Ing. Lutz Eckstein is the Head of the Institute of Automotive Engineering (ika) at RWTH Aachen University and a leading expert in automotive engineering. He concurrently serves as President of the VDI Association of German Engineers, President of the VDI Scientific Advisory Board for the Federal Ministry of Digital and Transport (BMDV), and Scientific Director of the NRW Competence Network for Automated and Connected Mobility. His professional background includes senior roles at BMW and DaimlerChrysler, focusing on vehicle safety, driver assistance systems, and ergonomics. Eckstein holds a Doctorate from the University of Stuttgart (2000) and received the Artur Fischer Prize (1995) for academic excellence. His research interests span automated driving systems, human-machine interfaces, scenario-based testing, and mobility infrastructure. He leads high-profile projects like UNICARagil and coordinates the Mobility and Transport profile at RWTH Aachen. Over 80 patents and publications reflect his contributions to automotive innovation, including advancements in sensor fusion, digital twins, and cooperative intelligent transport systems (C-ITS). His recent work emphasizes safety assurance for automated vehicles, scenario databases, and scalable simulation frameworks. Eckstein's interdisciplinary approach bridges academia and industry, driving transformative solutions for future mobility challenges.

• Computer Vision
• Machine Learning
• Pattern Recognition
• +۶ مورد دیگر

Xiangyang Xue is a Professor at Fudan University in Shanghai, China, with an extensive research portfolio spanning computer vision, machine learning, and artificial intelligence. His work demonstrates significant contributions to object-centric representation learning, 3D reconstruction, person re-identification, and semantic segmentation. With over two decades of publication history from 1999 to present, he maintains an active research program with numerous collaborations, particularly with researchers like Yanwei Fu, Bin Li, and Yu-Gang Jiang. Professor Xue's research interests focus on advancing computer vision through innovative approaches to object-centric representation learning, 3D scene understanding, and multi-modal learning. His recent work explores the integration of large vision-language models with 3D understanding, diffusion models for data synthesis, and brain-inspired approaches to robotic scene understanding. His research bridges theoretical advances with practical applications in robotics, autonomous systems, and security. Analysis of his recent publications (2023-2026) reveals a strong trend toward multi-modal learning, with increasing integration of vision-language models, 3D understanding, and diffusion-based generation techniques. His work shows a progression from traditional computer vision problems toward more complex, embodied AI challenges that require understanding of both visual scenes and their semantic interpretations. Key themes include object-centric representations, cross-modal alignment, and the application of these techniques to robotics and security domains. Professor Xue has mentored numerous researchers through collaborative projects, with extensive co-authorship indicating a strong advising presence. His work spans multiple funding areas including NSF-supported research in computer vision, AI security, and robotics applications. His publications appear consistently in top venues including CVPR, ICCV, ECCV, AAAI, and IEEE TPAMI. His research group appears to focus on computer vision and machine learning, with particular emphasis on object-centric scene understanding, 3D reconstruction, and person re-identification systems. The team works at the intersection of theoretical computer vision and practical applications, with projects spanning autonomous driving, robotics, security systems, and human-computer interaction. Recent work suggests active exploration of large vision-language models and their integration with 3D scene understanding.

Kristian Kerstingمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning
• Statistical Relational Artificial Intelligence
• +۵ مورد دیگر

Kristian Kersting is an Associate Professor in the Computer Science Department at Technical University of Darmstadt, Germany, having moved from TU Dortmund in 2017. He holds additional affiliations including Adjunct Assistant Professor roles and has contributed to institutions like the University of Bonn and Wake Forest University. His research focuses on data mining, machine learning, and statistical relational AI, with applications in medicine, plant phenotyping, and traffic analysis. He has authored over 150 peer-reviewed publications and received numerous awards, including the ECCAI Dissertation Award 2006 and Best Student Paper Award at ECML 2006. Education includes a Ph.D. from the University of Freiburg and postdoctoral research at MIT. He has held roles such as Assistant Professor at the University of Bonn and co-chaired major conferences like ECML PKDD 2013. His work emphasizes interdisciplinary applications, including collaborations with industries like goedle.io and pflegix.de. Scientific contributions span probabilistic logic learning, lifted inference, and neuro-symbolic systems. His research group's activities include projects on plant disease detection, traffic prediction, and collective attention analysis. Key awards highlight his impact in AI and data science.

Ahmet Cürebalمشاهده پروفایل

پژوهشگر

• Operations Research
• Linear Programming
• Mixed-Integer Programming
• +۵ مورد دیگر

Ahmet Cürebal is an External Doctoral Researcher at the Institute of Information Systems, part of the Business School at the University of Hamburg. He holds B.S. and M.S. degrees in Industrial Engineering from Kirikkale University, Turkey, and is currently pursuing his Ph.D. His research focuses on Operations Research with specializations in Linear Programming (LP), Mixed-Integer Programming (MIP), Combinatorial Optimization, Scheduling, Assignment, and Routing problems within Supply Chain contexts. Key research interests include solving real-world optimization challenges such as staff scheduling in retail sectors, driver workload balancing in logistics firms, and pandemic-era security personnel allocation. His work often involves applying metaheuristic methods like Variable Neighborhood Search (VNS) and Fixed Set Search (FSS) to complex operational problems. Notable publications explore applications such as straddle carrier routing in container terminals, bus route optimization for high-speed rail integration, and competency-based scheduling during crises. His methodologies frequently integrate Goal Programming and Analytical Network Process (ANP) for multi-criteria decision-making scenarios. Cürebal’s research demonstrates a strong emphasis on practical implementations across diverse industries, including healthcare, logistics, and retail, with a focus on balancing efficiency and real-world constraints through advanced mathematical modeling and algorithmic approaches.