جستجوی استاد راهنمای دانشگاهی

Marcel Aachمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• High-Performance Computing
• Machine Learning
• +۳ مورد دیگر

Marcel Aach is a researcher at the Jülich Supercomputing Centre (JSC) within Research Centre Jülich, focusing on high-performance computing (HPC) and artificial intelligence (AI) integration. His work spans hyperparameter optimization, quantum-classical hybrid systems, and scalable deep learning architectures. Research Focus: AI-driven CFD simulations, medical imaging analysis, and edge AI optimization Infrastructure: Leverages JSC's HPC systems for large-scale computational tasks Recent publications highlight his contributions to resource-adaptive AI training, turbulent flow prediction using GRU models, and enhancing CT data accuracy for respiratory simulations. His work intersects HPC scalability, machine learning efficiency, and interdisciplinary applications. While no scientific awards are explicitly documented in the provided texts, his research demonstrates technical leadership in merging classical and quantum computing paradigms for AI optimization.

Chinmay Datarمشاهده پروفایل

پژوهشگر

• Scientific Computing
• Deep Learning
• Partial Differential Equations
• +۳ مورد دیگر

Chinmay Datar is a doctoral candidate at the Technical University of Munich (TUM), affiliated with the Institute of Advanced Study (IAS) in the focus group 'Scientific Machine Learning.' He holds an M.Sc. in Computational Engineering from Friedrich-Alexander University (FAU), Erlangen, and a Bachelor's in Mechanical Engineering from the University of Pune. His research focuses on integrating classical scientific computing methods with deep learning to solve Partial Differential Equations (PDEs). Teaching Responsibilities include: Summer Semester 2025: Seminar on High-Dimensional Methods for Scientific Computing and Lab Course on Machine Learning in Crowd Modeling/Simulation Winter Semester 2024/25: Courses on Scientific Computing & Machine Learning and a Seminar on Ethics in Science Research Interests: Novel neural architectures for dynamical systems simulation Backpropagation-free training of neural PDE solvers Domain decomposition combined with neural networks for PDE solutions Student Supervision: Ongoing: Eray Yildiz (Master's) and Ahmet Semiz (Master's) Completed: Rahma Atamert and Aditya Phopale (Master's) Recognition: FAU Graduation Scholarship (Japan internship support). Labs/Teams: Part of the Chair of Scientific Computing in Computer Science (SCCS), led by Prof. Hans-Joachim Bungartz.

• Building Energy Systems
• Machine Learning Applications
• Model Predictive Control
• +۳ مورد دیگر

Martin Rätz is a researcher and team leader at the Institute for Energy Efficient Buildings and Indoor Climate of RWTH Aachen University , where he leads the Building Automation research team since January 2025. His work focuses on integrating advanced technologies like machine learning, IoT, and cloud computing into building automation systems to enhance energy efficiency and operational transparency. M.Sc. in Mechanical Engineering (Energy Engineering specialization), RWTH Aachen University Erasmus studies at Instituto Superior Técnico in Lisbon His research interests include: Model predictive control for building systems Energy-efficient control algorithms Machine learning applications in energy systems IoT integration for smart buildings Cloud computing in building automation Demand-controlled ventilation systems His recent publications demonstrate expertise in: Machine learning model validation Fault detection in district heating networks Energy weather data generation tools AI-driven building system optimization Dynamic ventilation strategies for healthcare settings Scientific honors include: Springorum Denkmünze for academic excellence (2018) Dean's List recognition (2018, 2019) As team leader, he directs projects such as: Monitoring 120 buildings in the OM4ABDO initiative Developing the BUDO standard for data point naming Advancing cloud-based building automation frameworks

Sebastian Schmidlمشاهده پروفایل

پژوهشگر

• Distributed Computing
• Data Profiling
• Time Series Anomaly Detection
• +۳ مورد دیگر

Sebastian Schmidl is a Ph.D. researcher at the Information Systems Group (University of Potsdam, Digital Engineering Faculty ). He is also affiliated with the Big Data Analytics Group at Philipps-Universität Marburg . Research Interests : Distributed computing, time series anomaly detection, data profiling, and scalable reactive systems using actor programming. Academic Roles : Supervised multiple master's and bachelor's theses, delivered guest lectures on reproducibility and order dependencies, and contributed to academic reviewing for conferences like SIGMOD and journals like SoftwareX. His recent publications focus on time series anomaly detection , data profiling query languages , and distributed algorithm design . He has collaborated extensively on benchmarking frameworks and optimization techniques for univariate and multivariate time series data.

Florian M. Wagnerمشاهده پروفایل

استاد

• Multi-method geophysical imaging
• Geophysical monitoring of subsurface fluid migration
• Optimized experimental design
• +۴ مورد دیگر

Florian M. Wagner is a full Professor of Geophysical Imaging and Monitoring at RWTH Aachen University since February 2023. His work bridges geophysical imaging, numerical modeling, and data integration for subsurface characterization. Key affiliations include Lawrence Berkeley National Laboratory (visiting scholar 2018), University of Bonn (postdoc 2016–2019), and GFZ German Research Centre for Geosciences (research associate 2011–2016). Dr. Wagner holds a Doctor of Sciences (Dr. sc.) from ETH Zurich (2012–2016) and a joint M.Sc. in Applied Geophysics from TU Delft, ETH Zurich, and RWTH Aachen (2009–2011). His research focuses on multi-method geophysical imaging , optimized experimental design , and monitoring subsurface fluid dynamics in contexts like permafrost degradation, geothermal systems, and radioactive waste repositories. He develops open-source software such as pyGIMLi for geophysical inversions. Recent publications highlight hybrid joint inversion frameworks combining transient electromagnetics, ERT, and seismic data, along with entropy-constrained inversion techniques for sharper subsurface imaging. Scientific awards include multiple best teaching awards at RWTH Aachen and research grants for innovative geophysical projects. Students mentored include Nino Menzel and Anton Ziegon, whose M.Sc. theses directly contributed to peer-reviewed publications. Public outreach projects like the Open AR-Sandbox and seismic wave exhibits engage both academic and general audiences. His work emphasizes uncertainty quantification in geophysical models and cross-disciplinary collaboration to address challenges in resource management and climate change impacts.

• Optimization Algorithms
• Machine Learning
• Image Processing
• +۳ مورد دیگر

Abdelhameed Ibrahim is an active academic researcher with a focus on optimization algorithms, machine learning, and their applications in healthcare, environmental science, and engineering. His work spans over 15 years, with contributions to nature-inspired metaheuristics, image processing, and predictive modeling. He frequently collaborates with institutions on projects involving cancer detection, energy forecasting, and medical diagnostics. Key areas: Optimization algorithms, medical imaging, renewable energy prediction, and machine learning frameworks. Notable collaborations include work on Greylag Goose Optimization and Al-Biruni Earth Radius algorithms. Research interests emphasize interdisciplinary applications, such as solar energy harnessing, rainfall forecasting, and electric vehicle recommendation systems. His methodologies often integrate advanced computational techniques with real-world problem-solving.

• Artificial Intelligence
• Machine Learning
• Edge Computing
• +۷ مورد دیگر

Huan Liu is a Professor at Arizona State University's School of Computing, Informatics, and Decision Systems Engineering, with research spanning artificial intelligence, edge computing, and interdisciplinary applications. His work bridges theoretical advances with real-world implementations across healthcare, transportation, and environmental systems. His primary research interests include: Artificial Intelligence and Machine Learning for complex system optimization Edge-Cloud Computing architectures for IoT and vehicular networks Medical image analysis and clinical decision support systems Graph neural networks and multimodal learning Privacy-preserving federated learning frameworks Recent publications demonstrate strong emphasis on applied AI, with 15+ 2025 papers addressing UAV swarm intelligence, medical diagnostics, and sustainable transportation through novel algorithmic approaches. Publication trends reveal consistent leadership in: Developing efficient edge computing solutions for dynamic environments Creating medical AI tools with clinical validation Innovating in graph-based and multimodal learning Applying optimization techniques to logistics and environmental challenges His work frequently appears in top venues including IEEE Transactions, Bioinformatics, and AAAI.

• Computer Vision
• Imaging
• Optimization Algorithms
• +۳ مورد دیگر

Michael Möller is a Professor for Computer Vision at the University of Siegen, Germany. His research focuses on integrating model-based and learning-based techniques in imaging and vision, with an emphasis on efficient optimization algorithms for solving high-dimensional minimization problems. Research Interests: Combining classical model-based methods with deep learning Optimization algorithms for inverse problems Applications in CT reconstruction, image segmentation, and quantum computing Development of energy-dissipating neural networks and non-smooth regularization techniques Scientific Awards: Best Paper Award at ISWC 2021 Best Paper Honorable Mention at GCPR 2020 Best Paper Award at VMV 2015 Collaborations and Grants: Active collaborations with institutions like IEEE, Springer, and universities across Europe. Publications in top-tier venues such as NeurIPS, CVPR, ICCV, and ICLR. His work spans theoretical frameworks (e.g., convex relaxations, spectral methods) and practical applications (e.g., medical imaging, THz defect detection).

Jovita Lukasikمشاهده پروفایل

پژوهشگر ارشد

• Neural Architecture Search
• Model Robustness
• Deep Learning
• +۳ مورد دیگر

Jovita Lukasik is a Research Fellow in the Institute for Vision and Graphics at the University of Siegen. Her research focuses on neural architecture search (NAS), model robustness, and efficient deep learning methods, with applications in computer vision and performance prediction. Key Research Areas: Developing zero-cost proxies for efficient neural architecture evaluation Improving CNN robustness via frequency-based regularization Creating benchmark datasets for architecture design and robustness analysis Exploring generative approaches for efficient NAS Her work has been published in TMLR, GCPR, ECCV, and ICLR. She contributed to the organization of the NAS workshop at ICLR 2021 and co-leads the AutoML seminar series. Recent work investigates transferable surrogates for expressive search spaces and texture/shape biases in vision-language models.

Daniel Schlörمشاهده پروفایل

پژوهشگر

• Machine Learning
• Anomaly Detection
• Natural Language Processing
• +۵ مورد دیگر

Dr. Daniel Schlör is a researcher at the Chair of Data Science (Informatics X) at the University of Würzburg, with additional affiliations to the CLiGS (Computational Literary Genre Stylistics) research group in Digital Humanities. His work focuses on machine learning for cybersecurity, fraud detection, and explainable AI, with recent projects exploring synthetic data generation, knowledge graph integration, and deep learning for imbalanced datasets. Research interests include Explainable AI (XAI) for anomaly detection Deep learning architectures for domain-specific relationships Multi-agent simulations for fraud scenario modeling Computational stylistics in digital humanities Article trends show expertise in Developing novel neural units (e.g., ModeConv) for structural anomaly differentiation Advancing XAI methods with generative inpainting techniques Creating open ERP datasets for occupational fraud research Applying graph neural networks to water distribution leakage detection Labs & collaborations include the Data Science Chair’s AI Institute at Hubland Nord campus and CLiGS research group for computational literary analysis.

• Relational Machine Learning
• Graph Neural Networks
• Complex Networks
• +۴ مورد دیگر

Dr. Rebekka Burkholz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. Her work bridges relational machine learning with biological applications , focusing on theoretical improvements to deep learning through complex network analysis. Education: Studied Mathematics and Physics at TU Darmstadt, followed by a PhD at ETH Risk Center under Frank Schweitzer and Hans J. Herrmann. Postdoctoral Experience: Worked at Harvard's Biostatistics Department (2019-2021) and ETH Zurich's Machine Learning Institute (2017-2019). Her research explores graph neural networks , sparse training , and biologically informed models , aiming to enhance algorithm robustness and interpretability. Recent publications analyze: 2025 : Implicit sparsification, hyperbolic regularization, and network balance techniques. 2024 : Spectral pruning, gene regulatory modeling, and dynamic rescaling for GNNs. Scientific recognition includes: Zurich Dissertation Prize for systemic risk research (2016). CSF Best Contribution Award for maize trade analysis (2016). She collaborates with institutions like Harvard T.H. Chan School of Public Health and ETH Zurich, focusing on trustworthy information processing and empirical security at CISPA.

Marius Lindauerمشاهده پروفایل

استاد

• AutoML
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Marius Lindauer is a Professor of Machine Learning at the Department of Artificial Intelligence , Leibniz University Hannover , and Deputy Head of the Institute since 2025. Previously, he served as Spokesperson of Computer Science Professors (2023-2025) and Head of the Institute (2022-2024). PhD (Dr. rer. nat, 2010-2015), Master (2008-2010), and Bachelor (2005-2008) in Computer Science from University of Potsdam His research focuses on democratizing AI through AutoML innovations, including: Green AutoML for sustainable deep learning Human-Centered AutoML for user-centric optimization Dynamic Algorithm Configuration in reinforcement learning Generalization techniques for production and health applications Recent publications show strong multi-objective optimization trends across medical imaging , protein design , and time series forecasting , with 15+ papers in 2024-2025 at venues like NeurIPS, AAAI, and IEEE TPAMI. Key scientific awards : ERC Starting Grant (2022), NeurIPS BBO-Challenge winner (2020), multiple AutoML/ML competition victories Advisory role in 140+ publications and leadership of LUHAI Institute

Fabian David Schmidtمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Machine Learning
• Multilingual Representation Learning
• +۷ مورد دیگر

Fabian David Schmidt is a Research Associate and Doctoral Student at the CAIDAS Chair for NLP at Julius-Maximilians-Universität Würzburg. He works on multilingual representation learning and sample-efficient cross-lingual transfer, co-advised by Prof. Dr. Goran Glavaš (University of Würzburg) and Ivan Vulić (University of Cambridge). Research Interests: His work focuses on cross-lingual transfer methods, low-resource NLP, and robust knowledge editing in LLMs. He also explores vision-language benchmarks, process mining, and semantic encoders for information retrieval. Key areas include Robust Cross-Lingual Transfer Sample-Efficient Training Vision-Language Integration LLM Evaluation Publication Trends: Fabian's recent publications emphasize multilingual and cross-lingual NLP advancements, including sliced fine-tuning for NER, model averaging for robustness, and domain adaptation. His 2025 work extends into vision-language tasks and LLM generalization across cultures. He also contributes to spoken language understanding benchmarks. Labs & Teams: Affiliated with the WüNLP group and the CAIDAS Chair at the University of Würzburg, collaborating with international researchers on cross-lingual NLP and LLM optimization.

• Machine Learning
• Deep Learning
• Cognitive AI
• +۱۱ مورد دیگر

Volker Tresp is a Professor at the Ludwig-Maximilians-Universität München (LMU) and a leading researcher in machine learning for relational structured domains . His work bridges cognitive AI , knowledge graphs , and quantum computing . He is a PI in the Munich Center for Machine Learning (MCML) and co-director of the ELLIS program on Semantic, Symbolic, and Interpretable Machine Learning . His research interests focus on temporal knowledge graphs , foundation models , multimodal learning , and quantum machine learning . Recent projects include WebPilot (multi-agent web task execution) and FedBiP (federated learning with diffusion models). His work on PyKEEN and RESCAL has advanced knowledge graph embeddings . Volker Tresp's scientific contributions are recognized through ELLIS Fellowship (2020) , Siemens Inventor of the Year (1996) , and Best Paper Awards at ISWC 2021 and IEEE ICHI 2020 . His students have published extensively at top AI venues like AAAI , CVPR , and ECCV . Awards and Honors: ELLIS Fellow (2020) Siemens Inventor of the Year (1996) Best Paper Award, ISWC 2021 Student Best Paper Award, ISWC 2017 Best Paper Runner-up, PKDD 2005

• Graph Neural Networks
• Combinatorial Optimization
• Federated Learning
• +۳ مورد دیگر

Shiqing Liu is a researcher at the University of Bielefeld, affiliated with the Faculty of Engineering and the Cognitronics & Sensor Technology Group within the Center for Cognitive Interaction Technology (CITEC). Based at office CITEC 3-204, Liu contributes to the university's research in the Socio-Technical World domain, focusing on technologies that enable agents to act and communicate in complex environments. Dr. Liu's research spans several cutting-edge areas in artificial intelligence and optimization. Their work primarily focuses on graph neural networks, combinatorial optimization, and federated learning systems. They have made significant contributions to applying machine learning techniques to solve complex optimization problems including vehicle routing, facility location, and neural architecture search. Their research bridges theoretical computer science with practical applications in distributed systems and privacy-preserving technologies. An analysis of Dr. Liu's recent publications reveals a strong trajectory in developing unified frameworks that combine graph-based learning with combinatorial optimization. Their work increasingly addresses challenges in federated settings where data privacy and distribution heterogeneity present significant obstacles. The research demonstrates a progression from single-objective optimization problems toward more complex multi-objective scenarios, with growing emphasis on practical implementation constraints and real-world applicability. Dr. Liu is actively involved in the Cognitronics & Sensor Technology research group at CITEC, which is part of Bielefeld University's strategic focus on the Socio-Technical World. This center investigates how humans, robots, and AI systems can effectively interact and collaborate in complex environments, aligning with the university's broader mission of "Transcending Boundaries" between disciplines and between science and society.