جستجوی استاد راهنمای دانشگاهی

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

• Modeling and Simulation
• Energy System Analysis
• Vehicle Grid Integration
• +۳ مورد دیگر

Prof. Dr.-Ing. Marco Pruckner leads the Chair of Communication Networks at the University of Würzburg since 2022, following his assistant professorship in Energy Informatics at Friedrich-Alexander-University Erlangen-Nürnberg (2016–2022). His research focuses on energy system analysis, vehicle grid integration, and future mobility systems using modeling and simulation methodologies. University of Würzburg (2022–present) Friedrich-Alexander-University Erlangen-Nürnberg (2016–2022) UC Berkeley (Visiting Scholar, 2020) Key research areas: Modeling and simulation of integrated energy systems Digital twin development for local energy systems Optimization of electric vehicle charging strategies Water-energy nexus analysis and sustainability metrics Recent publications (2022–2025) demonstrate expertise in: Smart charging algorithms for vehicle grid integration Heat pump electrification and seasonal performance modeling Battery state-of-health estimation techniques Multi-objective optimization for data centers and energy systems His team at University of Würzburg includes Daniel Bayer, Paul Benz, Jonas Schiller, and Leo Strobel. Methodologies combine system dynamics, discrete-event simulation, and machine learning for energy and mobility system analysis.

• Data Science
• Machine Learning
• Automated Machine Learning
• +۲ مورد دیگر

Eyke Hüllermeier is an Associate Professor at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, leading the Data Science and Its Applications research unit. His work bridges academic and industrial AI development with a focus on security-critical applications. His research centers on Machine Learning security , particularly vulnerabilities in Automated Machine Learning (AutoML) systems. Key areas include adversarial attacks on model selection pipelines, robustness verification, and explainability frameworks to prevent "misguided" AutoML practices. This work addresses critical gaps in trustworthy AI deployment where automated systems may inadvertently compromise model integrity. Analysis of his recent publications reveals a sharp focus on emerging threats in industrial AI adoption, with the 2025 ICML paper establishing foundational security principles for AutoML toolchains. This positions his research at the intersection of theoretical machine learning and real-world system reliability. Scientific Awards: No awards documented in source material Hüllermeier actively collaborates with industry partners through DFKI's applied research model, though specific grant details or student supervision are not disclosed in the provided text. His team specializes in building security-aware data science pipelines within DFKI's Kaiserslautern facility.

Martin Middendorfمشاهده پروفایل

استاد

• Swarm Intelligence
• Bioinformatics
• Genome Rearrangement Analysis
• +۷ مورد دیگر

Prof. Dr. Martin Middendorf is a faculty member at the Department of Computer Science , Faculty of Mathematics and Computer Science , Leipzig University , Germany. He leads the Swarm Intelligence and Complex Systems Group and focuses on interdisciplinary research at the intersection of computational methods and biological systems. Fields of Interest Swarm Intelligence Bioinformatics Genome Rearrangement Analysis Combinatorial Optimization Evolutionary Algorithms Task Allocation in Multi-Agent Systems His recent research emphasizes mitochondrial genome annotation , predator-prey dynamics in swarm systems , and metaheuristic algorithms for dynamic optimization . Key trends include de-Bruijn graph applications , pheromone-dependent movement modeling , and automated behavior tracking in social insects . Supervised Students Dr. Nicolas Wieseke Dr. Hoang Thanh Le Dr. Fatma Turna Tobias Jagla Carsten Seemann Prof. Middendorf's group develops tools like DeGeCI 1.1 for mitochondrial gene annotation and explores swarm-controlled emergence in ant clustering systems. They apply swarm intelligence principles to solve real-world problems in vehicle routing , sewer network design , and biomedical signal processing .

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.

• Privacy-preserving machine learning
• Adversarial robustness
• Differential privacy
• +۵ مورد دیگر

Saeed Mahloujifar is a researcher in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. He has established himself as a prominent researcher in machine learning security, privacy, and robustness, with extensive collaborations with leading researchers including Prateek Mittal, Mohammad Mahmoody, and Somesh Jha. His research primarily focuses on security and privacy challenges in machine learning systems. Mahloujifar has made significant contributions to understanding and mitigating membership inference attacks, developing defenses against poisoning and backdoor attacks, advancing differential privacy techniques, and exploring the fundamental limits of robust machine learning. His work bridges theoretical foundations with practical applications, often developing novel algorithms and frameworks that have been widely adopted in the security and privacy communities. The analysis of his recent publications reveals a strong research trajectory centered on privacy-preserving machine learning, with particular emphasis on large language models, differential privacy guarantees, and security vulnerabilities. His work spans theoretical foundations of privacy through practical security mechanisms like watermarking and robustness benchmarking. A notable trend is his increasing focus on large language models and their security implications, alongside continued contributions to foundational privacy theory. Mahloujifar has co-authored over 90 publications since 2017, with a significant acceleration in output since 2020. His work appears consistently in top-tier venues including NeurIPS, ICML, IEEE S&P, and USENIX Security, demonstrating both breadth and depth in his research contributions.

• Natural Language Processing
• Legal Informatics
• Greek Language Processing
• +۴ مورد دیگر

Ion Androutsopoulos is a Professor in the Department of Informatics at Athens University of Economics and Business (AUEB), where he leads the AUEB NLP Group. With over 180 publications spanning three decades, he is a prominent figure in Natural Language Processing research, particularly known for his work bridging NLP with legal informatics, Greek language processing, and biomedical applications. His research interests focus on several interconnected areas of Natural Language Processing: Legal Informatics : developing NLP systems for legal document analysis, legal judgment prediction, and legal reasoning, with recent work including GreekBarBench and Archimedes-AUEB systems Greek Language Technology : creating specialized tools for Modern Greek processing, including GR-NLP-TOOLKIT and Greeklish transliteration systems Biomedical Text Mining : working on diagnostic captioning and medical image analysis through participation in ImageCLEFmedical challenges Financial NLP : developing systems like EDGAR-CRAWLER for financial document analysis and XBRL tagging His recent publications (2023-2025) show an increasing focus on Greek-specific NLP resources and practical applications of large language models in legal reasoning. His work often combines theoretical NLP advances with practical implementations, particularly through his leadership of the AUEB NLP Group which regularly participates in international evaluation campaigns. Professor Androutsopoulos has supervised numerous PhD students who have become active researchers in their own right, including John Pavlopoulos, Prodromos Malakasiotis, and Ilias Chalkidis. His collaborative network spans multiple institutions and disciplines, reflecting the interdisciplinary nature of modern NLP research.

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.

Sebastian Peitzمشاهده پروفایل

استاد

• Multiobjective Optimization
• Optimal Control
• Machine Learning
• +۳ مورد دیگر

Sebastian Peitz is Professor (previously Assistant Professor) at Paderborn University's Department of Computer Science, leading the Data Science for Engineering group. He obtained his PhD in Multiobjective Optimization from Paderborn University and MSc in Mechanical Engineering from RWTH Aachen. His research develops computational methods for multiobjective optimization, optimal control, and machine learning with applications in fluid dynamics, autonomous systems, and industrial processes. He leads the BMBF-funded Multicriteria Machine Learning group. Research trends show consistent focus on Koopman operator theory, reinforcement learning applications in control systems, and physics-informed machine learning across publications. Achievements include the 2019 PRECEDE Best Paper Award and leadership in international optimization conferences.

• 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.

Asma Atamnaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Reinforcement Learning
• Numerical Optimization
• +۴ مورد دیگر

Dr. Asma Atamna is a postdoctoral researcher in the Theory of Machine Learning group led by Prof. Tobias Glasmachers at Ruhr-Universität Bochum’s Institute of Neuroinformatics (INI). Her research bridges numerical optimization and machine learning (ML), focusing on adaptive hyperparameter mechanisms for optimizers like Adam and SGD. She emphasizes real-world applications, contributing to the ecoKI project to aid SMEs in digitalization via ML platforms. Notably, she developed ContainerGym —a reinforcement learning benchmark addressing real-world resource allocation challenges. Her work critiques academic ML benchmarks for lacking real-world complexity, advocating for practical evaluation frameworks. Research interests include reinforcement learning, evolutionary algorithms, convergence analysis, and constrained optimization. She collaborates closely with industrial partners to ensure applied relevance. Affiliated with the INI (part of the Faculty of Computer Science), her work intersects disciplines like neuroscience-inspired AI, robotics, and data science. Contact: asma.atamna@ini.rub.de .

• 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).

Tim Menziesمشاهده پروفایل

استاد

• Search-Based Software Engineering
• Software Analytics
• Software Product Lines
• +۴ مورد دیگر

Tim Menzies is a Full Professor at North Carolina State University with a diverse professional background that includes previous careers as a nurse, rocket scientist, taxi driver, and journalist. His primary research focuses on Search-Based Software Engineering (SBSE), software analytics, software product lines, Mining Software Repositories, and data mining and machine learning applications in software engineering. Dr. Menzies has maintained an active research profile with publications spanning from 2018 through 2026, serving on program committees and organizing tracks at major conferences including ASE, ICSE, and ESEC/FSE. His work consistently bridges theoretical research with practical applications, emphasizing solutions that are both technically sound and implementable in real-world settings. His research interests include: Search-Based Software Engineering (SBSE) Software analytics and data-driven approaches Software product lines and configuration Mining Software Repositories Machine learning applications in software engineering Fairness in software engineering tools Interpretable and explainable AI for software engineering His recent publications (2023-2026) reveal a strong trend toward making complex software analytics more accessible and understandable while maintaining performance. He has pioneered approaches that simplify models without sacrificing effectiveness, directly addressing the 'black box' problem that often hinders adoption of machine learning in practice. Dr. Menzies is known for his critical perspective on software engineering research, challenging assumptions in the field, particularly regarding deep learning applications. His work emphasizes practical, evidence-based approaches over theoretical elegance alone, reflecting his commitment to improving the research-practice connection in software engineering.