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Gaël KermarrecView profile

Researcher

• Boundary Layer Meteorology
• Atmospheric Turbulence
• GNSS Applications
• +3 more

Dr. Gaël Kermarrec is a researcher at the Boundary Layer Meteorology Group , part of the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover . His work focuses on atmospheric turbulence, GNSS applications, and remote sensing for environmental monitoring. Boundary layer meteorology Turbulence theory GNSS signal processing Terrestrial laser scanning Climate change impacts Geodetic time series analysis His research integrates advanced mathematical models like LR B-splines and Matérn covariance with large eddy simulations to study: Atmospheric turbulence effects on optical/GNSS signals Hydrospheric mass loading Deformation analysis of terrain/port infrastructure Climatic sea-level changes Machine learning for remote sensing The 15 most recent articles (2025-2023) demonstrate his focus on: GNSS-based turbulence detection AI-enhanced climate mapping Advanced surface approximation techniques Multi-sensor data fusion Stochastic modeling of geodetic observations Environmental impacts on optical measurements He has developed tools like the Klimascanner QGIS plugin for urban climate resilience and contributes to: Understanding atmospheric scale lengths Improving TLS/GNSS deformation monitoring Analyzing hydrospheric changes Wavefront modeling Ionospheric corrections

Alessandro AliakbargolkarView profile

Professor

• Federated Satellite Systems
• CubeSat Constellations
• Spacecraft Systems Architecture
• +3 more

Alessandro Aliakbargolkar is a Professor at the Department of Space Systems Design under the School of Aerospace Engineering at Skolkovo Institute of Science and Technology (Skoltech). His research focuses on Federated Satellite Systems, CubeSat constellations, and Spacecraft Systems Architecture, with applications in Earth observation, messaging services, and networked satellite systems. He has an extensive publication record in these areas, including work on technology roadmapping and digital twin implementation. Key Research Areas: Satellite federation and resource sharing CubeSat constellation design Network performance optimization Integration of systems engineering models with AI Selected Trends: Recent work explores digital twin technologies for CubeSats, federated satellite network analysis, and large language model applications in spacecraft design. Publications often combine theoretical frameworks (e.g., network theory) with practical implementations (e.g., LoRa-based messaging services). ORCID Profile: 0000-0001-5993-2994

• Computer Science
• Artificial Intelligence
• Machine Learning
• +12 more

Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols

• Machine Learning
• Computer Vision
• Signal Processing
• +6 more

Dongwook Kim is affiliated with the Korea Advanced Institute of Science & Technology (KAIST) as a faculty member in the Department of Business and Technology Management under the College of Business. His research spans multiple domains including machine learning, robotics, signal processing, and biomedical engineering. Key contributions in Computer Vision (CNN-based semantic segmentation, 3D point cloud analysis) Significant work in Hardware Design (energy-efficient processors, neuromorphic computing) Interdisciplinary expertise in Medical Imaging (bone age assessment, retinal biomarkers) and Cybersecurity (attack detection, network analytics) Publications since 2015 demonstrate sustained innovation in AI applications , Signal Processing , and Smart City Governance . His work often integrates theoretical advances with practical implementations in real-world systems. No scientific awards or student mentorship details are explicitly documented in the provided records.

Stephan A. SieberView profile

Professor

• Bioorganic Chemistry
• Antibiotic Development
• Chemical Proteomics
• +5 more

Professor Stephan A. Sieber is a leading researcher in bioorganic chemistry at the Technical University of Munich (TUM), where he holds the Chair of Organic Chemistry II within the TUM School of Natural Sciences. His research program focuses on developing new drugs against multidrug-resistant bacteria through a multi-disciplinary approach that integrates synthetic chemistry, functional proteomics, microbiology, and protein biochemistry. His laboratory has made significant contributions to identifying unprecedented antibacterial targets beyond the scope of current antibiotics and exploiting these for chemical manipulation. Recent work has increasingly incorporated machine learning approaches to accelerate antibiotic discovery, with notable publications on AI-guided pipelines, drug-target interaction prediction, and high-throughput screening optimization. Sieber's research has resulted in the discovery of new active substances, some of which are currently being optimized for medical applications. His group's publications reveal a strong focus on chemical proteome mining, natural product mode of action studies, and novel antibacterial target identification. The lab has published extensively in top journals including Nature Chemistry, Nature Communications, and ACS Central Science. Inhoffen Medal (2024) Max Bergmann Medal (2023) ERC Advanced Grant (2023) Merck Future Insight Prize (2020) Klaus Grohe Prize (2020) ERC Consolidator Grant (2016) Professor Sieber leads an active research group that maintains a strong presence in the scientific community through regular publications, conference presentations, and collaborations. His laboratory website and BlueSky presence (@sieberlab.bsky.social) demonstrate ongoing research activities and engagement with the broader scientific community. He has successfully secured significant research funding including multiple ERC grants that have supported his innovative work in antibiotic discovery.

• Business Process Management
• Process Mining
• Complex Event Processing
• +5 more

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

Kevin BaumView profile

Researcher

• Explainable AI
• AI Ethics
• AI Fairness
• +4 more

Kevin Baum is a computer scientist currently serving as the deputy head of the Neuro-Mechanistic Modelling (NMM) department at the German Research Center for Artificial Intelligence (DFKI) since January 2023, and head of the Centre for European Research in Trusted AI (CERTAIN) at DFKI since December 2023. Based at the Saarland Informatics Campus in Saarbrücken, Germany, he completed his doctorate in philosophy in March 2024, combining technical expertise with philosophical depth. His work bridges computer science with ethics, focusing on making AI systems transparent and accountable to human users. Dr. Baum's research program centers on interdisciplinary questions concerning the explainability and transparency of AI systems. His work spans multiple significant projects including the Explainable Intelligent System (EIS) initiative and project E7 of the Transregional Collaborative Research Centre 248 "Foundations of Perspicuous Software Systems" (CPEC). He has developed frameworks for understanding stakeholder perspectives on explainable AI and investigated how different information types about automated systems affect user perceptions of fairness and justice. His approach consistently combines theoretical foundations with practical implementations across diverse contexts. Analysis of his publication trends reveals a clear trajectory from theoretical foundations in machine ethics toward practical implementations of explainability requirements in real-world contexts. His work demonstrates increasing focus on human oversight effectiveness, fairness monitoring, and ethical considerations across various AI applications. He has made significant contributions to both academic discourse and practical AI development guidelines, with publications spanning computer science, philosophy, psychology, and human-computer interaction venues. Award for Ethics for Nerds lecture series As a research leader, Dr. Baum contributes to shaping AI development practices through his departmental leadership and interdisciplinary collaborations. His current work with CERTAIN focuses on establishing European research standards for trusted AI development and deployment, emphasizing the practical implementation of ethical requirements in AI systems. He maintains active collaborations across multiple institutions and disciplines, reflecting his commitment to bridging technical and philosophical considerations in AI development. At DFKI, he leads research that combines neuroscientific insights with AI development to create more interpretable systems. The NMM department focuses on both theoretical research on explainable AI foundations and practical applications in various domains, with particular attention to how different stakeholders understand and require explanations from AI systems.

Enkelejda KasneciView profile

Professor

• Human-Centered Technologies
• Artificial Intelligence in Education
• Multimodal Interaction
• +4 more

Prof. Dr. Enkelejda Kasneci is a Distinguished Professor at the Technical University of Munich (TUM), leading the Chair of Human-Centered Technologies for Learning. She holds dual affiliations within TUM School of Social Sciences and Technology and TUM School of Computation, Information and Technology. Her research integrates AI, eye-tracking, and immersive technologies to advance educational paradigms. She directs the TUM Center for Educational Technologies and chairs the MSc program 'AI in Society.' Education: PhD in Computer Science from University of Tübingen (2013), M.Sc. from University of Stuttgart (2007). Earlier roles include Assistant Professor and Dean of Studies at University of Tübingen. Research Focus: Human-centered AI applications in education, multimodal interaction design, and privacy-preserving eye-tracking. Her work bridges technology and pedagogy through projects like AI tutor PEER, VR Classroom, and Privacy-Preserving Eye-tracking. Key Projects: Leads EU-funded projects VIVA (€1.125M), DigiProMIN (€163K), and SARA Kids (€244.8K). Active in policy initiatives like Europe’s AI Imperative. Awards: TUM Heinz Maier-Leibnitz Medal (2024), Liesel Beckmann Distinguished Professorship (2022), and Südwestmetall Research Prize (2014). Grants & Advising: Over €5M in secured funding across 12+ projects. Supervises 14+ PhD researchers and mentors postdocs in AI education and HCI. Labs & Teams: IT-Stiftung EdTech Lab houses advanced VR/eye-tracking setups. Research group includes 20+ members spanning AI, HCI, and educational technology.

Jonas SchorlemerView profile

Researcher

• Radar Systems
• AI-Based Signal Processing
• Ground Penetrating Radar
• +3 more

Jonas Schorlemer is a Researcher at the Department of High Frequency Systems within the Faculty of Electrical Engineering and Information Technology at Ruhr University Bochum. His work focuses on radar systems, AI integration in sensor technologies, and applications in humanitarian demining. He collaborates with Prof. Dr.-Ing. Ilona Rolfes and contributes to projects like KI-ROJAL and Terahertz-NRW. His research emphasizes radar echo simulation, GPR-based localization, and SAR algorithm development. Research interests include radar-based particle tracking, sensor fusion in indoor environments, and electromagnetic localization in granular materials. Recent work highlights AI-driven approaches for improving training data generation and scenario augmentation in demining applications. Publications span high-impact journals like Sensors and conferences such as IEEE MTT-S and ICEAA. He actively participates in academic events including the Faculty Colloquium and international workshops. He maintains a lab website at www.etit.ruhr-uni-bochum.de/hfs/ and holds an ORCID ID for scholarly tracking.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Min Peng is a Professor at Wuhan University's School of Computer Science. His research focuses on artificial intelligence, machine learning, natural language processing, and knowledge graphs. He has collaborated extensively with institutions like Hefei University of Technology and the University of Chinese Academy of Sciences. His work bridges theoretical advancements in AI with practical applications in finance, social media analysis, and network optimization. Recent contributions include neural-symbolic reasoning frameworks, contrastive learning for knowledge graphs, and financial benchmarking with large language models. Research interests emphasize scalable machine learning models for complex reasoning tasks, explainable AI, and domain-specific applications in finance and social networks. Over 100 publications span venues like WWW, ACL, and NeurIPS, highlighting interdisciplinary impact. Notable projects include SymAgent (neural-symbolic agent frameworks), PIXIU (financial LLM benchmark), and DTC (commonsense machine comprehension). Key technical trends include integrating large language models with structured data, temporal knowledge graph reasoning, and transfer learning across domains. His work often addresses real-world challenges in data efficiency, interpretability, and cross-domain scalability. Current efforts explore financial LLMs, agent-based reasoning systems, and multimodal applications. While no specific grants or awards are listed in the provided data, his prolific publication record indicates sustained research excellence. Collaboration networks include teams in computer science, electrical engineering, and finance disciplines.

Gabriele SchragView profile

Professor

• MEMS Technology
• Microfluidics
• Acoustic Sensors
• +3 more

Prof. Gabriele Schrag holds the Professorship of Microsensors and Actuators at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. Her research focuses on MEMS (Micro-Electro-Mechanical Systems), including microsensors, actuators, and their applications in acoustics, microfluidics, and bioengineering. She has pioneered work in virtual prototyping for system-level modeling to enhance device robustness and performance. Education: PhD (summa cum laude) from TUM on 'Modeling coupled effects in microsystems' Habilitation in sensor systems technology (2018) Acting head of the Chair of Technical Electrophysics (2018-2023) Research emphasizes acoustic MEMS transducers , electrohydrodynamic printing , and physics-based modeling . Notable projects include developing piezoelectric MEMS microphones with corrugated membranes and integrated micropump systems. Awards include the Bavarian Prize for Good Teaching (2021) and Eurosensors Fellow Award (2019). Her work bridges virtual prototyping with real-world applications , addressing challenges in miniaturization, energy efficiency, and sensor integration for medical and industrial systems.

Ilia PolianView profile

Professor

• Circuit Architecture
• System Design
• Hardware Reliability
• +6 more

Prof. Ilia Polian serves as Head of the Institute of Computer Engineering and Chair of the Hardware-Oriented Computer Science (HOCOS) department at the University of Stuttgart. His leadership spans research, teaching, and institutional coordination across multiple high-impact projects. Prof. Polian's research focuses on developing circuit and system architectures based on both traditional and novel principles, including neuromorphic, stochastic, and approximate architectures. His second major research focus is systematic design methodology and design automation, with particular emphasis on safety and reliability properties of developed systems. Current research directions include quantum computing engineering, secure mixed-signal neural networks, and resource-efficient stochastic circuits for near-sensor computing applications. His recent publications demonstrate strong trends in quantum computing (particularly circuit partitioning and compilation for multi-QPU architectures), hardware security (including memristive cryptographic implementations), and AI-driven approaches to hardware testing and reliability. These works bridge fundamental computer architecture research with practical industrial applications. University of Stuttgart's Publication Prize for Paper on Partitioning of Quantum Circuits Prof. Polian actively supervises doctoral students including Devanshi Upadhyaya, and leads significant research grants such as the DFG Priority Program Nano Security which he coordinates. His department offers numerous thesis and research opportunities for students interested in cutting-edge hardware research. The Hardware-Oriented Computer Science department maintains strong collaborations with industry partners including IBM, Infineon Technologies, and Advantest, as well as academic institutions through the IQST Graduate School and QuantumBW initiatives.

• Medical Image Computing
• Tumor Growth Modeling
• Computational Physiology
• +3 more

Bjoern Menze is a Professor and Rudolf Mößbauer Tenure Track Chair at the Technical University of Munich (TUM), leading the Image-based Biomedical Modeling Group within the Munich School of Bioengineering. His research focuses on medical image computing, tumor growth modeling, and computational physiology, with applications in clinical neuroimaging and personalized radiotherapy design. He holds a Ph.D. in Computer Science from Heidelberg University and has held positions at ETH Zurich, INRIA Sophia Antipolis, MIT, and Harvard Medical School. His academic journey includes a postdoc at MIT’s CSAIL and Harvard Medical School, followed by roles at ETH Zurich and INRIA. His work bridges biomedical imaging with machine learning, emphasizing model-driven analysis of physiological processes. He has been a visiting professor at Maastricht University and contributes to initiatives like the Center for Translational Cancer Research at TUM. Key research areas include tumor growth modeling, quantitative imaging biomarkers, and integrating mathematical models with clinical data. His awards include the MICCAI Young Scientist Award (2014), Leopoldina Fellowship (2009), and DFG Research Fellowship (2008). He advises on medical AI, leads interdisciplinary projects, and publishes extensively in top journals like Nature Neuroscience and IEEE Transactions on Medical Imaging. His lab’s work spans applications such as glioblastoma radiotherapy optimization, whole-body bone lesion detection, and neural connectivity imaging. Collaborations include institutions like Harvard, MIT, and ETH Zurich. He emphasizes translating computational methods into clinical practice for personalized healthcare solutions.

Matthias KrauledatView profile

Professor

• Machine Learning
• Brain-Computer Interface
• Signal Processing
• +3 more

Prof. Dr. Matthias Krauledat is a faculty member at Hochschule Rhein-Waal , specifically within the Faculty of Technology and Bionics . His academic career spans both theoretical research and industrial application, with a focus on Machine Learning and Brain-Computer Interfaces . After completing his PhD in Electrical Engineering/Computer Science at Technische Universität Berlin , he has contributed significantly to the advancement of EEG-based communication systems and neural signal processing methodologies. Born in Essen, Germany Studied Mathematics with a minor in Computer Science at University of Münster/Oxford Doctoral research at TU Berlin on Brain-Computer Interfaces Industrial experience at Henkel AG & DMT GmbH Research Interests focus on Machine Learning applications in Neuroscience and Biomedical Engineering , specifically Brain-Computer Interfaces , EEG Signal Processing , and Adaptive Classification Systems . His work explores how algorithms can be developed to enable self-learning computers to solve complex tasks involving neural data interpretation and prediction for previously unseen data in clinical and technological contexts. Publications demonstrate a consistent contribution to Neuroscience and Machine Learning fields, with particular emphasis on Brain-Computer Interface systems from 2004 through 2009. His research has focused on reducing training requirements, improving signal processing accuracy, and developing novel interaction paradigms like the Hex-o-Spell mental typewriter while addressing statistical challenges like covariate shift in neural data analysis. Professional Experience includes academic research at TU Berlin's Intelligent Data Analysis group, industrial software development roles at Henkel AG's Scientific Computing department, and TÜV Nord Group's Optical Metrology and Machine Diagnostics divisions. He maintains active research connections through collaborative publications with leading experts in the field.

Jochen GarckeView profile

Professor

• Numerical Methods in Science and Technology
• Machine Learning
• Sparse Grids
• +5 more

Prof. Dr. Jochen Garcke is a faculty member at the Institute for Numerical Simulation, University of Bonn, with a dual affiliation at Fraunhofer SCAI's Department of Numerical Data-Based Prediction. His work bridges numerical simulation and machine learning, focusing on high-dimensional problems, sparse grids, and optimal control. Key research themes: Sparse grids, machine learning for simulations, reinforcement learning, uncertainty quantification Teaching includes courses on Numerical Methods in Science and Technology and Scientific Computing , emphasizing practical machine learning applications. Recent publications explore hybrid models combining data-driven and physics-based approaches in automotive engineering, wind turbines, and geoscientific modeling. His group employs adaptive sparse grids, graph algorithms, and spectral methods to tackle challenges in crash simulations, fluctuating renewable energy systems, and turbulent flow analysis. Collaborations span Fraunhofer SCAI and industry 4.0 initiatives.