جست‌وجوی استاد و پژوهشگر

Alessio Gagliardiمشاهده پروفایل

دانشیار

• Nanoscale Simulation
• Solar Cell Technology
• Electrochemical Systems
• +۳ مورد دیگر

Professor Alessio Gagliardi is an Associate Professor at the TUM School of Computation, Information and Technology , Technische Universität München. His academic career includes a tenure-track position since 2014 and promotion to Associate Professor in 2020. He contributes to the development of the TiberCAD and GDFTB simulation software and integrates machine learning into materials science research. Education: Engineering degree from the University of Rome Tor Vergata (Italy) Doctorate in Physics from the University of Paderborn (2007) Postdoctoral research at Bremen Center for Computing Materials and Rome Research Interests: Prof. Gagliardi’s work focuses on simulating nanostructured devices for energy conversion, including organic semiconductors, perovskite solar cells, and electrochemical systems. His methods span nanoscale ( density functional theory , quantum Green’s functions), mesoscale ( kinetic Monte Carlo ), and macroscopic ( drift-diffusion models). Recent efforts emphasize machine learning applications for multiscale modeling and experimental-theoretical integration. Articles Overview: Recent publications explore machine learning optimization in materials design, nanoscale simulations for energy devices, and advancements in electrocatalysts. Key themes include improving platinum electrocatalyst efficiency, perovskite solar cell stability, and AI-driven predictions for material performance. Labs/Teams: Active in software development teams for TiberCAD and GDFTB , advancing computational tools for materials science and energy systems.

Gianluca Demartiniمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Gianluca Demartini is a Research Fellow at The University of Queensland's School of Electrical Engineering and Computer Science. He was appointed in 2025 under the Dieter Schwarz Fellowship program, hosted by Prof. Maribel Acosta. His work intersects artificial intelligence, machine learning, and quantum computing, focusing on applications in computer vision, natural language processing, and human-AI collaboration. Research Interests: Grounding natural language to 3D scenes Data-driven algorithms for signal and information processing Toward more general deepfake detection through anomaly-based analysis Bridging quantum logic, artificial intelligence, and quantum computation Trustworthy Knowledge Graphs Software Engineering for AI Scientific Awards Dieter Schwarz Fellowship

• Artificial Intelligence
• Machine Learning
• Automated Algorithm Configuration
• +۳ مورد دیگر

Holger Hoos is a Professor in the Department of Methodology of Artificial Intelligence at RWTH Aachen University. His research spans artificial intelligence, automated algorithm configuration, and machine learning robustness, with applications in optimization, earth observation, and quantum computing challenges. Research Focus: His work emphasizes: Robustness verification and efficiency improvements in neural networks Automated Machine Learning (AutoML) frameworks and benchmarking Multi-objective optimization and algorithm configuration AI applications in remote sensing, time-series analysis, and recommender systems Recent publications (2024-2025) show a dominant trend toward enhancing AI reliability through rigorous verification methods, scalability solutions for large-scale problems, and adaptable frameworks for dynamic data environments. Quantum computing applications and energy-efficient AI also feature prominently. He leads research initiatives at RWTH Aachen focusing on methodological advances in AI, though specific labs/teams are not detailed.

• Solar Energy Conversion
• Semiconductor Devices
• Photovoltaic Cells
• +۴ مورد دیگر

Dr. Gideon Segev is a senior academic staff member at the School of Electrical Engineering , Tel Aviv University, leading the Energy Devices Research Lab . His work focuses on solar energy conversion, semiconductor devices, and sustainable water treatment technologies. Research Interests : Solar energy conversion with photon-enhanced thermionic emission (PETE) and hybrid photoelectrochemical-photovoltaic systems . Development of ratchet-based ion pumping membranes for low-cost, decentralized water desalination. Advanced characterization of charge collection efficiency in photovoltaic and photoelectrochemical devices. Publications : His recent work explores 2025 ratchet-driven ion separation , 2024 solar fuels roadmaps , and 2023 nanofluidic desalination systems , reflecting his commitment to sustainable energy and water solutions.

Jonas Winklmannمشاهده پروفایل

پژوهشگر

• Quantum Computing Architecture
• Efficient Algorithms on Heterogeneous Hardware
• Control Algorithms for Quantum Systems
• +۳ مورد دیگر

Jonas Winklmann is a researcher at the Chair of Computer Architecture and Parallel Systems at Technische Universität München (TUM), focusing on quantum computing architectures and hardware acceleration. He contributes to projects like SEANERGYS and MUNIQC-ATOMS, integrating quantum systems with high-performance computing (HPC) frameworks. Research: Quantum hardware design, FPGA-based acceleration, and parallel algorithms Collaborations: EuroHPC initiatives, interdisciplinary quantum computing efforts Teaching: Mentoring lab courses on HPC systems and quantum integration His work spans neutral atom quantum computing, algorithm optimization for heterogeneous platforms, and control systems for quantum devices. Publications highlight FPGA applications in quantum simulation and atom detection techniques. He collaborates on software projects like QMPI and SWEET, advancing quantum-HPC hybridization.

• Artificial Intelligence
• Optimization
• Machine Learning
• +۳ مورد دیگر

Sebastian Pokutta is a Professor at Technische Universität Berlin, Vice President at Zuse Institute Berlin (ZIB), and Chair of the Cluster of Excellence MATH+ and MODAL. His research focuses on artificial intelligence, optimization, and machine learning, with emphasis on algorithmic development, decision-making systems, and real-world applications in areas like satellite data analysis, quantum computing, and combinatorial mathematics. He leads a research group exploring AI-driven methodologies and their interdisciplinary applications. He holds academic positions including leadership roles in major institutions and has contributed to numerous high-impact publications in optimization, machine learning, and quantum computing. His work has been recognized with awards such as the Science Prize of the Association for Pediatric Orthopedics (VKO). Pokutta actively engages in academic outreach, delivering talks on optimization and AI, and contributes to open-source projects like the FrankWolfe.jl library. His research bridges theoretical foundations with practical implementations, addressing challenges in computational efficiency, mathematical modeling, and AI ethics.

Manfred Hauswirthمشاهده پروفایل

استاد

• Distributed Systems
• Internet of Things (IoT)
• Stream Processing
• +۴ مورد دیگر

Prof. Manfred Hauswirth is the Managing Director of Fraunhofer Institute for Open Communication Systems (FOKUS) and holds the Chair for Open Distributed Systems at Technical University of Berlin. His research focuses on distributed systems, IoT, stream processing, quantum computing, and blockchain. He has held roles including Vice Director at Digital Enterprise Research Institute (DERI) and professor at National University of Ireland, Galway. He leads multiple strategic initiatives, including the Fraunhofer Quantum Technologies Research Field and the Weizenbaum Institute. His work bridges academia and industry, emphasizing digitalization, quantum computing, and IoT. Education: Dipl.-Ing. (1993), Dr. techn. (1999) in Computer Science from Vienna University of Technology. Postdoctoral work at École Polytechnique Fédérale de Lausanne (EPFL). Research Interests: Prof. Hauswirth’s work spans distributed systems, semantic web technologies, quantum algorithms, and IoT edge computing. He emphasizes real-world applications like smart cities, autonomous driving, and secure data management. Recent trends in his publications include quantum programming frameworks (e.g., Qrisp), scalable graph distillation, and edge-based AI systems. Awards: Not explicitly listed, but his work has been recognized through leadership roles in IEEE, ACM, and Fraunhofer committees. Advising & Grants: Active in funding initiatives like the Berlin Institute for Learning and Data (BIFOLD) and Einstein Center Digital Future (ECDF). Leads projects on quantum benchmarking, energy flexibility markets, and semantic stream processing. Labs/Teams: Directs the Fraunhofer High Performance Center for Digital Networking and chairs the Quantum Computing Competence Network, integrating interdisciplinary teams across quantum computing, IoT, and AI domains.

Jan Baumbachمشاهده پروفایل

استاد

• Computational Systems Biology
• Bioinformatics
• Network Medicine
• +۵ مورد دیگر

Prof. Jan Baumbach is a Professor and Director of the CoSy.Bio Institute at the University of Hamburg (since 2021), holding a part-time position as head of the Computational BioMedicine group at the University of Southern Denmark (since 2012). He leads the Computational Systems Biology department within the Faculty of Mathematics, Informatics and Natural Sciences. His academic journey includes roles as Chair of Experimental Bioinformatics at TU Munich, Junior Research Group Leader at the Max Planck Institute for Informatics, and postdoctoral research at UC Berkeley. He holds a doctoral degree from Bielefeld University, where he specialized in genome informatics and systems biology. His research focuses on computational systems biology, network medicine, and AI-driven biomedical applications. Key areas include drug repurposing via network-based analysis, federated learning for privacy-preserving multi-institutional studies, and bioinformatics tools like NeDRex and Drugst.One. Notable contributions include methodologies for differential co-expression analysis (DRaCOon), disease module identification (DysRegNet), and proteomic data harmonization (ProHarMeD). Prof. Baumbach’s work bridges algorithm development and clinical translation, addressing challenges in personalized medicine, metabolic disorders, and regenerative biology. His team actively develops open-source platforms to democratize access to advanced computational tools in biomedicine. His publications span 2025–2023, with a focus on genomic/proteomic analysis, federated learning applications, and AI-driven insights into disease mechanisms. Collaborative projects include multi-omics studies of multiple sclerosis, diabetic bone healing, and antiviral strategies against influenza. Labs/Teams: CoSy.Bio Institute (Hamburg), Computational BioMedicine Group (SDU), and collaborations with institutions like ZBH (Center for Bioinformatics Hamburg).

Sebastian Stillerمشاهده پروفایل

استاد

• Optimization
• Operations Research
• Algorithmic Game Theory
• +۵ مورد دیگر

Prof. Dr. Sebastian Stiller is a Professor at the Institute for Mathematical Optimization within the Carl-Friedrich-Gauß-Fakultät at Technische Universität Braunschweig. His research focuses on optimization, operations research, algorithmic game theory, quantum computing, and transportation science. He leads projects funded by DFG and BMBF, including 'SOAP' for air transport optimization and 'HyNEAT' for hydrogen supply networks in aviation. He teaches courses such as Linear Algebra, Algorithmic Game Theory, and Quantum Algorithms in both German and English. His research spans robust optimization, vehicle routing, and quantum algorithm development. Notable projects include optimizing wind farm control systems and analyzing quantum computing's potential for solving linear programming challenges. He collaborates with industry partners like 4flow AG and GAMS Software GmbH, emphasizing practical applications of his theoretical work. Teaching responsibilities include foundational mathematics courses for data science and engineering students, as well as advanced seminars on optimization. His contributions to operations research have been published in top-tier journals like Operations Research and Transportation Science.

• Big Geospatial Data Management
• Distributed Computing
• Machine Learning
• +۴ مورد دیگر

Prof. Martin Werner is a Professor of Big Geospatial Data Management at the Technische Universität München (TUM), within the TUM School of Engineering and Design. His research focuses on distributed computing, machine learning, quantum algorithms, and geospatial data analysis. He holds a doctorate in mathematics from Ludwig Maximilian University of Munich and has held positions at institutions including the German Aerospace Center and Bundeswehr University Munich. Education: Studied mathematics at the University of Bonn (B.Sc./M.Sc.), completed a Ph.D. at Ludwig Maximilian University of Munich (2014) on indoor navigation and geometric applications. Postdoctoral and senior researcher roles at LMU Munich, University of Hanover, and others. Research interests include geospatial data processing, parallel computing, and quantum computing applications. Notable work includes the GloBiMaps probabilistic data structure and contributions to trajectory analysis and anomaly detection in industrial systems. Awards include the ACM SIGSPATIAL GIS Cup (2015) and IPIN Best Paper Award (2014). Advising and grants: Supervised research in geospatial AI, quantum computing, and environmental monitoring. Collaborates with interdisciplinary centers like the Interdisciplinary Center for Applied Machine Learning (ICAML). Labs/Teams: Involved in the ICAML and leads projects such as the AtlasHDF framework and FOREST-2 mission collaborations.

Ruedi Seilerمشاهده پروفایل

استاد

• Quantum Hall Effect
• Mathematical Physics
• Information Theory
• +۵ مورد دیگر

Professor Ruedi Seiler is a faculty member at the Technical University of Berlin, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics. His office is located at Straße des 17. Juni 136, 10623 Berlin, Germany, with contact information including office telephone (+49 30 - 314 25 761) and email (seiler@math.tu-berlin.de). Professor Seiler's research spans multiple domains within mathematical physics and information theory. His primary interests include: Quantum Hall Effect and related topological phenomena in condensed matter physics Information theory, data compression, and universal source coding Quantum information theory and Q-typical configurations Distributed systems and peer-to-peer network architectures E-Pedagogy for large mathematics courses at the university level His publication record spans nearly four decades, with recent work focusing on quantum information theory, distributed virtual environments, and mathematical aspects of data compression. His research shows a consistent thread connecting mathematical rigor with physical applications, particularly in quantum systems and information processing. Professor Seiler has collaborated extensively with researchers across disciplines, including physicists working on quantum Hall systems, computer scientists developing distributed systems, and mathematicians exploring information-theoretic concepts. His work bridges theoretical mathematics with practical applications in physics and computer science.

Jan Stühmerمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Molecular Mechanics
• +۷ مورد دیگر

Jun.-Prof. Dr. Jan Stühmer is an Assistant Professor and Principal Investigator (PI) at the Helmholtz Institute for Information Technology (HITS). His research focuses on machine learning, artificial intelligence, and their interdisciplinary applications in molecular mechanics, protein structure prediction, and materials science. He explores advanced graph neural networks, neuro-symbolic systems, and geometric algorithms to address challenges in computational biology, weather forecasting, and computer vision. Key research interests include permutation-invariant learning models (e.g., Set-LLM), error correction in machine learning systems, and the development of physics-based force fields like Grappa. His work bridges theoretical computer science with practical applications in drug design, materials discovery, and climate modeling. Recent contributions highlight innovations in protein design through geometric algebra, dynamic graph representation learning, and the optimization of crystal structure networks. Dr. Stühmer’s methodologies often integrate spatial information learning and ensemble forecasting techniques, showcasing a commitment to impactful, cross-disciplinary research. Despite extensive publications since 2009, no awards or grants are explicitly mentioned in the provided data. His academic trajectory reflects a sustained focus on advancing machine learning fundamentals and their real-world implications.

• Theoretical Physics
• Quantum Optics
• Cavity Optomechanics
• +۱۲ مورد دیگر

Florian Marquardt is the Scientific Director and Head of the Theory Division at the Max Planck Institute for the Science of Light in Erlangen, Germany, and holds a part-time professorship at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He leads a research group focused on the intersection of quantum physics, optics, and machine learning, contributing to foundational and applied advances in quantum technologies. His educational background includes a PhD summa cum laude from the University of Basel (2002), followed by postdoctoral research at Yale University and in the Swiss National Center for Competence in Research. He served as a Junior Professor at LMU Munich (2005–2010) and Full Professor at FAU (2010–2016) before joining MPL. Marquardt's research spans cavity optomechanics , quantum many-body physics , neuromorphic computing , and machine learning for physics . His group develops minimalist theoretical models to explain experimental phenomena and explores how AI can automate scientific discovery. He is particularly interested in energy-efficient analog computing devices inspired by neural networks and in leveraging physics to improve machine learning. The recent publications reflect a strong trend toward integrating artificial intelligence with quantum science, including automated experiment design, quantum error correction via neural networks, and neuromorphic photonics. There is also sustained work in quantum measurement, topological photonics, and nonequilibrium dynamics, showing both breadth and depth in theoretical physics. Emmy-Noether Fellowship (DFG, 2007) Walter Schottky Prize (DPG, 2009) ERC Starting Grant (2011) Marquardt actively supervises research and teaches advanced courses such as 'Machine Learning for Physicists' and 'Foundations of Quantum Mechanics' at FAU. His group participates in major research networks including the Max Planck School of Photonics, ELLIS, and the European Laboratory for Learning and Intelligent Systems. The Theory Division fosters collaborations on quantum technologies and neuromorphic systems, with strong emphasis on interpretability, generalization, and hardware implementation.

Michaela Geierhosمشاهده پروفایل

استاد

• Computational Linguistics
• Computer Science
• Natural Language Processing
• +۵ مورد دیگر

Prof. Dr. Michaela Geierhos holds the Chair of Data Science at the CODE Research Institute, Faculty of Computer Science, Bundeswehr University Munich. Her research bridges computational linguistics and computer science, focusing on semantic information processing and practical applications like cyber threat intelligence and trustworthy AI in law enforcement. Roles: Technical Director at CODE Research Institute (2021–present), Professor of Data Science (2020–present) Collaborations: Partnerships with military, industry, and public sectors; projects include disinformation detection, deepfake identification, and AI for cybersecurity. Her work emphasizes addressing dataset contamination in NLP, developing adjusted evaluation metrics, and exploring biases in healthcare data. She leads research into synthetic data generation, quantum NLP, and privacy-preserving AI. Recent articles focus on contestable AI systems, NER evaluation frameworks, and ethical considerations in language models. Scientific awards include the Best Paper Award at AI4HMO (NATO STO) in 2021. Grants: Directed projects like VIKING (trustworthy AI), KiTIE (technology transfer), and KIMONO (campaign monitoring). Education: Supervised doctoral students, including external candidate Philipp J. Rösch (2025), and contributed to interdisciplinary teaching combining theory and practice.

• Optical Networking
• Elastic Optical Networks
• Machine Learning for Network Management
• +۳ مورد دیگر

Georgios Ellinas is a Professor at the University of Cyprus, specializing in optical networking, machine learning for network management, and UAV swarm coordination. His research spans critical areas such as Elastic Optical Networks (EONs) , Physical Layer Security , and Multi-Agent Reinforcement Learning for autonomous systems. Recent work includes quantile regression models for handling uncertainty in network traffic, edge-assisted collision warning systems for urban mobility, and multi-task learning architectures for drone state identification. He applies distributed estimation techniques to jamming aerial targets and explores probabilistically robust trajectory planning for autonomous vehicles. His contributions extend to fair resource allocation in optical networks, UAV swarm coordination using ROS-LoRa integration, and quantum key distribution optimization. Collaborations with researchers like Tania Panayiotou and Panayiotis Kolios highlight his interdisciplinary approach.