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Hanna MeyerView profile

Professor

• Remote Sensing
• Spatial Modeling
• Machine Learning for Spatial Data
• +7 more

Prof. Dr. Hanna Meyer is a Professor of Remote Sensing and Spatial Modeling at the Institute of Landscape Ecology, University of Münster (WWU). She leads the Remote Sensing and Spatial Modeling Group and is actively involved in teaching and research in geospatial data science, machine learning, and environmental monitoring. Her work is supported by multiple national and international funding bodies including the DFG, EU Horizon Europe, and internal university grants. B.Sc. Geography, Philipps University Marburg (2007–2010) M.Sc. Environmental Geography, Philipps University Marburg (2010–2013) Ph.D., Philipps University Marburg (2014–2018) Her research focuses on machine learning methods for spatial data, optical remote sensing, environmental monitoring, and spatio-temporal modeling. She develops and applies advanced statistical and machine learning techniques to satellite and drone-based data for mapping ecological variables, land cover, and environmental change. Her work emphasizes methodological rigor, model transferability, and uncertainty quantification in spatial predictions. The recent publications reflect a strong trend in developing and validating machine learning models for environmental mapping, with applications in soil science, peatland hydrology, forest ecology, and polar climatology. She contributes both to theoretical advancements in spatial model validation and to practical software tools in R for geospatial analysis. She has secured competitive research funding for projects such as PRISM, Carbon4D, Uebersat, and BEyond, focusing on spatial pattern recognition, carbon modeling, AI model transferability, and biodiversity prediction. She teaches courses on remote sensing, spatial data analysis with R, and environmental modeling, and supervises students and early-career researchers. She collaborates widely with researchers across institutions and leads a dynamic research group including postdoctoral researchers and students. Her open-source contributions, particularly R packages like CAST and uavRst, support reproducible research in geospatial machine learning.

Michael BührenView profile

Professor

• Automation
• Robotics
• Digitalization
• +7 more

Michael Bühren is a Professor at Westfälische Hochschule, affiliated with the Faculty of Mechanical Engineering Bocholt and the Mechatronik-Institut Bocholt. He serves as Project Manager for the Digital Factory Bocholt and Digi-Up! initiatives and was the speaker of the Mechatronics Institute Bocholt in 2019. His research focuses on automation, robotics, digitalization, and Industry 4.0 . He investigates practical applications of smart manufacturing technologies, including simulation and virtualization, robotics with human-robot collaboration (MRK) capabilities, OPC UA for data acquisition, and machine learning for data and image processing. His work bridges industrial automation with modern digital technologies. His recent publications reflect a strong trend toward integrating industrial systems with digital platforms, particularly through OPC UA and IoT frameworks, while earlier work includes large-scale industrial automation projects in the steel industry and dynamic modeling of marine drive systems. Prof. Bühren is actively involved in technology transfer, represented through his role in the "Effizienz Forum Wirtschaft" and initiatives connecting startups with small and medium enterprises (SMEs). He has no listed scientific awards in the provided text. He teaches courses in control engineering, electrical drive systems, motion control, energy management, and artificial intelligence at the bachelor’s and master’s levels. He advises students, though specific names are not listed. His research is conducted within the Mechatronik-Institut Bocholt, a hub for interdisciplinary research in mechatronics, automation, and digital production technologies.

David EggerView profile

Associate Professor

• Halide Perovskites
• Electronic Structure Theory
• Molecular Dynamics
• +6 more

Prof. Dr. David Egger is an Associate Professor at the Technical University of Munich (TUM) , leading the Associate Professorship of Theory of Functional Energy Materials under the TUM School of Natural Sciences . He joined TUM in 2019 as a Rudolf Mößbauer Professor and was promoted to Associate Professor in 2023. His work focuses on atomistic theories of energy materials , particularly solar cells , using electronic-structure and molecular-dynamics techniques to study molecules , solid-state materials , and nanoscale interfaces . His research interests span Halide Perovskites , Density Functional Theory , Machine Learning in Materials Science , and Optoelectronic Properties . Recent publications highlight advancements in machine-learning force fields for predicting dielectric properties and ion migration in energy materials, alongside studies on perovskite surface dynamics and phonon effects in solar energy conversion. Scientific Awards : Sofja-Kovalevskaja Award (2017) Best Thesis Award of TU Graz (2015) Koshland Prize (2014) Erwin Schrödinger Fellowship (2014) DOC Fellowship (2010) Teaching and Leadership : Prof. Egger teaches Computational Materials Physics and Advanced Topics in Theory of Functional Energy Materials . He serves as Executive Director of the Atomistic Modeling Center , fostering interdisciplinary research in energy materials.

Rainer HeintzmannView profile

Professor

• Super-resolution microscopy
• Optical microscopy
• Point spread function calculation
• +4 more

Prof. Dr. Rainer Heintzmann serves as Head of the Microscopy Department at the Leibniz Institute of Photonic Technology (Leibniz-IPHT) in Jena, Germany. His research focuses on advancing optical microscopy techniques, particularly super-resolution methods that surpass the diffraction limit to visualize cellular structures at nanoscale resolution. His primary research interests center on structured illumination microscopy (SIM), point spread function modeling, and computational imaging techniques. He has made significant contributions to developing automated multicolor SIM systems, extreme ultraviolet microscopy approaches, and deep learning-enhanced image analysis methods. His work bridges optical physics, computational algorithms, and biomedical applications, with particular emphasis on making advanced microscopy techniques more accessible through open-source hardware and software solutions. Analysis of his recent publications reveals a strong focus on overcoming fundamental limitations in optical microscopy. His research spans from theoretical modeling of optical systems to practical implementations for biological imaging. Key trends include the development of more accurate point spread function calculations, expansion of super-resolution techniques to new wavelength regimes, and integration of machine learning for image analysis and segmentation. Prof. Heintzmann actively collaborates with researchers across multiple institutions, as evidenced by his co-authorship on numerous interdisciplinary publications. His work has appeared in high-impact journals including Nature Methods, Nature Reviews Molecular Cell Biology, and Optics Express, reflecting the significance of his contributions to advancing microscopy techniques. His laboratory at Leibniz-IPHT appears to focus on developing novel microscopy instrumentation, particularly open-source implementations of super-resolution techniques. Recent projects include the openSIMMO platform for automated multicolor structured illumination microscopy and work on extreme ultraviolet microscopy that could potentially extend super-resolution capabilities into the X-ray regime.

Sandra Diaz PierView profile

Researcher

• Computational Neuroscience
• High Performance Computing
• Machine Learning
• +3 more

Dr. Sandra Diaz Pier is a Scientific Lead at the Jülich Supercomputing Centre (JSC) within the Jülich Research Centre , Germany. Specializing in computational neuroscience , high performance computing (HPC) , and machine learning , she bridges neuroscience and advanced computational methods through her research. Education: B.Sc. in Electronic Systems Engineering, Mexico M.Sc. in Computer Science (focus: machine learning, quantum computing), Mexico Second M.Sc. in Electrical Engineering, Ontario, Canada Ph.D. in Computer Science, Germany (2021) Her research focuses on modeling and simulating brain dynamics and plasticity at multiple scales, leveraging HPC to accelerate large-scale neural network simulations. She actively contributes to EU projects like the Human Brain Project (HBP) , Virtual Brain Cloud , and EBRAINS 2.0 , emphasizing infrastructure development and educational training. Her work includes open-source tools such as the NEST simulator , The Virtual Brain , and L2L , enabling efficient parameter exploration and multiscale co-simulation frameworks. The 15 most recent publications highlight her interdisciplinary approach, spanning topics from quantum computing in biomolecular simulations to neural plasticity algorithms and cloud-based brain modeling . These articles reflect her expertise in integrating machine learning , multi-scale simulation , and HPC infrastructure for neuroscience challenges, including seizure propagation, Parkinson’s disease progression, and swarm intelligence in spiking networks. She leads technical coordination in projects like EBRAINS and serves as a task leader in the HBP infrastructure work package , while also organizing workshops and hackathons for open-source tools. Her role involves supporting domain scientists through methodological research and workflow optimization for brain simulations.

Khalil EsperView profile

Researcher

• Verification and Model Checking
• Energy Optimization
• Real-time Systems
• +4 more

Khalil Esper is a Researcher at the Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), where he works at the Chair of Computer Science 12 (Hardware-Software Co-Design). His research focuses on verification, energy optimization, and runtime requirement enforcement in embedded systems and MPSoCs. His educational background includes: Informatics Engineering from Aleppo University, Syria (2010-2015) European Master in Embedded Computing Systems (EMECS) from Rhineland-Palatinate University of Technology Kaiserslautern-Landau (Germany) and Norwegian University of Science and Technology (Norway) (2017-2019) Esper's research interests center around verification and model checking, energy optimization on MPSoC, real-time systems and embedded systems, and autonomic computing. His work particularly focuses on runtime requirement enforcement mechanisms for non-functional properties in multi-processor systems-on-chip, with applications extending to medical devices and human-robot interaction systems. He has developed approaches using finite state machines, reinforcement learning, and evolutionary algorithms to ensure system properties are maintained during execution. His publication record shows a strong trend toward applying formal methods and runtime enforcement techniques to increasingly complex systems, with recent work expanding into safety-critical applications like orthoses and human-robot interaction. The interdisciplinary nature of his research bridges computer science, embedded systems engineering, and biomedical applications. Esper has supervised multiple theses including: Sascha H.: Runtime Requirement Enforcement of Non-Functional Requirements on MPSoCs Using Fuzzy Logic (2022) Iana S.: Feedback-Based Control of Non-functional Program Execution Properties on Linux (2023) Philipp L.: Runtime Requirement Enforcement of Functional and Non-Functional Requirements of a Knee Orthosis Based on a Digital Twin (2024) Avinash N.: Runtime Requirement Enforcement of Safety Properties of an Ankle Orthosis Based on a Digital Twin (2024) Zhiyi T.: Generation of Environment FSMs Using Machine Learning Techniques (2025) Moustafa A.: Runtime Requirement Enforcement of Safety Properties of Human-Robot Interaction Based on a Digital Twin (2025) Florian K.: Runtime Requirement Enforcement of Safety Properties of Human-Robot Interaction (2025) He has been actively teaching courses on Approximate Computing and Embedded Systems since the 2021/2022 academic year, demonstrating his commitment to academic instruction alongside his research activities. Esper is involved in the InvasIC research project, part of the DFG Transregional Collaborative Research Center 89 on Invasive Computing, which explores novel approaches to resource management in parallel computing systems.

Batuhan SesliView profile

Researcher

• Embedded Systems
• Machine Learning Hardware Acceleration
• RISC-V Architecture
• +3 more

Batuhan Sesli is a Researcher at the Department of Computer Science 12 (Hardware-Software-Co-Design) at Friedrich-Alexander University Erlangen-Nuremberg (FAU), Germany, since July 2024. His work focuses on embedded systems with emphasis on hardware acceleration for machine learning and efficient deployment of TinyML applications on resource-constrained devices. His educational background includes: M.Sc. in Information and Communication Technology from FAU (2022-2024) B.Sc. in Electrical & Electronics Engineering from Istanbul Ozyegin University, Turkey, with a minor in Computer Science (2016-2021) Research interests span acceleration techniques for machine learning workloads via RISC-V ISA extensions and specialized hardware accelerators, efficient deployment of TinyML on low-power devices, and co-design of AI algorithms and hardware. His work optimizes the synergy between algorithmic requirements and hardware capabilities in embedded systems, targeting practical solutions for edge AI applications. His recent DATE 2024 publication on DNN acceleration through weight clustering on RISC-V custom functional units exemplifies the trend toward energy-efficient specialized hardware, reflecting his focus on bridging algorithmic demands with hardware constraints in real-world embedded scenarios. He teaches a tutorial on Embedded Systems for Winter Semester 2024/2025 and offers thesis opportunities in his research domain, though no current open theses are listed.

Matthias BeckerView profile

Professor

• Human-Computer Interaction
• Simulation and Modeling
• Distributed Systems
• +6 more

Matthias Becker is a Professor at the Institute for Practical Computer Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover, where he has been a core member of the Human-Computer Interaction group since 2019. He serves as Internship Coordinator for Computer Science and Computer Engineering and holds key roles in the Computer Science Examination Board and Selection Committee, actively shaping academic governance and student development. His academic journey began with PhD studies at the University of Bremen (1996-2000) supported by a DFG grant, followed by a postdoctoral permanent position at Leibniz University Hannover (2000-2019), an Associated Assistant Professor role at École des Mines de Nantes (2000), and a Habilitation in Computer Science in 2013. This foundation enabled his transition to a full professorship in 2019. Becker's research spans Human-Computer Interaction, Simulation and Modeling, and Bio-inspired Computing, with applications in agriculture, renewable energy, and manufacturing. His work integrates distributed systems, optimization algorithms, and wireless sensor networks to solve complex real-world problems, such as greenhouse monitoring, wind farm logistics, and tire noise reduction. Recent publications reveal a strategic focus on practical validation of simulation models and cross-domain applications of nature-inspired algorithms. His 15 most recent publications (2018-2024) demonstrate consistent innovation in applying simulation techniques to offshore wind farm installation, agricultural pest management, and sports science. These works emphasize real-world validation, collaborative problem-solving, and the development of domain-specific optimization frameworks that bridge theoretical algorithms and industrial implementation. As Internship Coordinator, Becker facilitates critical industry-academia connections for students, while his examination board responsibilities ensure rigorous academic standards. His leadership in the Human-Computer Interaction group drives research on interactive systems for agriculture, energy, and health, with particular emphasis on user-centered design in complex operational environments like wind farm logistics and greenhouse automation.

Antonella Di PizioView profile

Associate Professor

• Computational Chemistry
• Molecular Modeling
• GPCR Research
• +5 more

Prof. Dr. Antonella Di Pizio (born 1984) is an Associate Professor for Chemoinformatics and Protein Modelling at the Department of Molecular Life Sciences within the TUM School of Life Sciences at the Technical University of Munich. Since 2018, she has led the Molecular Modeling group at the Leibniz Institute of Food Systems Biology at TUM in Freising, Germany. Her academic journey includes a PhD in Pharmaceutical Sciences from the University of Chieti, Italy (2012), followed by research at Philipps University in Marburg, Germany, and a postdoc position at the Hebrew University of Jerusalem, Israel. Prof. Di Pizio's research focuses on computational approaches to understanding chemosensory G protein-coupled receptors (GPCRs), particularly taste and smell receptors. Her work combines molecular modeling, chemoinformatics, and structural bioinformatics to investigate the molecular basis of ligand recognition and activation mechanisms. Her group develops predictive models for screening and designing bioactive compounds relevant to food reformulation and therapeutic applications. Analysis of Prof. Di Pizio's publication record reveals a strong focus on bitter taste receptors, particularly TAS2R family members, and odorant receptors. Her research spans computational modeling of receptor-ligand interactions, development of predictive algorithms for taste compound identification, and investigation of the structural basis of chemosensory perception. Recent work demonstrates increasing integration of machine learning approaches with traditional molecular modeling techniques. Scientific Recognition: Leibniz Best Minds Programme for Women Professors (2022) Platinum Manfred Rothe Excellence Award in Flavor Research (2019) Bernardo Nobile doctorate award VIII Edition (2013) Keystone Symposia Future of Science Fund Fellowship (2017) Prof. Di Pizio serves on the editorial board of Frontiers in Molecular Biosciences and is a Working Group Leader and Management Committee member of the ERNEST Cost Action CA18133. She teaches courses including 'Modeling and simulations of Biological Macromolecules' and 'Drug and Protein Design' at TUM. Her Molecular Modeling group collaborates extensively with other research groups at the Leibniz-LSB@TUM on interdisciplinary projects focused on food systems biology. The Molecular Modeling group, established relatively recently at the institute, investigates food-relevant molecules and their interactions using computational tools including molecular docking, molecular dynamics simulations, pharmacophore modeling, QSAR, machine learning, and virtual screening. Their work aims to develop next-generation methodologies for food design that address current challenges in the food system.

Bernhard SchröderView profile

Professor

• Computational Linguistics
• Cognitive Science
• Corpus Linguistics
• +3 more

Prof. Dr. Bernhard Schröder holds the full professorship (W3) of German Studies/Linguistics at the University of Duisburg-Essen , where he leads research at the Institute of German Studies (Faculty of Humanities). His academic career includes a Magister in Linguistics from University of Bonn (1991), PhD in Philosophical Linguistics (1997), and Habilitation in Language and Communication Sciences (2003). Education: Magisterabschluss (1991), University of Bonn Dr. phil. (1997), University of Bonn Habilitation (2003), University of Bonn His research bridges computational linguistics with cognitive science, focusing on prosodic analysis in mathematical proof texts, autism spectrum disorder communication studies, and corpus-based linguistic resource development. He directs the Naproche Project for computational proof checking and the AEET institute for early modern text digitization. Key projects include: Mathematical proof text modeling with frame semantics Prosody perception in autism through speech synthesis Corpus portal korpora.org development German-Namibia institute partnership Media-intensive linguistics training modules (MiLCA) He collaborates across institutions like University Hospital Freiburg, TU Dresden, and University of Bonn, with leadership roles in the European Society for Philosophy and Psychology and German Society for Computational Linguistics . His work involves speech synthesis uncertainty modeling, Kant text digitization, and Frege's arithmetic analysis.

• Parallel File and Storage Systems
• Modular Supercomputing
• Resource Disaggregation and Virtualization
• +6 more

Sarah Neuwirth is a tenured Professor for Computer Science at Johannes Gutenberg University Mainz (JGU) and a Visiting Researcher at the Jülich Supercomputing Centre. She manages JGU's High Performance Computing (HPC) division, coordinates regional/national HPC activities, and represents JGU in NHR, Gauss-Allianz, and HPC committees. Education : PhD (Dr. rer. nat.) in Computer Science (2018), Heidelberg University Diplom in Computer Science (2012), University of Mannheim Bachelor of Science in Computer Science (2010), University of Mannheim Research Interests : Parallel File and Storage Systems Modular Supercomputing (resource disaggregation/virtualization) Performance Engineering High Performance Computing Networking Reproducible Benchmarking Parallel I/O Publications Trends : Her work focuses on HPC performance modeling, parallel I/O optimization, modular supercomputing, network characterization, and reproducible benchmarks. Key themes include resource disaggregation, automated workflows, and data-intensive distributed applications. Scientific Awards : 2023 PRACE Ada Lovelace Award for HPC ZONTA Science Award 2019 Grants & Leadership : She leads the High Performance Computing division at JGU, participated in European DEEP projects, and serves on SC conference committees.

Hendrik DietzView profile

Professor

• DNA Origami
• Molecular Nanotechnology
• Structural Biology
• +5 more

Professor Hendrik Dietz holds the Chair of Biomolecular Nanotechnology at the Technical University of Munich (TUM) , affiliated with the TUM School of Natural Sciences and the Munich Institute of Robotics and Machine Intelligence . His research focuses on constructing synthetic molecular devices and machines through DNA origami and self-assembly principles. Research Themes DNA origami for programmable nanodevices Self-assembly inspired by natural molecular systems Molecular visualization with cryo-EM Applications in medicine and synthetic biology Key Article Trends : Dietz's work explores DNA-based rotary motors, virus-trapping shells, and bio-inspired vesicle production. His recent articles highlight integrations of DNA origami with electrochemical sensing, deep learning, and transmembrane transport systems. Scientific Awards ERC Consolidator Grant (2016) Gottfried Wilhelm Leibniz Prize (2015) Hoechst Lecturer Scholarship (2012) Arnold Sommerfeld Award (2010) ERC Starting Grant (2010) Collaborations and Grants : He receives funding from the Deutsche Forschungsgemeinschaft (DFG) via the Excellence Clusters CIPSM and NIM, SFB863, and the Leibniz Prize program, as well as the European Research Council. Dietz collaborates with institutions like Harvard Medical School and the Max Planck School Matter to Life.

Benjamin SturmView profile

Research Fellow

• Distributed Ledger Technology
• Blockchain Systems
• Design Science Research
• +4 more

Benjamin Sturm is a postdoctoral researcher at the Chair of Information Infrastructures at the Technical University of Munich . His work focuses on Distributed Ledger Technology (DLT) , blockchain systems , and design science research . He has contributed to projects like COOLedger (DLT configuration analysis) and BISE Student (open access thesis platform using blockchain). Sturm’s research investigates security vulnerabilities in DLT , autonomous agent collaboration , and systematic information retrieval frameworks . His work on DLT design principles and configuration trade-offs has been published in top conferences like ICIS and HICSS . He has developed tools for transparent academic publishing using the bloxberg blockchain and IPFS for decentralized storage. Key publication trends include DLT security , blockchain configuration challenges , and methodological innovations in design science research . He collaborates with Prof. Ali Sunyaev and researchers like Niclas Kannengießer on DLT applications in cloud computing, autonomous driving, and academic infrastructure. Current projects focus on decentralized knowledge management and robustness in open multi-agent systems .

Gordon ChengView profile

Professor

• Neuroengineering
• Robotics
• NeuroRobotics
• +1 more

Professor Gordon Cheng is a faculty member at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology (CIT). He serves as the Director of the Chair of Cognitive Systems at the Institute for Cognitive Systems (ICS), where he focuses on advancing cognitive systems, robotics, and their intersections. His research addresses fundamental challenges in creating intelligent systems capable of understanding and interacting with complex environments. Research Interests: His work spans Neuroengineering , Robotics , and Cognitive Systems , emphasizing the integration of biological principles into artificial systems. Key projects include robot skin development , EEG-based prosthetic control , and human-robot co-adaptation . Selected Publications: Recent articles highlight advancements in real-time tactile sensing , neuroprosthetics , and BCI-driven rehabilitation , reflecting his interdisciplinary approach bridging robotics and neuroscience. Supervision: He supervises PhD students from the Graduate School of Neuroscience (GSN), including Jasmin Kajopoulos and Nikolas Berberich. Contact: Email: gordon@tum.de | Website

Werner HemmertView profile

Professor

• Theoretical Neuroscience
• Technical Applications
• Biomedical Neuroscience
• +5 more

Professor Werner Hemmert leads the Bio-Inspired Information Processing group at the Munich Institute of Biomedical Engineering (TUM School of Computation, Information and Technology). His research spans theoretical, biomedical, and systems neuroscience, focusing on auditory processing, cochlear implants, and computational modeling of neural coding mechanisms. Primary research focus: Theoretical Neuroscience & Technical Applications Secondary research focus: Biomedical Neuroscience Tertiary research focus: Cellular & Systems Neuroscience His work employs computational modeling , psychophysical and objective nerve potential measurements , vibration analysis , and otoacoustic emission measurements to investigate: Coding of sound into nerve-action potentials Neuronal processing in the auditory brainstem Electrical stimulation of neurons Patient measurements in cochlear implant users Biophysics of sensory organs and neurons Recent publications highlight his contributions to auditory neuroscience, including studies on neural coding dynamics, computational modeling of auditory systems, and biophysical mechanisms of hearing. These works intersect with fields like neural networks , computational modeling , and auditory signal processing . Current or graduated GSN students under his supervision include Miguel Obando, Anna Dietze, Dr. Michael Drews, and Dr. Miguel Eduardo Obando Leitón. Contact: werner.hemmert@tum.de | Website