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Christoph NiemannView profile

Researcher

• Reliability Engineering
• Nanometer CMOS Technologies
• FPGA Design and Optimization
• +5 more

Dr.-Ing. Christoph Niemann is a researcher at the Institute of Computer Engineering within the School of Computer Science and Electrical Engineering at the University of Rostock. His work bridges computer engineering and biomedical applications, with a focus on hardware design for medical devices and energy-efficient computing systems. Dr. Niemann's primary research interests include reliability and aging in nanometer CMOS technologies and contributions to increasing the energy efficiency of FPGA-based applications . His work spans both theoretical hardware design and practical biomedical implementations, particularly in the field of deep brain stimulation systems. His research demonstrates a unique intersection of computer engineering principles applied to critical medical technologies. Analysis of Dr. Niemann's recent publications (2020-2025) reveals a strong focus on energy efficiency in implantable medical devices, particularly for deep brain stimulation applications. His work combines FPGA optimization techniques with biomedical requirements, creating hardware solutions that address the stringent power constraints of implantable systems. The publications show consistent collaboration with Dirk Timmermann, Christian Haubelt, and Franz Plocksties, indicating a well-established research group focused on medical device engineering. Dr. Niemann has made significant contributions to the STELLA platform for deep brain stimulation research and has developed novel approaches for sensor-based adaptive voltage scaling on FPGAs. His work demonstrates both theoretical innovation in hardware design and practical implementation in medical contexts.

Lorenz AdlungView profile

Researcher

• Computational Biology
• Systems Biology
• Immunology
• +6 more

Lorenz Adlung (Dr. rer. nat.) is a researcher at the University Medical Center Hamburg-Eppendorf (UKE), leading his own research group. His work spans computational biology, systems immunology, and translational medicine, with a focus on inflammatory diseases, erythropoiesis, and hepatocellular carcinoma (HCC). Research Interests Computational modeling of biological pathways (JAK2/STAT5, AKT/ERK) Intestinal mucosal healing and microbiome-immune interactions Machine learning applications in clinical decision-making Hepatocarcinogenesis and metabolomic biomarkers Key Contributions Developed tools like SCANet and Data2Dynamics for single-cell and dynamical systems analysis Identified regulatory networks in erythroid progenitor cells and lipid-associated macrophages Awards Co-winner of the 350th Anniversary Merck Innovation Cup (2020) His affiliations include collaborations with institutions like the European Molecular Biology Laboratory (EMBL) and the Immunology and Systems Biology groups at UKE. He has authored 18 publications, including high-impact journals like Cell , Nature Reviews Immunology , and Trends in Biotechnology .

Mario StankeView profile

Professor

• Bioinformatics
• Computational Genomics
• Machine Learning
• +7 more

Prof. Dr. Mario Stanke is a Professor of Bioinformatics at the Institute of Mathematics and Computer Science, University of Greifswald, Germany. He leads a research group focused on probabilistic models, machine learning, and algorithm development for genome annotation and biological sequence analysis. His research interests lie in probabilistic models and machine learning methods for structured biological data. He specializes in developing and applying recurrent neural networks , conditional random fields , and hidden Markov models for tasks such as gene prediction, genome annotation, and multiple sequence alignment. His work is instrumental in advancing computational tools for genomics. His most notable contributions include the development of the AUGUSTUS gene prediction program and the BRAKER pipeline, both widely used in the bioinformatics community. His recent publications demonstrate a strong trend in integrating deep learning with traditional statistical models for protein and genome analysis, particularly in multi-genome and comparative annotation frameworks. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: Prof. Stanke supervises bachelor's and master's theses in bioinformatics and computational genomics. While specific grants are not listed, his long-standing research output and leadership in major genome projects suggest active grant funding. He collaborates widely across institutions, as evidenced by co-authorship in large-scale genomics consortia. Labs and Teams: He leads a bioinformatics research group at the University of Greifswald, contributing to the development and maintenance of the AUGUSTUS and BRAKER software suites. The group operates a bioinformatics web server providing gene prediction services and tools.

• Remote Sensing
• Computer Vision
• Machine Learning
• +6 more

Qian Cheng is a Professor at the National University of Defense Technology, specifically affiliated with the College of Electronic Science in Changsha, China. With an extensive publication record spanning from 2013 to 2025, Dr. Cheng has established a significant research presence across multiple interdisciplinary domains. Their work bridges theoretical computer science with practical applications in agriculture, medical imaging, and remote sensing technologies. Dr. Cheng's research primarily focuses on the intersection of machine learning, computer vision, and domain-specific applications. Their work demonstrates particular expertise in applying advanced computational techniques to solve real-world problems in precision agriculture, where they've developed innovative approaches for crop monitoring using UAV technology and spectral analysis. In medical imaging, Dr. Cheng has contributed to photoacoustic imaging techniques for bone characterization and ophthalmic disease diagnosis. The research portfolio also includes significant contributions to autonomous systems, including UAV path planning and autonomous driving technologies. Analysis of Dr. Cheng's recent publications (2023-2025) reveals a strong trend toward interdisciplinary applications of artificial intelligence, with particular emphasis on transfer learning approaches that can adapt models across different domains and conditions. The research consistently demonstrates practical implementation value, with numerous applications in agricultural monitoring, medical diagnostics, and autonomous vehicle technologies. This work often involves collaboration with large research teams across multiple institutions, reflecting the complex, multidisciplinary nature of the problems being addressed. Dr. Cheng actively mentors numerous researchers, with several students appearing as co-authors on publications. Their research group appears to focus on developing practical AI solutions that can be deployed in real-world settings, particularly in agricultural and medical contexts where precise monitoring and analysis are critical.

• Wireless Communications
• Satellite Networks
• Machine Learning for Communications
• +6 more

Hayssam Dahrouj is a Professor in the Department of Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), where he leads research in wireless communications and next-generation networks. His work spans satellite communications, 5G/6G technologies, machine learning applications in communications, and integrated space-air-ground networking systems. He directs research at KAUST's Center of Excellence for NEOM Research, focusing on cutting-edge communication solutions for future smart cities and infrastructure. Dr. Dahrouj earned his PhD in 2010 from the University of Toronto's Department of Electrical and Computer Engineering. His research interests encompass Wireless Communications, Satellite Networks, Machine Learning for Communications, 5G/6G Networks, Non-Orthogonal Multiple Access, Cloud Radio Access Networks, and Terrestrial-Aerial Integrated Networks. His work bridges theoretical analysis with practical implementation, addressing real-world challenges in next-generation communication systems. Analysis of Dr. Dahrouj's recent publications reveals a strong focus on integrated communication architectures combining satellite, aerial, and terrestrial networks. His research increasingly incorporates machine learning techniques for network optimization, with growing emphasis on equitable resource allocation, extended reality applications, and energy-efficient network design. The interdisciplinary nature of his work spans communications theory, signal processing, and AI applications. Dr. Dahrouj maintains an extensive collaborative network with researchers across KAUST and internationally. He has supervised numerous graduate students and postdoctoral researchers, though specific names are not documented in the available information. His research has been supported by various grants focused on next-generation communication systems and smart infrastructure development. His laboratory at KAUST focuses on wireless communications systems, with particular emphasis on integrated space-air-ground networks, machine learning for communications, and next-generation network architectures. Current projects include satellite-terrestrial integration, NOMA systems, and edge computing applications for extended reality services.

• Machine Learning
• Deep Learning
• Cybersecurity
• +7 more

Amandeep Kaur is a researcher with affiliations across multiple institutions including the University of Cambridge , IIT Delhi , and Central University of Punjab . Her work spans interdisciplinary fields such as machine learning , deep learning , cybersecurity , and biomedical signal processing . Her research focuses on solving complex problems in healthcare, network security, and agricultural technology. Key contributions include optimizing SDN environments for DDoS attack detection, advancing 6G IoMT applications, and developing deep learning models for rice disease detection and COVID-19 classification using X-rays. The 15 most recent articles highlight her expertise in multi-objective optimization , hybrid algorithms , and federated learning for privacy protection. Topics range from smart city management and blockchain security to medical image segmentation and sentiment analysis in multilingual contexts.

Phaedon-Stelios KoutsourelakisView profile

Professor

• Uncertainty Quantification
• Multiscale Modeling
• Computational Mechanics
• +3 more

Prof. Phaedon-Stelios Koutsourelakis is a Professor of Continuum Mechanics at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. His research focuses on computational strategies for modeling and analyzing physical systems with a strong emphasis on uncertainty quantification, multiscale phenomena, and stochastic systems. He holds a PhD from Princeton University and has held academic positions at institutions including Cornell University, Heriot-Watt University, and the Lawrence Livermore National Laboratory. His educational background includes a degree from the National Technical University of Athens and a PhD from Princeton University (1998). Key research areas include data-driven coarse-graining techniques, Bayesian inference in engineering systems, and physics-aware machine learning models for high-dimensional problems. He has received notable awards such as the Top Teaching Trophy from the Munich School of Engineering (2014–2016) and the Dean’s First Year Merit Prize at Princeton (1998). His work bridges computational mechanics with statistical methods, addressing challenges in materials design, structural health monitoring, and predictive modeling under uncertainty. Recent projects include developing physics-constrained deep learning frameworks for surrogate modeling and quantifying model bias in digital twins of infrastructure systems. Notable contributions include the Predictive Coarse-Graining framework and probabilistic methods for uncertainty quantification in heterogeneous media. His research also extends to optimization under uncertainty and interdisciplinary applications in biomedical engineering and materials science.

Christian StemmerView profile

Professor

• Hypersonic Aerodynamics
• Fluid Mechanics
• Thermal and Chemical Nonequilibrium Flows
• +3 more

Prof. Christian Stemmer is a Professor of High-Speed Aerodynamics at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design and the Department of Aerodynamics and Fluid Mechanics. His research focuses on hypersonic flows, boundary layer transition, thermal and chemical nonequilibrium phenomena, and research data management. Stemmer holds a Dr.-Ing. habil. and has extensive international experience, including postdoctoral work at Stanford University and NASA Ames. Career highlights: Appointment as extraordinary professor in 2019, leadership roles in the Collaborative Research Center TRR40 (SFB/TRR40), and membership in editorial boards such as Advances in Aerodynamics . Awards: 2020 NATO AVT Panel Excellence Award for contributions to hypersonic flow research. Key projects: Investigation of hypersonic boundary layer transition under high-enthalpy conditions, development of numerical models for rocket combustion chambers, and leadership in national and international research consortia. His work bridges fundamental fluid mechanics with aerospace engineering applications, emphasizing high-performance computing and data-driven methodologies. Recent contributions include studies on roughness-induced instabilities, shock-wave interactions, and metadata extraction frameworks for HPC workflows.

Berna Özkale EdelmannView profile

Professor

• Nano-robotics
• Regenerative Medicine
• Biomaterials
• +3 more

Prof. Berna Özkale Edelmann is a Professor of Nano- and Microrobotics at the Department of Electrical Engineering and Computer Engineering, Technische Universität München (TUM). She leads research in developing nanorobotic tools for understanding cell-microenvironment interactions, leveraging nanotechnology, smart biomaterials, and microfluidics for regenerative medicine. Her work integrates robotics, materials science, and biology to create advanced microdevices for biomedical applications. Education: Bachelor's in Chemical Engineering, Istanbul Technical University (2009) Master's in Biomedical Engineering, ETH Zurich (2011) Doctorate in Mechanical Engineering, ETH Zurich (2016) Postdoctoral Researcher, Mooney Lab, Harvard University (2017) Research Interests: Her interdisciplinary research focuses on nanorobotics, biomaterials, and mechanotransduction. Key areas include designing microgels for stem cell cultivation, developing modular robotic microdevices, and applying thermal/laser-driven microrobots for cell stimulation. She emphasizes translating materials science into clinical tools for regenerative medicine and drug delivery. Awards: European Research Council (ERC) Starting Grant (2024) Early Postdoc Mobility Fellowship (Swiss National Science Foundation, 2016) Advising/Grants: While specific grants or advisees are not detailed, her ERC Starting Grant underscores significant research funding. Her lab’s work aligns with TUM’s focus on computational and technological innovation in biomedical engineering. Labs/Teams: Her lab at TUM develops novel robotic and biomaterial systems, collaborating across disciplines to advance nanomedicine and cellular engineering.

Sonja BerensmeierView profile

Professor

• Biofunctional Materials
• Process Development
• Biomolecule Separation
• +3 more

Prof. Sonja Berensmeier is a Professor of Bioseparation Engineering at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. She leads the TUM WACKER Institute for Industrial Biotechnology and is a director at the Munich Institute of Integrated Materials, Energy and Process Engineering (MEP). Her research focuses on biofunctional materials, magnetic/conductive materials, and process simulation for biomolecule separation. After earning her doctorate in Chemical Engineering from TU Braunschweig, she held roles at Karlsruhe Institute of Technology and Mannheim University of Applied Sciences before joining TUM in 2009. She has international experience at institutions like MIT and ETH Zurich. Her work emphasizes interdisciplinary collaboration between material science, biotechnology, and process engineering. Key achievements include developing magnetic nanoparticle-based separation technologies and optimizing bioprocesses. She has received awards for teaching and doctoral supervision, including the 2019 Supervisor Advisory Award and a teaching trophy. Prof. Berensmeier actively supports early-career scientists through advisory roles, such as with the TUM:Junge Akademie. Her research spans biomolecule purification, nanoparticle applications, and sustainable processing solutions for industries like food and pharmaceuticals.

Michael W. GeeView profile

Professor

• Numerical Simulation Methods
• Fluid-Structure Interaction
• Biomechanics
• +4 more

Prof. Michael W. Gee is a Professor of Mechanics on High-Performance Computing at the Department of Engineering Physics and Computation, Technische Universität München (TUM). He leads the Mechanics & High Performance Computing Group within the TUM School of Engineering and Design. His research focuses on numerical simulation methods for mechanical and physical phenomena, with a specialization in fluid-structure interaction, biomechanics, and uncertainty quantification. He earned his PhD in Civil Engineering from the University of Stuttgart and completed a postdoc at Sandia National Laboratories in the USA before joining TUM in 2006. He has held leadership roles including Academic Program Director of 'Interdisciplinary Engineering' (2021-2024) and Dean of Studies at the Munich School of Engineering (2016-2019). Education: Bachelor's/Master's in Civil Engineering, University of Stuttgart PhD in Civil Engineering, University of Stuttgart Habilitation and Group Leader position at TUM Research interests include computational models for cardiovascular mechanics, medical device simulation, and advanced numerical methods. His work bridges engineering and medicine, with applications in endovascular repair, atherosclerosis modeling, and cardiac mechanics. He has advised over 20 PhD students on topics ranging from blood coagulation to meshfree methods. Prof. Gee is a member of the German Association for Computational Mechanics (GACM) and the International Association of Applied Mathematics and Mechanics (GAMM). Notable awards include the Arburg Preis 2021 and Julius Springer Award 2020. His lab develops the BACI open-source simulation environment and collaborates on projects like the 'digital twin' for cardiovascular treatments.

Markus KowarschikView profile

Researcher

• Interventional Imaging
• Blood Flow Quantification
• Perfusion Imaging
• +7 more

Dr. Markus Kowarschik is a researcher affiliated with the Chair of Computer Science Applications in Medicine at Technical University of Munich under Prof. Nassir Navab. His work focuses on interventional imaging, blood flow quantification, and tomographic reconstruction. He contributes to labs like DHM (Deutsches Herzzentrum München), NARVIS, and IFL, advancing medical imaging and AI applications in healthcare. His teaching includes courses on medical procedures, robotics, and deep learning in medical contexts. Recent research emphasizes AI-driven solutions for endovascular procedures, motion compensation in imaging, and 3D pose estimation. His research interests span medical image analysis, computer vision, and generative models applied to surgical data science. Key projects involve developing datasets for benchmarking, improving X-ray imaging systems, and integrating simulation for medical training. He has no listed awards but maintains an active publication record in top-tier journals and conferences. Labs and collaborations include the NARVIS Lab (navigation and robotics), DHM for cardiac imaging, and GenAI initiatives exploring generative models. His work bridges clinical needs with computational methods, addressing challenges in radiation dosimetry, robotic control systems, and real-time imaging analysis.

Stephan G. NekollaView profile

Associate Professor

• Medical Physics
• Biomedical Imaging
• Biophysics
• +7 more

Stephan G. Nekolla is an Associate Professor at the Technical University of Munich, where he works at the Clinic and Polyclinic of Nuclear Medicine. With a career spanning over three decades in medical physics and imaging, he has established himself as a leading researcher in the field of nuclear medicine and cardiovascular imaging. Dr. Nekolla's educational background includes training at the University of Wuerzburg, where he worked as a Physicist from June 1989 to June 1993. Since May 1993, he has been affiliated with the Technical University of Munich, initially as a Senior Physicist before advancing to his current position as Associate Professor. His primary research interests focus on the intersection of medical physics and clinical applications, with particular emphasis on: Medical Physics and Biophysics Biomedical Imaging and Diagnostic Imaging Computed Tomography and Magnetic Resonance Imaging Medical Image Analysis and Processing Cardiology applications of nuclear medicine Dr. Nekolla's recent publication record demonstrates a strong focus on hybrid imaging techniques, particularly PET/MRI applications in cardiology and oncology. His work spans multiple areas including myocardial viability assessment, cardiac sarcoidosis imaging, prostate cancer diagnostics, and radiation therapy optimization. A significant portion of his research involves developing and validating new imaging protocols, quantification methods, and machine learning approaches to enhance diagnostic accuracy and patient outcomes. As the leader of his research lab at the Technical University of Munich, Dr. Nekolla has built a multidisciplinary team focused on advancing nuclear medicine techniques and their clinical applications. His laboratory work bridges the gap between fundamental physics research and practical clinical implementation, with a particular emphasis on improving cardiac and oncological imaging.

• Molecular Communication Networks
• 6G Networks
• Medical Applications of Communication
• +3 more

Alexander Wietfeld is a Researcher at the Chair of Communication Networks, Technische Universität München (TUM), under Prof. Wolfgang Kellerer. He pursues a PhD focusing on molecular communication networks for medical and biological applications, particularly within the 6G-life project. His work explores diffusion-based communication systems for in-body medical sensing and 6G network advancements. Education: B.Sc. in Electrical Engineering (2021) from RWTH Aachen and Imperial College London; M.Sc. in Communication Engineering (2023) from RWTH Aachen, with a thesis on hybrid beamforming in millimeter-wave cellular networks. Research interests include molecular communication systems, non-orthogonal multiple access (NOMA), and biomedical applications such as arterial plaque detection. He has contributed to projects like the 6G Testbed Bavaria and the ERC Network Flexibility initiative. Key publications address molecular communication for medical diagnostics, interference management in bio-nano networks, and testbed validation. His work bridges communication engineering with biomedical science, targeting future 6G and bio-nano network integration. Labs/Teams: Active in the FlexComNetsLab and 6G-IP Lab, collaborating with institutions like Northeastern University and Universitat Politècnica de Catalunya.

Alfonso CaiazzoView profile

Researcher

• Mathematical Modelling of Biological Flows
• Finite Element Methods
• Agent-Based Modeling
• +4 more

Dr. Alfonso Caiazzo is a researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, leading the Numerical Mathematics and Scientific Computing group. Since 2010, he has held a postdoctoral researcher position at WIAS, focusing on computational methods for biomedical and engineering applications. His work integrates mathematical modeling, numerical analysis, and scientific computing to address challenges in fluid dynamics, tissue mechanics, and multiscale systems. Caiazzo holds a joint PhD from Scuola Normale Superiore di Pisa and TU Kaiserslautern (2006-2007), with prior postdoctoral experiences at institutions including the University of Trento, INRIA, and the University of Amsterdam. His research interests span mathematical modeling of biological flows, finite element methods, agent-based modeling, and lattice Boltzmann techniques. He has contributed to projects such as the MATH+ initiative (Berlin Mathematics Research Center) and the EuroHPC-funded dealii-X framework, focusing on exascale computing for biomedical applications. Key collaborations include work with Prof. D. Peterseim (University of Augsburg) on poroelasticity and multiscale methods, and Prof. I. Sack on magnetic resonance elastography. Caiazzo has taught courses at Freie Universität Berlin, including Linear Algebra for Physicists and Mathematics for Geosciences. His funded projects emphasize data-driven reduced-order models, computational hemodynamics, and cancer growth modeling. Current research includes inverse estimation in poroelastic tissues, multiscale coupling of vascularized tissues, and exascale framework development for digital twins of the human body.