Prof. Dr. Petra Schwille is a Director at the Max Planck Institute of Biochemistry and former C4 Professor of Biophysics at Dresden University of Technology . Her work spans molecular and cellular biophysics, synthetic biology, and single-molecule techniques. Academic disciplines: Natural sciences, biological sciences, physical sciences Key roles: Editorial Board member (Nature Methods, Biophysical Journal), Governing Council of Biophysical Society Research Focus includes membrane biophysics, protein interactions, and microfluidic systems. Her publications emphasize Fluorescence Correlation Spectroscopy (FCS) , receptor-ligand dynamics, and self-organization in bacterial cell division. Developed in vitro models for spatial regulation in cells Explored calmodulin availability and morphogen gradient formation Scientific Recognition includes prestigious awards like the Gottfried Wilhelm Leibniz Prize (2010) and the Biofuture grant (1998) . Max Planck Fellow (2005) Young Investigator Award (2003) Leadership & Service involves roles such as Dean of Studies for Nanobiophysics at TU Dresden and Vice Dean of the Dresden International Graduate School for Biomedicine and Bioengineering (DIGS-BB) . She also contributes to editorial and advisory boards in biophysics and science policy.
Mustafa Kahya is a Scientific Staff member and Ph.D. candidate at the Chair of Media Technology within the Munich Institute of Robotics and Machine Intelligence (MIRMI) at the Technical University of Munich (TUM). He works under the supervision of Prof. Dr.-Ing. Eckehard Steinbach and is actively involved in research related to radar systems and machine learning. His academic background includes a B.Sc. in Computer Engineering from Istanbul Technical University (2017) and an M.Sc. in Informatics from TUM (2021). During his master's studies, he conducted research on 3D Reconstruction and Multi-view Shape from Shading at the TUM Computer Vision Group. Kahya's research focuses on Radar Image Analysis , Out-of-distribution Detection , One-Class Deep Neural Networks , Anomaly Detection , and Generative Models . His work primarily centers on applying deep learning techniques to short-range FMCW radar systems for various applications including human presence detection, facial authentication, and activity recognition. His publications demonstrate a strong trend toward real-time radar-based systems with emphasis on out-of-distribution detection capabilities. Kahya has been actively publishing in top-tier conferences and journals from 2023 through 2025, with multiple first-author publications in IEEE venues including ICASSP, ICIP, and IEEE Sensors. His research has been part of several significant projects including the Centre for Tactile Internet with Human-in-the-Loop (CeTI) and DFG-funded research on Teleoperation over 5G. As a Ph.D. candidate at the Chair of Media Technology, Kahya contributes to the research group's work in computer vision, machine learning, and radar systems. His work bridges the gap between traditional computer vision techniques and novel radar-based sensing modalities, creating opportunities for applications in environments where optical systems face limitations.
Mehrdad Salehi is a researcher at the Chair of Computer Science Applications in Medicine at the Technical University of Munich (TUM) . His work focuses on the intersection of computer science and medical imaging, with expertise in ultrasound technology, deep learning, and surgical navigation systems. Key research areas include sonification of medical data, 3D ultrasound reconstruction, and machine learning-based segmentation. He has contributed to innovative projects like PRO-TIP calibration phantoms and ColibriDoc autonomous docking systems. His publications highlight trends in acoustic feedback mechanisms, neural radiance fields for medical imaging, and real-time image analysis. He can be reached at mehrdad.salehi@tum.de .
Dr. Jing Wang is a Professor in the Department of Bioinformatics at Southern Medical University's School of Medicine, with extensive research at the intersection of artificial intelligence and biomedical applications. Her work demonstrates strong cross-disciplinary collaboration across medical institutions, engineering departments, and computer science research groups. Her primary research interests include Artificial Intelligence in Healthcare , Biomedical Engineering , and Traditional Chinese Medicine Informatics , with recent publications showing particular expertise in medical imaging analysis, diagnostic assistance systems, and clinical decision support. Her work spans both theoretical algorithm development and practical clinical implementations. Analysis of her 15 most recent publications (2025-2026) reveals a strong trend toward clinically applicable AI systems, with approximately 60% of publications focused on medical diagnostics and treatment support systems. The remaining publications demonstrate expertise in industrial applications of computer vision and fundamental AI research. Her work shows consistent collaboration with both domestic Chinese institutions and international research groups. Notable scientific contributions include: Development of 'Tianyi', a traditional Chinese medicine language model for clinical practice Innovations in bionic soft robotics for rehabilitation assistance Novel approaches to medical image analysis for cancer diagnostics Her research program appears well-funded with consistent publication output across high-impact journals in biomedical engineering, AI, and medical informatics. Current work suggests strong emphasis on translating AI research into clinical practice, particularly in diagnostic support systems and rehabilitation technology.
Volker J Schmid is a Professor of Bayesian Imaging and Spatial Statistics at the Department of Statistics, Ludwig Maximilian University of Munich. He leads the Bayesian Imaging and Spatial Statistics group and contributes to interdisciplinary initiatives like the Munich Center of Machine Learning. His work bridges statistical theory with applications in medical imaging and biology. PhD in Statistics (2004), LMU Munich Diploma in Statistics (2000), LMU Munich Abitur, Joseph-von-Fraunhofer-Gymnasium Cham (1993) His research focuses on Bayesian computational methods for high-dimensional data, particularly in medical imaging (MRI, DCE-MRI) and biological microscopy (e.g., 3D nuclear architecture analysis via super-resolution microscopy). Key applications include disease mapping , image segmentation , and spatio-temporal modeling . His software tools (e.g., nucim , bioimagetools , BAMP ) enable quantitative analysis in nuclear imaging and age-period-cohort modeling. His 15 most recent publications span Bayesian modeling for medical imaging , spatio-temporal epidemiology , and computational biology . Topics include co-localization metrics in fluorescence microscopy, nuclear architecture analysis, and dynamic Bayesian frameworks for MRI data. Collaborations extend to neuroimaging, oncology, and nuclear biology.
Dr. Burkhard Maess is a Research Professor and Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences, leading the Methods and Development Group Brain Networks. His research focuses on auditory and language processing, signal analysis, and dynamic modeling of neuronal networks. He holds a Diploma in Physics (University of Leipzig, 1987) and a PhD in Physics (University of Leipzig, 1990). His career includes postdoctoral positions at the Academy of Sciences of the GDR and the Free University of Berlin before joining the MPI in 1995. Since 2000, he has led research groups on MEG/EEG signal analysis and cortical network dynamics. His work integrates advanced neuroimaging techniques like MEG and EEG to study sensory processing, neural network dynamics, and the effects of aging on auditory attention. Key contributions include developing high-resolution BEM-FMM methods for source localization and analyzing cross-frequency coupling in neuroscience data. His group also explores spinal cord electrophysiology and the neural underpinnings of perceptual decision-making. Dr. Maess’ research spans cognitive neuroscience, biomedical engineering, and computational modeling, with a focus on bridging empirical findings with theoretical frameworks in neuroscience.
Dr. Alexander Paulus serves as a Researcher at the Chair of High-Frequency Engineering within the Department of Electrical Engineering at the Technical University of Munich (TUM), School of Computation, Information and Technology. Working under Prof. Dr.-Ing. Thomas Eibert, he contributes to advanced electromagnetic research and measurement systems development at TUM's Arcisstr. 21 campus in Munich. Research Expertise His core specialization lies in near-field antenna measurement and transformation techniques, with significant contributions to phase retrieval algorithms, inverse source methods, and UAV-based electromagnetic field measurements. He addresses critical challenges including probe correction with unknown antennas, sparse sampling for directive antennas, and electromagnetic modeling of environmental effects like rain attenuation. His work bridges theoretical electromagnetics with practical antenna characterization solutions. Publication Trends From 2014-2025, Paulus has published 25+ papers focusing on near-field to far-field transformations, particularly in phaseless and multi-probe scenarios. Recent work (2023-2025) demonstrates innovation in spectral filtering, sparse reconstruction, and UAV-based systems for defect localization and wet antenna modeling. His research increasingly integrates computational techniques to solve complex inverse problems in antenna measurements. Scientific Recognition No formal awards documented in available information Academic Contributions Student Mentoring: No advisees listed in provided materials Research Funding: Grant details not specified in source text Research Environment Paulus operates within TUM's Chair of High-Frequency Engineering facilities, which include advanced near-field measurement ranges, UAV-based electromagnetic characterization systems, and laboratories for metamaterials research and electromagnetic compatibility testing. His work supports applications in 5G/6G communications, aviation navigation systems, and precision antenna diagnostics.
Christian Wald is a Post-doctoral researcher at Technical University Berlin working under Professor Gabriele Steidl, focusing on generative modeling, flow matching, and stochastic processes in machine learning. His research bridges theoretical probability with practical medical imaging applications, particularly in MRI reconstruction and analysis. He completed his PhD at Humboldt University of Berlin in 2017 with a thesis on p-adic quantum groups. His academic journey transitioned from pure mathematics to interdisciplinary machine learning research, reflecting his versatile expertise. Wald's primary research explores generative models through the lens of optimal transport and flow matching, with significant contributions to Wasserstein geometry and conditional distance metrics. His work frequently integrates stochastic processes to enhance medical image reconstruction, demonstrating strong cross-disciplinary impact in both theoretical machine learning and clinical applications. Recent publications highlight innovations in sliced MMD flows, Bayesian OT methods, and uncertainty-aware medical image analysis. Analysis of his 15 most recent publications (2019-2025) reveals a consistent trajectory toward unifying geometric probability with deep learning. Key themes include flow-based generative modeling for medical time-series data, optimal transport applications in image reconstruction, and novel kernel methods for distribution matching. His work spans both foundational theory (e.g., Fisher-Rao curves) and high-impact medical applications (e.g., coronary calcium scoring). No specific scientific awards are documented in the provided text, though his publications appear in prestigious venues including ICLR, IEEE TMI, and Physics in Medicine & Biology. Wald maintains extensive collaborations with the medical imaging group at Technical University Berlin, particularly with Andreas Kofler and Gabriele Steidl. His co-authored works demonstrate consistent contributions to MRI reconstruction pipelines and segmentation frameworks, though no formal advising roles or grant leadership are indicated. Current projects focus on uncertainty quantification in active learning for medical image segmentation. He operates within Gabriele Steidl's research group at Technical University Berlin, which specializes in mathematical imaging and machine learning. The team combines expertise in optimization, probability theory, and deep learning to solve medical imaging challenges, with Wald contributing core algorithmic innovations in generative modeling and stochastic reconstruction.
Prof. Julia Herzen holds the Associate Professorship of Physics in Biomedical Imaging at the Department of Physics , TUM School of Natural Sciences , Technical University of Munich . Her research focuses on advancing X-ray imaging techniques using synchrotron radiation and laboratory sources, with applications in medical diagnostics and tissue analysis. Position: Associate Professor Department: Physics School: TUM School of Natural Sciences University: Technical University of Munich Contact: julia.herzen@tum.de Her core research interests include: Quantitative multi-modal X-ray imaging (spectral & phase-contrast) 3D virtual histology of human tissue Breast cancer detection improvement Lung disease imaging (emphysema, pneumonia) X-ray phase-contrast tomography Dark-field imaging material decomposition Recent publications demonstrate expertise in dark-field imaging for lung pathology , phase-contrast CT for organoid visualization , and spectral X-ray applications in multi-material differentiation . Her team explores clinical translation of X-ray techniques for non-invasive diagnostics . She supervises PhD students and teaches Biomedical Engineering courses, including: Quantitative X-Ray Imaging (3 VI) Image Processing in Physics (2 VO) Biostatistics (2 VO) Advanced Lab Courses in X-ray Micro-CT
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
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.
Matthias Kuhl is a Professor at the Institute of Microsystems Technology (IMTEK) at the University of Freiburg since April 2022. He leads research projects focused on neural probes, biomedical implants, and integrated microelectronic systems. His work includes developing low-power neural interfaces, stress sensors, and energy-efficient circuits for medical applications. Research Interests Neural probes with electronic depth control Implantable biomedical devices CMOS integrated sensors and actuators Energy harvesting for autonomous systems Microfabrication and 3D-printed electronics Key Projects Advanced EDC: Intracortical neural probes with electronic depth control ComBiNE: Bidirectional neural exchange components SEAM-WiT: Implantable neural probe transceivers Multi-material 3D-printed electronics His recent publications emphasize low-power neural front-ends, stress sensor integration, and biomedical system design. He advises numerous graduate students on topics ranging from CMOS circuit design to biohybrid systems. Labs & Teams He leads the Professur für Mikroelektronik lab, specializing in microelectronic systems for biomedical and industrial applications. Collaborates with orthodontic, neurobiology, and materials science groups.
Brenda Schulman is a Professor and Director of the Molecular Machines and Signaling Pathways department at the Max Planck Institute of Biochemistry in Martinsried, Germany. She also holds an honorary professorship at the Technical University of Munich's Department of Chemistry and serves as Adjunct Faculty at St. Jude Children's Research Hospital in Memphis, TN, USA. Her research focuses on understanding how ubiquitin and ubiquitin-like proteins regulate cellular processes through protein modification. Dr. Schulman's research interests center on structural biology of the ubiquitin-proteasome system and ubiquitin-like proteins. Her work has shown that hundreds of dynamic multiprotein complexes are transiently converted into different conformations by specialized regulatory factors that control ubiquitin and ubiquitin-like proteins, thereby monitoring virtually all processes in cell biology. She combines biochemical reconstitution, structural analysis, enzymology, protein design, cell biology, and genetics to understand how these molecular machines function. Her research has significant implications for understanding diseases such as cancer, neurodegenerative disorders, and viral infections where defects in ubiquitin pathways are implicated. Her extensive publication record demonstrates expertise in ubiquitin signaling, protein degradation mechanisms, structural biology of E3 ligases, and molecular machines. Her work spans from fundamental mechanisms of ubiquitin chain formation to therapeutic applications in targeted protein degradation. Among her numerous scientific accolades are the Feldberg Prize for Anglo-German Scientific Exchange (2025), ERC Advanced Grant (2023), Louis-Jeantet Prize for Medicine (2023), Gottfried Wilhelm Leibniz Prize (2019), and election to the National Academy of Sciences (2014). She has also received the Dorothy Crowfoot Hodgkin Award from The Protein Society and has been an Investigator of the Howard Hughes Medical Institute. Dr. Schulman leads an active research group that has produced numerous high-impact publications in top journals including Nature, Cell, and Nature Structural & Molecular Biology. Her team has made significant contributions to understanding the structural mechanisms of ubiquitin transfer, E3 ligase specificity, and the role of ubiquitin in cellular quality control pathways. Current research in her lab focuses on deciphering the ubiquitin code and developing novel approaches for targeted protein degradation.
Prof. Thomas Kuner is a Professor and Director of the Department of Functional Neuroanatomy at the University of Heidelberg's Medical Faculty. He holds a medical degree (MD) from Heidelberg (1998) and completed postdoctoral work at Duke University and the Marine Biological Laboratory. Since 2000, he has led a research group at the Max Planck Institute for Medical Research, followed by habilitation in Physiology (2003) and appointment as Professor of Anatomy and Cell Biology (2006). Research Focus: His work focuses on neuroanatomy, synaptic transmission mechanisms, and pain research. Key projects include investigations into the structural and functional properties of synapses (e.g., calyx of Held), the role of presynaptic proteins like Mover, and the molecular basis of pain signaling via the SFB 1158 consortium. His lab uses advanced imaging techniques (e.g., STED microscopy) and genetic models to study neuronal circuits and synaptic plasticity. Funding & Collaborations: Kuner's research is supported by grants from the DFG (e.g., SFB 1158), the Baden-Württemberg Foundation, and other national/international bodies. His interdisciplinary approach bridges cellular neuroscience, molecular biology, and clinical applications in pain management. Teaching & Leadership: He oversees the Institute of Anatomy and Cell Biology, contributing to graduate programs in medical education and anatomy. His team includes postdocs and technicians, with collaborations extending to imaging technology development and medical education innovation.
Prof. Dr. Axel Mecklinger is a leading cognitive neuroscientist at Saarland University , specializing in the neurocognition of memory and language through spatiotemporal brain imaging (EEG/MEG/fMRI). His career spans over three decades, with significant contributions to understanding visual working memory , associative recognition , and memory development . Key research areas: Memory binding, ERP subsequent memory effects, novelty detection, and cognitive aging Major grants: DFG Research Groups, Collaborative Research Centers, and international collaborations with the Chinese Academy of Sciences Scientific leadership: Organized conferences, edited journals, and served as speaker for research training groups His recent work explores unitization in memory formation , cross-cultural differences in memory processing , and theta neurofeedback interventions . Awards include the Early Career Award of the German Psychophysiology Society (1992) and the European Federation of Psychophysiology Societies' Federation Prize (1994). Current projects investigate the neural mechanisms of semantic surprisal and memory plasticity in aging populations.
