Prof. Dr.-Ing. Ahmad Osman is a Professor at the Saarland University of Applied Sciences (htw saar), specializing in Test Technologies and Test Methods within the Faculty of Engineering. He also holds an Adjunct Professor position at Laval University in Quebec, Canada, in the Department of Electrical Engineering and Computer Science. His research focuses on Artificial Intelligence applications in Signal and Image Processing for Non-destructive Testing (NDT) , with extensive work on Deep Learning , 3D Ultrasound Tomography , and Sensor Data Fusion in industrial contexts. Engineering Artificial Intelligence Signal Processing Image Processing Non-destructive Testing Quality Control Augmented Reality Osman leads the AutomaTiQ research group and serves as Head of the Algorithms/Signal and Data Processing Department at Fraunhofer IZFP . His recent publications (2017–2022) emphasize Deep Learning for defect detection in CFRP , Terahertz Imaging for artwork diagnostics, and Acoustic Sensors for agricultural quality control. He has organized international conferences on Structural Health Monitoring and contributed to Springer books on NDT technologies. His projects include ComforTex-AI (2024) and development of 3D positioners for ultrasound measurements. Collaborations span institutions in Germany, Canada, Italy, and Brazil, with advisory roles in the German Society for NDT and technical committees for conferences in Montreal and Egypt.
Dongwoo Kim is a researcher affiliated with Hanyang University, ERICA Campus (Department of Electronics and Communication Engineering) and has previously collaborated with institutions like POSTECH , Chungnam National University , and Microsoft . His work spans interdisciplinary domains in Computer Science and Engineering . Hanyang University, ERICA Campus - Department of Electronics and Communication Engineering POSTECH - Power Analog Electronics & Semiconductor Devices Lab Microsoft Chungnam National University Kim's research focuses on formal verification of automotive control software, deep learning applications in environmental monitoring, 3D modeling for indoor positioning, and machine learning for signal processing. His recent publications highlight advancements in graph neural networks (GNNs), including analyzing oversmoothing and gradient dynamics, as well as developing geometric vision-language models with domain-agnostic encoders. His 15 most recent articles (2023-2025) address topics like: Optimizing hybrid electric vehicle engine performance 3D modeling for indoor localization GNN training stability UAV-based environmental monitoring Algorithm difficulty prediction for programming problems Millimeter-wave antenna design Kim collaborates with researchers in software engineering , signal processing , and environmental science domains. His work intersects formal methods , applied machine learning , and embedded systems research.
Lukas Hiendlmeier is a Researcher at the Technical University of Munich, affiliated with the Munich Institute of Biomedical Engineering (MIBE) and the Associate Professorship of Neuroelectronics led by Prof. Bernhard Wolfrum. He holds a Master of Science in Mechanical Engineering from TUM. His research focuses on advanced fabrication technologies such as 3D printing, laser micromachining, and polymer material science, with applications in neuroelectronics and biomedical devices. Hiendlmeier’s work emphasizes developing self-folding bioelectronic interfaces, flexible electrodes, and implantable neural devices for peripheral nerve interfacing. His contributions include innovations in 4D printing techniques, thermoformed materials, and origami-inspired electrode designs. He collaborates on projects involving cell manipulation, microfluidic lab-on-a-chip systems, and closed-loop neural stimulation systems. Publications span topics like self-folding bioelectronics, flexible sensor arrays, and nanorobotics, showcasing expertise in materials science and biomedical engineering. His research bridges fundamental science and translational applications, addressing challenges in neural prosthetics, wearable diagnostics, and tissue engineering. Hiendlmeier is actively involved in the neuroTUM initiative and contributes to interdisciplinary teams at TUM, focusing on advancing neurotechnology through innovative fabrication methods and biomaterials.
Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.
Sebastian Möller is a Professor at the Faculty for Electrical Engineering and Computer Science at Technical University of Berlin and leads the Speech and Language Technology research department at the German Research Center for Artificial Intelligence (DFKI) since 2017. His work bridges speech technology, natural language processing, and quality engineering, with a focus on usable security, virtual reality applications, and ethical AI. Born in 1968, studied electrical engineering at Ruhr University Bochum, Université d'Orléans, and University of Bologna Doctorate in 1999 from Ruhr University Bochum on speech quality prediction Adjunct Professor at University of Technology Sydney since 2018 His research spans speech signal processing, quality assessment of AI systems, and ethical challenges in medical AI applications. Recent work includes fairness in clinical AI decision-making, audio deepfake detection using self-supervised learning, and collaborative approaches to natural language explanation generation. All three 2025 publications reflect his focus on AI quality assessment and ethical implementation across domains: medical AI fairness, audio deepfake detection, and collaborative explanation frameworks. These works demonstrate expertise in natural language processing, speech technology, and model interpretability. Johann-Philipp-Reis-Prize (2009) Heisenberg Scholarship (2005) Lothar Cremer Prize (2003) VDE ITG Prize (2001) Geers Foundation award (1998) Möller has held leadership roles at TU Berlin including Vice Dean for Research (2015-2017) and Dean (2017-2019). He contributes to international standardization through ITU-T (since 1997) and has served in key positions at DEGA, VDE, and ISCA, including ISCA presidency (2021-2023).
Prof. Margret Keuper is a Professor of Machine Learning at the University of Mannheim's School of Business Informatics and Mathematics, leading the Data and Web Science Group. She is also affiliated with the Max-Planck-Institute for Informatics and ELLIS (fellow since 2024). Her research focuses on robust deep learning, neural architecture search, and computer vision tasks like motion segmentation and adversarial defense. She holds a PhD from the University of Freiburg and previously held positions at the University of Siegen and the University of Mannheim. Her work spans projects funded by DFG and BMBF, including Climate Visions for social media analysis and TrackOpt for motion tracking. She teaches courses on computer vision, generative models, and reinforcement learning. She actively serves on program committees for top conferences like CVPR, ECCV, and NeurIPS, and is an associate editor for IEEE TPAMI and JAIR. Education: PhD in Computer Science from University of Freiburg (advisor: Thomas Brox) Research Projects: Learning to Sense (DFG), Climate Visions (BMBF), TrackOpt (BMBF) Key Roles: Head of Mannheim Master in Data Science Examination Board, Member of MSc Business Informatics Board Her research emphasizes robustness in AI systems, with contributions to adversarial attacks, domain generalization, and efficient solvers for large-scale problems. She advises over 15 PhD students across academic and industry partnerships.
Michael Schaub is a tenure-track Assistant Professor in the Department of Computer Science at RWTH Aachen University, specializing in Computational Network Science. His research focuses on analyzing complex systems through network and graph models, integrating dynamical systems, control theory, and machine learning. He leads the Computational Network Science group, advancing methodologies for higher-order network models like simplicial complexes and hypergraphs. Schaub holds a PhD from Imperial College London and has held postdoctoral positions at MIT and Oxford. He is an ERC Starting Grant recipient (2022) and a Marie Curie Fellow, recognized for contributions to network dynamics and topological data analysis. Education: PhD in Mathematics, Imperial College London (2011-2015) MSc in Biomedical Engineering, Imperial College London (2010) BSc in Electrical Engineering, ETH Zurich (2007-2010) Research Interests: Schaub’s work spans interdisciplinary applications of network science, including biological systems, social networks, and technical infrastructures. Key areas include: Higher-order network models (hypergraphs, simplicial complexes) Graph signal processing and dynamics on networks Community detection and dynamical systems analysis Topological data analysis and machine learning Grants & Awards: ERC Starting Grant (2022): HIGH-HOPeS project Marie Skłodowska-Curie Fellowship (2017-2019) Junior Fellow, German Informatics Society (GI) Member of Junges Kolleg (North Rhine-Westphalia Academy) Labs & Teams: Leads the Computational Network Science Lab at RWTH Aachen, collaborating internationally on projects like the ELLIS Society and the European Laboratory for Learning and Intelligent Systems (ELLIS). Active in organizing workshops (e.g., Toponets, SIAM MDS).
Nils Ole Tippenhauer is a faculty member at the CISPA Helmholtz Center for Information Security , leading the SCy-Phy research group . His academic journey includes an Assistant Professorship at the Singapore University of Technology and Design (SUTD) (2014–2018), a PhD in Computer Science from ETH Zurich (2012), and a Diploma in Computer Engineering from Hamburg University of Technology (2007). He also holds an exchange study year at the University of Waterloo (2004–2005) supported by a DAAD scholarship. Research Interests focus on practical systems security , particularly Security of Cyber-Physical Systems Industrial Control Systems (ICS) and Industrial IoT (IIoT) Physical-Layer Security and Wireless Security Privacy-Preserving Technologies (e.g., DP3T project) Embedded Systems Security Threat Detection & Defenses His work integrates both theoretical and applied approaches, addressing vulnerabilities in critical infrastructures like water systems and power grids. Recent Publications highlight trends in Anomaly detection evasion in industrial networks Microarchitectural side-channel defenses Localization techniques in cellular networks Security analysis of ICS protocols Data-driven vulnerability assessments for robotics Cyber-physical simulation for infrastructure security Scientific Awards include Best paper at DIMVA'23 Distinguished Paper at ACSAC 2022 Best paper at CPSIOTSEC 2022 Best paper at CPSS 2017 K-H Ditze Award for diploma thesis (2007) SG Mark Institution award (2016) Runner-Up at smart energy hackathon (2013) Advising and Grants feature PhD defenses of Daniele Antonioli and Hamid Reza Ghaeini (2022), co-organizing conferences like WiSec and CPSS , and leading projects such as the National Science Experiment (NSE) with 50,000 mobile sensors in Singapore. He also contributed to the DP3T project for privacy-preserving contact tracing. Labs and Teams include the SCy-Phy research group at CISPA and the development of testbeds like SWaT (Secure Water Treatment), WADI (Water Distribution), and EPIC (Electric Power and Industrial Control) at SUTD. These platforms enable hands-on security research in realistic industrial environments.
Dr. Angelika Rambold is a Group Leader at the Max Planck Institute of Immunobiology and Epigenetics in Freiburg, Germany, heading the Laboratory for Metabolic Organelle Networks in Immunology within the Department of Developmental Immunology. Previously affiliated with the University of Münster's Center for Molecular Biology of Inflammation (ZMBE) and Institute of Medical Biochemistry until January 2025, she investigates how intracellular organelle networks regulate immune cell function during inflammation, infection, and metabolic stress. Her research centers on dynamic interactions between mitochondria, lysosomes, lipid droplets, and autophagosomes during cellular adaptation to nutrient deprivation and pathogen challenge. Key interests include organelle communication mechanisms in immune cell activation, metabolic reprogramming in T cells and macrophages, and how defects in organelle networks drive primary immunodeficiencies like Chediak-Higashi syndrome. She employs advanced live-cell microscopy, super-resolution imaging, metabolomics, and single-cell transcriptomics to dissect these processes in primary immune cells and human disease models. Analysis of her publication record reveals consistent focus on mitochondrial dynamics as a central regulator of immune cell metabolism and fate determination. Landmark studies demonstrate TFEB-mediated itaconate synthesis for bacterial control in macrophages and coordinated organelle network responses during starvation, establishing critical links between organelle communication, immunometabolism, and disease pathogenesis across multiple immune cell types. Dr. Rambold serves as a supervisor in the Cells in Motion International Max Planck Research School (CiM-IMPRS) Graduate Programme, mentoring PhD students in interdisciplinary research. Her laboratory maintains active collaboration with the Center for Chronic Immunodeficiency (CCI) at the University of Freiburg to translate basic findings on organelle-mediated immune defects into clinical insights for patient-oriented research.
Tobias Dick serves as Professor and Head of the Division of Redox Regulation at the German Cancer Research Center (DKFZ) in Heidelberg, maintaining a primary affiliation with Heidelberg University's Faculty of Biosciences. His leadership spans molecular switch research within the SFB/TRR186 consortium focusing on spatio-temporal control of cellular signal transmission. Academic Background: PhD in Biochemistry, Freie Universität Berlin (1997, summa cum laude) Habilitation in Biochemistry, Heidelberg University (2009) Diploma thesis at German Cancer Research Center (1994) Study program in Biochemistry, Freie Universität Berlin (1989-1994) Research Focus: Dick pioneers investigations into thiol-based redox switches governing cellular signal transduction. His work establishes fundamental mechanisms of peroxiredoxin-mediated hydrogen peroxide signaling, protein persulfidation dynamics, and sulfur-based radical scavenging systems. Key contributions include developing real-time imaging probes for redox species and elucidating redox relays connecting peroxiredoxins to transcription factors like STAT3. Current research explores hydropersulfide protection against ferroptosis and metabolic adaptation through redox-sensitive enzymes. Publication Trends: Over 15 years of high-impact publications reveal an evolutionary trajectory from foundational redox imaging techniques (2008-2011) to sophisticated molecular mechanism studies (2013-2020), culminating in recent breakthroughs on sulfur signaling in cell death pathways (2023). His work consistently appears in premier journals like Nature Chemical Biology , demonstrating sustained innovation in redox biology methodology and conceptual frameworks. Scientific Recognition: ERC Advanced Grant (2017) Society for Free Radical Research Europe Basic Science Award (2017) Chica- and Heinz-Schaller-Award for young scientists (2009) Marie Curie Excellence Grant (2004) DFG Postdoctoral Fellowship (1998-2000) Studienstiftung des Deutschen Volkes Scholarship (1989-1994) Leadership & Mentorship: As founding vice-coordinator of DFG priority program SPP1710 (2014-present) and GBM Redox Biology Study Group (2011-2017), Dick shapes national research agendas. His division at DKFZ mentors next-generation scientists through ERC and DFG-funded projects, with trainees contributing to landmark publications on redox switches and cellular physiology. Research Infrastructure: The Division of Redox Regulation operates within DKFZ's state-of-the-art facilities, collaborating extensively through the SFB/TRR186 consortium. This environment enables cutting-edge investigations into redox-controlled cellular processes using advanced biochemical, imaging, and computational approaches.
Joachim Weickert is a Professor of Mathematics and Computer Science at Saarland University where he heads the Mathematical Image Analysis Group since 2001. He received his diploma and Ph.D. in mathematics from the University of Kaiserslautern (1991, 1996), and a habilitation degree in computer science from the University of Mannheim (2001). Prior to his current position, he worked as a research assistant at the University of Kaiserslautern, as a post-doctoral researcher at the universities of Utrecht and Copenhagen, and as an assistant professor at the University of Mannheim. His research focuses on image processing, computer vision, and scientific computing, with special emphasis on techniques based on partial differential equations, variational principles, wavelets, morphological and nonlocal methods, as well as neuroexplicit approaches. He has developed mathematical models and efficient numerical algorithms for image restoration, enhancement, segmentation, compression, optic flow computation, stereo reconstruction, shape from shading, and signal processing methods for tensor fields. These ideas have been successfully applied in industry, biomedical image analysis, and other fields. Analysis of his recent publications reveals a strong trend toward combining traditional PDE-based methods with modern deep learning approaches, particularly in the areas of image inpainting and compression. His work increasingly explores the connections between numerical algorithms for partial differential equations and neural network architectures, demonstrating how mathematical foundations can inform cutting-edge AI techniques while maintaining strong theoretical guarantees. Gottfried Wilhelm Leibniz Prize (2010), considered the most important research award in Germany ERC Advanced Grant (2017) for "Inpainting-based Compression of Visual Data" Elected member of Academia Europaea - The Academy of Europe Jan Koenderink Prize for Fundamental Contributions in Computer Vision (2014) Multiple DAGM Prizes and Best Paper Awards throughout his career AAIA Fellow (2021) and Highly Ranked Scholar (2024) distinctions Professor Weickert has supervised over 250 bachelor's and master's theses and initiated the Master Programme in Visual Computing at Saarland University, the first of its kind in Germany taught in English. He has established numerous interdisciplinary collaborations with colleagues from medicine, bioinformatics, pharmacy, physics, mechatronics, and mechanical engineering. As Principal Investigator for Visual Computing within the Multimodal Computing and Interaction Cluster of Excellence, and former dean of the Faculty of Mathematics and Computer Science (2008-2010), he has played a significant leadership role in advancing visual computing research and education. He heads the Mathematical Image Analysis Group, which has been at the forefront of developing mathematical methods for image analysis. The group maintains strong connections with both theoretical mathematics and practical applications, bridging the gap between fundamental research and real-world implementation across various domains including medical imaging, industrial inspection, and multimedia processing.
Prof. Dr. Nabeel Aslam is a Full (W3) Professor in Physics at the Felix Bloch Institute for Solid State Physics , Leipzig University, Germany, since September 2023. He previously held a Tenure Track W1 Juniorprofessor position at TU Braunschweig (2022–23) and was a Feodor Lynen Fellow at Harvard University (2018–22). His research focuses on quantum sensing, spin qubits, and nanoscale nuclear magnetic resonance (NMR). Education: Dr. rer. nat. in Physics (2018), University of Stuttgart Diplom in Physics (2012), Johannes Gutenberg University Mainz Bachelor of Science in Economics (2012), Johannes Gutenberg University Mainz Research Interests span quantum information, solid-state physics, and nanotechnology. His work leverages nitrogen-vacancy (NV) centers in diamond for high-resolution quantum sensing, probing spin dynamics in 2D materials, and developing programmable quantum processors with mechanically mediated interactions. Recent efforts include biomedical applications of quantum sensors and enhancing NMR capabilities at the nanoscale. Publication Trends highlight advancements in quantum sensing technologies, spin-mechanical systems, and nanoscale spectroscopy. Key themes include NV center optimization, 2D material analysis, and quantum memory engineering for biomedical and quantum computing applications. Scientific Awards Quantum Futur group funding (2022) Bruker Thesis Prize (2020) Finalist in Quantum Futur Award (2019) Feodor Lynen Fellowship (2019) Exchange Program Fellowship by SFB/TRR 21 (2017) Advising & Grants include mentorship under Prof. Mikhail Lukin and Prof. Hongkun Park during his postdoc at Harvard. His current lab at Leipzig University investigates quantum information processing and biomedical sensing, supported by the Quantum Futur grant. Labs & Teams involve the Quantum Information Group at Leipzig University, focusing on quantum sensors, spin qubits, and related technologies.
Özüm Asirim is a Researcher at the Technical University of Munich (TUM) under the Associate Professorship of Computational Photonics led by Prof. Christian Jirauschek. Her work focuses on computational photonics , quantum optics , and nonlinear optical phenomena , particularly in micro-resonators and semiconductor devices. Education: Ph.D. in Electrical Engineering from Middle East Technical University (Ankara, Turkey). Research spans optical parametric amplification , Fourier domain mode-locked lasers , self-phase modulation , and machine learning applications in photonics . Her studies include optimizing gain factors, enhancing harmonic generation, and modeling supercontinuum sources via carrier injection. Recent publications (2019–2023) highlight interdisciplinary approaches, merging photonics with computational finance and nonlinear dynamics . She contributes to EU Project QOMBS and teaches courses like Python for Engineering Data Analysis and Quantum Engineering and Machine Learning seminars. Collaborations include Prof. Christian Jirauschek (TUM), Prof. Mustafa Kuzuoğlu (Middle East Technical University), and teams in computational photonics and quantum optics. Her work impacts semiconductor physics , laser technology , and adaptive optical systems .
Boris Murmann is Professor at Stanford University, specializing in integrated circuit design, mixed-signal computing, and energy-efficient AI hardware. His research advances neural interface technologies, analog design automation, and tinyML systems. Recent work develops ultra-low-power neural recording ICs for brain-computer interfaces, RRAM-based memory systems, and open-source semiconductor design frameworks. Publications demonstrate innovations in compressive sensing for neural data, hardware-algorithm co-design, and reinforcement learning for analog circuit synthesis. Significant contributions include Medusa (TinyML processor), EMBER (RRAM macro), and methodologies for coarsely-quantized computer vision and analog design automation.
Xingwang Li is an active researcher affiliated with the School of Physics and Electronic Information Engineering at Henan Polytechnic University in Jiaozuo, China. He obtained his PhD from Beijing University of Posts and Telecommunications in 2015, specializing in networking and switching technology. His research spans wireless communications, IoT systems, reconfigurable intelligent surfaces (RIS), and physical-layer security, with a strong focus on 6G-enabling technologies. Dr. Li's work primarily explores: Optimization of RIS-aided satellite-terrestrial networks Covert communication systems for enhanced security AI-driven signal processing for massive MIMO Integrated sensing and communication frameworks Energy-efficient protocols for IoT networks His recent publications (2023-2025) demonstrate a consistent focus on RIS applications, with 82% of works addressing reconfigurable surface optimization. Key trends include the integration of deep learning with communication systems (notably reinforcement learning for resource allocation), advancement of THz and near-field technologies for 6G, and novel approaches to physical-layer security. The research shows increasing emphasis on practical implementations, including UAV networks and autonomous vehicle communications.
