Prof. Dr. rer. nat. Rainer Leupers is a faculty member at RWTH Aachen University, chairing the Department of Software for Systems on Silicon. His research focuses on embedded systems, hardware-software co-design, virtual prototyping, and security in computing-in-memory architectures. He has published extensively on RRAM accelerators, logic locking, and neuromorphic security. Chair of Software for Systems on Silicon Research in hardware security and deep learning accelerators Recent publications on cross-tool virtual frameworks and thermal side-channel attacks His work bridges system-level modeling with practical security implementations, emphasizing reliability and performance in heterogeneous computing environments. Key trends in his 2025-2023 articles include compute-in-memory optimization, neural network inference efficiency, and security vulnerabilities in emerging hardware. Awards and formal recognitions are not explicitly detailed in the provided materials. He has not directly mentioned advising students or research grants in the given text fragments. The chair's contact information includes an office at ICT Cube 1, Electrical Engineering, Aachen, with direct email and website links.
Barbara Namer is an Adjunct Professor at Friedrich-Alexander University Erlangen-Nuremberg and leads the IZKF-funded "Neuroscience: translational pain research" group at RWTH Aachen University Hospital. Her career spans 20+ years in neurophysiological pain research with clinical translations. Doctor of Medicine (2000-2004), Erlangen-Nuremberg Venia Legendi (Habilitation) in Physiology (2010) Adjunct Professor title (2018) Her research focuses on nociceptor mechanisms in diabetic neuropathy, migraine pathophysiology, and TRPA1 channel dynamics . Computational modeling and human microneurography techniques are central to her work. Key publication trends show expertise in peripheral nerve sensitization , diabetic pain mechanisms , translational pain modeling , and ion channel pharmacology . She has received multiple DGSS and German Neurology Society awards for her pain research. 2019 - DGSS Poster Award 2015 - DGSS Poster Award 2010 - EFIC Grünenthal Grant 2003 & 2018 - German Neurology Society Awards National and international collaborations include research stays in Norway and Sweden, with extensive grant funding from DFG and IZKF projects.
Philippe Ciblat is a Professor at TELECOM Paris Tech, affiliated with the Department of Signal Processing and Communications. His research spans signal processing, wireless communications, and machine learning applications in networking. He has collaborated extensively with institutions like the University of Paris-Saclay and international researchers in areas such as cooperative communication protocols, resource allocation, and coding theory. Research Interests: Machine learning for signal processing, wireless channel modeling (Rician fading), lattice decoding, caching strategies, and distributed optimization. Notable Work: Pioneered transformer-based packet scheduling, neural network approaches to lattice decoding, and effective capacity analysis in fading channels. His contributions include over 170 publications in top venues (IEEE Trans. Signal Process., IEEE Trans. Wireless Commun.) and collaborations with industry partners on practical implementations like cache-aided polar coding. He has advised multiple researchers in distributed systems and wireless resource management.
Pau Colomer is a Researcher at the Technical University of Munich , affiliated with the Theoretical Information Technology department. His work focuses on quantum communication, information theory, and wireless networks, contributing to foundational research in quantum channel identification and network information processing. His research interests span quantum information theory , neural networks , and signal processing , with recent publications addressing deterministic identification over quantum channels, coherence theory in quantum systems, and multi-user quantum communication. These works highlight advancements in quantum channel coding , decentralized processing , and security in wireless networks . Publications demonstrate a trend toward quantum communication and network information theory , emphasizing challenges in interference modeling , entanglement transmission , and information-theoretic security . Key subfields include superlinear rates in finite output channels , decoupling mechanisms , and quantum-enhanced sensing .
Johannes Maly is an Assistant Professor at the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU Munich. He previously held postdoctoral positions at Catholic University of Eichstaett-Ingolstadt and RWTH Aachen University, and completed his PhD at TUM Munich under Prof. Massimo Fornasier. PhD in Mathematics (2019, TUM Munich) M.Sc. in Mathematics (2015, TUM Munich) B.Sc. in Mathematics (2013, TUM Munich) His research focuses on mathematical data science and machine learning, specifically addressing: Robust covariance estimation under quantization Neural network approximation properties Implicit bias in gradient descent training Multi-structured signal recovery Quantization effects in deep learning and compressed sensing His recent publications analyze dithered quantization in covariance estimation, implicit regularization in overparameterized models, and multi-structured data recovery. He applies mathematical rigor to practical challenges in wireless communications (e.g., MIMO systems) and neural network training. Scientific recognition includes: relAI Fellow MCML Associate He supervises code/toolbox development for reproducibility and teaches graduate courses in convex optimization, high-dimensional probability, and mathematical data science. His work bridges theoretical mathematics and applied signal processing.
Leila Musavian is a Professor of Wireless Communications at the University of Surrey's Department of Electrical and Electronic Engineering within the Faculty of Engineering and Physical Sciences. She is a leading researcher in next-generation wireless communication systems with a focus on 5G/6G technologies, particularly in the areas of non-orthogonal multiple access (NOMA), energy harvesting communications, physical layer network coding, and ultra-reliable low-latency communications (uRLLC). Her research has significant implications for vehicular networks, IoT applications, and future wireless infrastructure. Her research interests span multiple critical areas of modern wireless communications, including energy-efficient resource allocation, security in wireless networks, massive MIMO systems, and reconfigurable intelligent surfaces. She has pioneered work on the performance analysis of NOMA systems under statistical quality of service constraints, which has become increasingly important for emerging applications requiring ultra-reliable and low-latency communications. Her research bridges theoretical foundations with practical implementations, often incorporating machine learning techniques for optimization of wireless networks. Her publication portfolio shows a clear evolution toward cutting-edge topics in wireless communications, with recent focus on holographic beamforming via dynamic metasurface antennas, robotic wireless energy transfer, and deep reinforcement learning applications for vehicular optical camera communications. These works demonstrate her ability to identify and address emerging challenges in next-generation wireless systems, particularly those related to 6G technologies and beyond. Professor Musavian has successfully supervised numerous PhD students who have gone on to become active researchers in the field, with many continuing to collaborate with her on cutting-edge projects. Her research has been supported by significant grants from national and international funding bodies, though specific grant details are not visible in the current dataset.
Peter Jax is a Full Professor at RWTH Aachen University , leading the Chair of Communication Systems . His research focuses on speech and audio processing , with expertise in active noise control , spatial audio , and machine learning applications for acoustic systems. Diploma in Electrical Engineering (1997), RWTH Aachen University PhD (2002), RWTH Aachen University Research areas include binaural direction-of-arrival estimation , MIMO acoustic system identification , and adaptive filtering for consumer and medical audio applications. Recent articles highlight innovations in ambisonics upscaling , noise control for UAVs , and data-driven uncertainty modeling in headphones. Scientific honors : Distinguished Member of Technicolor Fellowship Network (2010) Johann-Philipp-Reis Preis Borchers Medal E-Plus Award for Best Dissertation With over 25 patents in speech/audio processing and leadership roles in industry (Deutsche Thomson OHG, 2005–2015), he bridges academic research and industrial innovation.
Sandrine Blazy is a Professor in the Computer Science Department at the University of Rennes, France. She is a member of CELTIQUE (also referred to as Epicure), a joint project-team with Inria Rennes Bretagne Atlantique and the IRISA laboratory. Since 2021, she has served as deputy director of the IRISA CNRS UMR 6074 laboratory and will be the general chair for POPL 2026, which will be held in Rennes. She is also a member of the editorial board of the LMCS journal. Dr. Blazy completed her PhD at CNAM (Conservatoire National des Arts et Métiers) in 1993 with a thesis titled "La spécialisation de programmes pour l'aide à la maintenance du logiciel" (Program Specialization for Software Maintenance Assistance). She later completed her Habilitation à diriger des recherches (HDR) in 2008 at the University of Évry Val d'Essonne with a thesis titled "Sémantiques formelles" (Formal Semantics). Her research focuses on the formal verification of program transformations and semantic properties of programming languages, particularly in the context of the CompCert compiler and Verasco static analyzer. She develops mechanized semantics using the Coq (or Rocq) proof assistant to ensure software correctness and security. A prime application domain of her work is software security, including constant-time programming for cryptographic applications and software obfuscation techniques. Her teaching includes mechanized semantics (in Coq), functional programming (in OCaml), formal methods (using Why3), and software vulnerabilities. Dr. Blazy's publication record from 2019-2025 shows a sustained focus on verified compilation techniques, particularly in preserving security properties during compilation. Her work bridges theoretical formal methods with practical compiler implementation, resulting in tools that have real-world impact in safety-critical systems. She has made significant contributions to the CompCert formally verified compiler project, with particular attention to constant-time preservation for cryptographic applications and JIT compilation verification. Her scientific achievements have been recognized with several major awards: CNRS Silver Medal (2023) Lucas Award from Formal Methods Europe (2023) ACM SIGPLAN Programming Languages Software Award for CompCert (2022) ACM Software System Award for CompCert (2021) Dr. Blazy has been actively involved in the programming languages research community, serving on numerous program committees for major conferences including POPL, ICFP, PLDI, and CPP. She has mentored students and contributed to education through teaching mechanized semantics and formal methods. Her work with the CompCert compiler has led to practical applications in safety-critical systems, with industry collaborations documented in publications like "CompCert: Practical experience on integrating and qualifying a formally verified optimizing compiler" (ERTS 2018). She leads research within the CELTIQUE project team, which focuses on developing trustworthy software using deductive verification. Her team works on advancing the state of the art in formal verification of compilers and static analyzers, with applications in security-critical domains including cryptographic implementations and safety-critical embedded systems.
Andreas Winter is an ICREA Research Professor at Universitat Autònoma de Barcelona and holds a Hans Fischer Senior Fellowship at TUM-IAS, Technical University of Munich. He specializes in quantum and classical information theory with affiliations at the University of Cologne's Department of Quantum Information and Computation. His research examines fundamental limits in quantum communication, entropy applications, and quantum computing foundations. Education: Diploma in Mathematics, Freie Universität Berlin Ph.D. in Mathematics, Universität Bielefeld Research: His interdisciplinary work bridges quantum Shannon theory, thermodynamics, and discrete mathematics, exploring quantum channel capacities, information tradeoffs, and cryptographic protocols. Recent publications demonstrate advances in quantum coding efficiency and high-dimensional quantum systems. Awards: 2022: Hans Fischer Senior Fellowship, Alexander von Humboldt Prize, QCMC Quantum Award 2017: IEEE Information Theory Paper Award 2012: Whitehead Prize (LMS) 2007: Philipp Leverhulme Prize Leadership: Leads the Quantum Information Theory Focus Group at TUM-IAS, collaborating with Prof. Holger Boche on quantum communication frameworks.
Alexandra Dmitrienko is a researcher at the University of Würzburg's Institute of Computer Science. Her work focuses on cybersecurity, privacy-preserving technologies, and secure machine learning systems. She has collaborated extensively with institutions like TU Darmstadt and the University of California. Her research spans federated learning security, IoT device protection, Tor network analysis, and mobile platform vulnerabilities. Key contributions include defenses against poisoning attacks in federated learning, analysis of contact discovery exploits in messengers, and practical SGX cache attack mitigations. She has authored over 90 publications across top conferences like NDSS, CCS, and USENIX Security, and contributed to open-source tools like DNNShield and ClearMark for model ownership verification.
Dr. Lisa Kohl is a tenured Researcher in the Cryptology Group at CWI Amsterdam since October 2020. Her work focuses on secure computation and practical post-quantum secure protocols . Prior to CWI, she was a postdoctoral researcher at Technion with Yuval Ishai and completed her PhD at Karlsruhe Institute of Technology under Dennis Hofheinz in 2019. She also spent eight months at the FACT center, IDC Herzliya during her PhD and wrote her master’s thesis at CWI Cryptology Group as a visiting student in 2015. PhD in Cryptology (2019, Karlsruhe Institute of Technology) Postdoctoral Researcher (Technion, 2019-2020) Research Visit Fellow (FACT Center, 2015-2019) Visiting Student (CWI Cryptology Group, 2015) Her research spans secure multi-party computation , homomorphic secret sharing , post-quantum cryptography , and pseudorandom correlation generation . Articles demonstrate expertise in optimizing oblivious transfer , improving Σ-protocol efficiency , and constructing cryptographic primitives from lattice problems and LPN assumptions . Contact: Lisa.Kohl@cwi.nl
Wiktor Młynarski is a Professor and Research Group Leader at the Faculty of Biology, Ludwig-Maximilians-University Munich, where he directs computational neuroscience research focused on adaptive neural computations in sensory systems. His work bridges theoretical frameworks with experimental validation to uncover fundamental principles of biological information processing. His research centers on computational and theoretical neuroscience, leveraging information theory, statistics, and probabilistic machine learning to model how neural systems efficiently represent dynamic natural environments. Key interests include sensory coding adaptation across timescales, statistical structure of natural stimuli, and the development of normative frameworks applicable from synaptic to behavioral levels. This inherently collaborative approach integrates theoretical predictions with experimental neuroscience to identify universal processing rules. Analysis of Młynarski's publication trajectory (2014-2025) reveals consistent exploration of efficient coding principles across auditory and visual domains, with recent work emphasizing anticipatory processing during locomotion, time-constrained decision-making, and panoramic visual statistics. His research demonstrates how environmental dynamics shape neural representations, moving beyond static models to capture the brain's adaptive capabilities in real-world contexts. Professor Młynarski leads the Computational Neurobiology Research Group (https://compneurobio.org), fostering interdisciplinary collaborations to confront theoretical models with experimental data. The group's work aims to establish general rules for biological information processing by examining how sensory systems dynamically optimize computations in response to environmental regularities and changes.
Dr. Yulin Hu serves as a Visiting Professor at RWTH Aachen University, holding the Chair of Information Theory and Data Analytics. His research program bridges theoretical foundations with practical implementations in next-generation wireless systems, with particular emphasis on UAV-aided networks and information-theoretic approaches to communication challenges. His core research interests span multiple interconnected domains: Wireless Communications (especially finite blocklength regimes) Information Theory applications in network design UAV trajectory optimization and network integration Wireless power transfer with nonlinear energy harvesting Edge computing and distributed learning systems Data analytics for network performance optimization Analysis of Dr. Hu's 2025 publication record reveals a concentrated research thrust on UAV trajectory design, where he develops joint optimization frameworks addressing energy efficiency, security, and reliability constraints. His work consistently integrates information-theoretic principles—particularly finite blocklength analysis—to solve practical challenges in ultra-reliable low-latency communications (URLLC) and wireless power transfer. A distinctive feature of his approach is the fusion of deep reinforcement learning with traditional optimization methods for dynamic network scenarios, including no-fly zone constraints and covert operations. While no specific scientific awards are documented in the available materials, his prolific output across top-tier venues demonstrates significant scholarly impact. Details regarding graduate student mentoring and research funding mechanisms remain unspecified in the current documentation. The Chair of Information Theory and Data Analytics, which Dr. Hu leads, functions as a specialized research unit focused on theoretical rigor and algorithmic innovation for wireless systems, though specific laboratory infrastructure or team composition details are not provided.
Ingo Weber is a Professor affiliated with Technische Universität München (TU Munich) and Fraunhofer Gesellschaft. His research focuses on blockchain technology, business process management (BPM), and artificial intelligence (AI), with a particular emphasis on integrating these fields. He has held former positions at TU Berlin, CSIRO Data61, and other institutions. Current affiliations: TU Munich and Fraunhofer Gesellschaft Former affiliations: TU Berlin, CSIRO Sydney, University of New South Wales, SAP Research, and University of Massachusetts Amherst Research interests include blockchain applications in business processes, process mining, AI-driven systems, and sustainability-oriented process analysis. His work explores topics such as blockchain scalability, data confidentiality, and cost-efficient process execution on next-generation blockchains like Algorand. He has pioneered frameworks like SOPA for sustainability analysis and FhGenie for confidentiality-preserving AI. Key contributions include over 200 publications in journals like IEEE Access, Future Generation Computer Systems, and ACM Transactions on Management Information Systems. Recent work emphasizes AI-augmented BPM systems and the application of large language models (LLMs) in scientific contexts. Notable projects include blockchain-based process execution engines (e.g., Caterpillar), platform architectures for multi-tenant blockchain systems, and frameworks for evaluating payment channel networks. He has collaborated extensively with industry partners and academic institutions globally.
Dr. Seongmin Lee is a researcher at the Max Planck Institute for Security and Privacy, specializing in software security and program analysis. Their work bridges theoretical and practical aspects of software testing, with a particular focus on automated testing techniques, dependency modeling, and genetic improvement. Research interests include: Software Security Software Testing and Fuzzing Program Analysis and Slicing Machine Learning Applications in Software Engineering Genetic Algorithms for Code Optimization Statistical and Causal Analysis of Code Behavior Recent publications (2016–2025) demonstrate a trajectory from foundational work on GPU parameter optimization to cutting-edge research on LLM-driven regression testing. Key trends include: Statistical modeling of software behavior Machine learning for bug classification and optimization Approximate analysis techniques for scalability Advancements in greybox fuzzing and coverage prediction Application of causal inference to mutation testing
