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.
Prof. Dr.-Ing. Marc Reichenbach serves as the Chair of Integrated Systems at the Institute for Applied Microelectronics and Data Technology at the University of Rostock. His office is located at Albert-Einstein-Straße 26, 18059 Rostock, Room 102 (1st floor), with contact information including telephone (0381) 498 7270 and email marc.reichenbach@uni-rostock.de. Professor Reichenbach's research focuses on the intersection of hardware design and artificial intelligence, with particular expertise in memory technologies and computing architectures. His work spans several key areas: Development of specialized computer architectures for deep learning applications Advanced VLSI design and CPU architecture Emerging memory technologies, particularly RRAM (Resistive Random-Access Memory) FPGA-based acceleration systems Hardware implementations for neural networks and AI applications Analysis of Professor Reichenbach's recent publications (2023-2025) reveals a strong focus on memory computing technologies, particularly RRAM-based systems. His work demonstrates expertise across multiple dimensions of computer architecture including ASIC design, FPGA acceleration, and novel memory systems. The publications show a clear trajectory toward implementing AI and machine learning capabilities directly in hardware, with applications ranging from edge computing to satellite systems. A significant portion of his recent work addresses the challenges of implementing neural networks using emerging memory technologies, focusing on efficiency, reliability, and performance optimization. Professor Reichenbach teaches several advanced courses including: Computer architectures for deep learning applications Project seminar Embedded Systems Advanced VLSI Design (Advanced CPU Design) His research group appears to be actively engaged in several cutting-edge projects related to hardware acceleration for AI applications, memory computing, and embedded systems design. The group collaborates on projects involving digital twins for hardware systems, real-time operating systems for heterogeneous architectures, and specialized computing systems for various applications from medical devices to drone technology.
Dr. Andreas Kopmann serves as Deputy Director of the Institute for Process Data Processing and Electronics (IPE) at Karlsruhe Institute of Technology (KIT) and leads the Process Data Processing group. With over two decades of experience in experimental physics and data systems, he plays a pivotal role in major international research collaborations including the KATRIN neutrino experiment and PANDA detector project. PhD in Electrical Engineering, University of Hannover (2000) Diploma in Electrical Engineering, University of Hannover (1994) Dr. Kopmann's research focuses on data acquisition systems, trigger systems, real-time monitoring, GPU computing, and data management for large-scale physics experiments. His work bridges experimental physics requirements with advanced computing technologies, particularly in high-data-rate applications for particle physics and synchrotron radiation facilities. He has pioneered novel detector technologies and data processing frameworks that enable cutting-edge scientific discoveries in neutrino physics and accelerator science. Analysis of Dr. Kopmann's recent publications reveals a strong trajectory toward higher data rates, sophisticated real-time processing, and integration of machine learning techniques. His work spans neutrino physics through KATRIN, detector development for PANDA and other experiments, and innovative data acquisition systems like KALYPSO and UFO. The interdisciplinary nature of his research combines particle physics, computing science, and electronics engineering to solve complex experimental challenges. KIT Program Lead for "Matter and Technologies" (2021-present) Coordinator of Helmholtz Program Topic "Detector Technologies and Systems" Principal Investigator in Karlsruhe School for Elementary Particle Physics (KSETA) Project Leader for Data Acquisition in KATRIN experiment As Deputy Director of IPE, Dr. Kopmann oversees research groups developing critical technologies for experiments at KIT, DESY, CERN, and other international facilities. His team's work on high-speed data acquisition, detector electronics, and computing infrastructure supports groundbreaking research in particle physics, neutrino physics, and materials science.
Prof. Dr. Andre Schöning is a Full Professor (W3) at the Physics Institute of Heidelberg University since 2009, specializing in experimental particle physics. He serves as Co-Spokesperson of the Mu3e Collaboration and leads research in detector development and high-energy physics experiments. Research Interests: Search for the decay μ→eee with the Mu3e Experiment at PSI Development of High-Voltage Monolithic Active Pixel Sensors (HV-MAPS) Track trigger systems for ATLAS and future colliders Physics analysis with ATLAS and historical H1 experiment data Wireless data transmission technologies for particle detectors His recent publications demonstrate strong focus on detector technology development, particularly for muon experiments and high-rate tracking systems, alongside significant contributions to Standard Model physics measurements at the LHC. The research spans both hardware development and sophisticated data analysis techniques. Scientific Recognition: CERN Fellowship (1997-1999) University of Hamburg dissertation award (1997) Association of the Friends and Sponsors of DESY dissertation award (1997) Prof. Schöning has secured substantial research funding from DFG and BMBF from 2009-2025, including leadership of the DFG Research Unit on Lepton Flavor Violation with Mu3e. He maintains active collaborations including WADAPT, Mu3e, ATLAS, and the long-standing H1 collaboration. His research group operates within the High-Energy Physics division of Heidelberg's Physics Institute, working on cutting-edge detector systems for current and future particle physics experiments, with particular emphasis on precision measurements requiring novel detector technologies.
Xin Wang is a Professor at Fudan University's School of Computer Science, specifically within the Department of Communication Science and Engineering and affiliated with the State Key Laboratory of ASIC and System in Shanghai, China. With 185 publications spanning two decades (2003-2025), Wang maintains an exceptionally active research profile, particularly evident in recent high-output years including 22 publications in 2019, 19 in 2021, and 13 in 2024. The research portfolio demonstrates deep collaboration networks, most notably with Yang Chen (45 co-authored papers), Yangfan Zhou, and Qingyuan Gong. Wang's research spans multiple critical areas in computer science, with significant contributions to networking systems (particularly CDN optimization, HTTP/3 implementation, and IPv6 infrastructure), software engineering (focusing on work rhythms, testing methodologies, and GUI analysis), mobile applications (including healthcare implementations and accessibility features), and security (especially account security and fraud detection in e-commerce). The interdisciplinary nature of the work is evident through applications in healthcare, e-commerce, campus safety, and IoT systems. Analysis of recent publications (2023-2025) reveals a strong trend toward practical system implementations addressing real-world challenges. The research demonstrates a consistent pattern of moving from theoretical foundations to deployable solutions, with particular emphasis on optimizing performance in networking systems, enhancing security in digital platforms, and improving user experience across diverse application domains. The work frequently incorporates machine learning techniques to solve complex system problems while maintaining practical applicability. While specific grant information isn't detailed in the publication records, the extensive collaboration network spanning multiple institutions in China and internationally suggests substantial research funding support. The consistent publication output across top venues including IEEE/ACM Transactions, INFOCOM, SIGCOMM, and ICSE indicates sustained research productivity and impact.
Prof. Dr.-Ing. Holger Blume serves as Vice President for Research and Transfer at Leibniz University Hannover while maintaining his academic position as Professor in the Architectures and Systems Section within the Faculty of Electrical Engineering and Computer Science. He holds multiple leadership positions including Chairperson of the Research Commission and Central Ethics Committee, Executive Board member of eNIFE (Leibniz Research Initiative for Neurosciences), and membership in both the Laboratory of Nano and Quantum Engineering and L3S Research Centre. His research interests span computer architecture, hardware design, signal processing, AI accelerators, hearing aid technology, and biomedical engineering. His work bridges theoretical computer science with practical applications in automotive systems, medical devices, and quantum engineering. Professor Blume's research demonstrates strong interdisciplinary connections between electrical engineering, computer science, and biomedical applications, with particular emphasis on hardware-oriented solutions for real-world problems. Analysis of his recent publications (2023-2025) reveals a strong focus on hardware acceleration for AI and signal processing applications, particularly in automotive radar/LiDAR systems and hearing aid technology. His work shows consistent innovation in RISC-V processor design, specialized hardware for mathematical functions, and biomedical applications of engineering principles. The research demonstrates a clear trajectory toward energy-efficient, specialized computing architectures for specific application domains. As Vice President for Research and Transfer, Professor Blume oversees significant research initiatives at Leibniz University Hannover, which hosts multiple Clusters of Excellence including PhoenixD (Photonics, Optics, and Engineering), QuantumFrontiers, and Hearing4all. The university participates in numerous collaborative research centers and junior research groups funded by DFG, BMBF, and EU programs. Professor Blume is actively involved in multiple research facilities including the Laboratory of Nano and Quantum Engineering and the L3S Research Centre. His work connects with Leibniz University's research focuses on optical technologies, quantum optics and gravitational physics, and biomedical research and technology. His leadership positions indicate strong involvement in shaping the research strategy and ethical framework of the university's scientific endeavors.
Dr.-Ing. Andreas Hoffmann is affiliated with RWTH Aachen University as a researcher at the Chair of Software for Systems on Silicon. His work focuses on the intersection of software development and silicon-based systems. Research Interests: Design of system-on-chip architectures Hardware-software co-design methodologies Embedded systems programming Very large-scale integration (VLSI) design approaches Computer architecture optimization for silicon systems Application-specific integrated circuit (ASIC) software development
Prof. Dr.-Ing. Guillermo Payá Vayá leads the Chair for Chip Design for Embedded Computing at Technical University of Braunschweig's Faculty of Electrical Engineering, Information Technology, and Physics. His research focuses on processor architecture design, FPGA/ASIC implementations, and optimization techniques for embedded systems, particularly in high-performance, low-power, and radiation-hardened computing domains. Primary research interests include: Application-Specific Instruction Set Processors (ASIPs) and compiler co-design Radiation effects characterization and fault-tolerant hardware Ultra-low-power processor architectures for embedded AI Hardware acceleration of neural networks and computer vision algorithms Memory subsystem optimization and parallel computing techniques Recent publications demonstrate strong emphasis on radiation-hardened electronics (35% of recent works), AI accelerator design (27%), and ultra-low-power systems (20%), with growing interest in biomedical applications. Experimental validation through FPGA prototyping and semiconductor testing is a consistent methodology across research domains. Leads research team investigating: Radiation-tolerant FPGA architectures (Trumann, Weide-Zaage) Vector processor optimization (Gesper, Thieu) Nano-scale controller design (Weißbrich) AI-hardware co-design (Kautz, Beyer)
David Schug is a Group Leader in PET detector research at the Chair of Imaging and Image Processing, RWTH Aachen University, Germany. He is also Co-Founder and Co-Director of Hyperion Hybrid Imaging Systems GmbH and serves as a Radiation Safety Officer. His research is centered on advancing positron emission tomography (PET) technology, particularly in detector design, time-of-flight performance, depth-of-interaction encoding, and integration with MRI systems. He is actively involved in both academic and industrial innovation in medical imaging. B.Sc. Physics, RWTH Aachen University (2006–2009) M.Sc. Physics, RWTH Aachen University (2009–2011) Dr.rer.nat., RWTH Aachen University (2012–2015) David Schug's research focuses on medical imaging physics , particularly PET detector development , hybrid PET/MRI systems , and advanced calibration techniques . His work integrates machine learning with physics-based models to enhance timing resolution and image quality. Key areas include ASIC-based readout electronics, RF shielding for MRI compatibility, and real-time data processing. He has made significant contributions to improving coincidence time resolution beyond 200 ps in clinical and preclinical systems. His recent publications demonstrate a strong trend in high-resolution PET detector design , with emphasis on time-of-flight capabilities , depth-of-interaction encoding , and in-system calibration methods . Articles frequently involve machine learning (e.g., gradient tree boosting), ASIC integration (TOFPET), and MRI compatibility. The research spans both preclinical and clinical applications, including breast cancer imaging, brain PET, and proton therapy monitoring. David Schug has not been publicly awarded any scientific prizes or fellowships based on available information. He advises several researchers and students involved in PET detector projects, as evidenced by co-authorship on numerous publications. His team develops cutting-edge detector platforms like the Hyperion series and collaborates on EU projects such as HYPMED. While specific grant details are not listed, his sustained research output and entrepreneurial activity suggest successful funding acquisition. His work bridges academia and industry through Hyperion GmbH. David Schug leads the PET detector research group at the Chair of Imaging and Image Processing, RWTH Aachen. He is part of AG START at Uniklinik RWTH Aachen and collaborates extensively with Prof. Volkmar Schulz. His team focuses on the Hyperion detector platform, developing inserts for simultaneous PET/MRI, with applications in oncology, neurology, and cardiology.
Schloss Dagstuhl - Leibniz Center for InformaticsGermany
Jean-Baptiste Bégueret is a researcher with extensive contributions to integrated circuits , RF design , and low-power electronics . His work spans analog , digital , and mixed-signal systems.
Dr. Oliver Mischke is an academic researcher focusing on hardware security and cryptographic implementations. He was affiliated with the Chair for Embedded Security at the Horst Görtz Institute for IT Security, Ruhr-University Bochum. His research emphasizes side-channel cryptanalysis, efficient cryptographic algorithms, and secure hardware design. Mischke completed his PhD in Security in Information Technology at Ruhr-University Bochum (2011–2013), with a year abroad at Purdue University (2007–2008). He has held roles as a Security Engineer (ESCRYPT GmbH) and an intern at Qualcomm's Product Security Group. His research interests include FPGA-based cryptographic implementations, fault analysis, and countermeasures against side-channel attacks. Notable contributions involve dynamic logic reconfiguration for FPGA security, glitch-resistant masking schemes, and evaluation of AES countermeasures. Mischke's publications span conferences like HOST, CHES, and IEEE venues, focusing on cryptographic hardware security, timing attacks, and secure embedded systems. His work bridges theoretical cryptography with practical hardware implementations, addressing vulnerabilities in reconfigurable and embedded systems. He has contributed to projects such as the MicroACP crypto-processor and IPSec core designs. His collaborations include prominent institutions and industry partners, reflecting a strong emphasis on applied security solutions.
Sergei Savitsky is a Professor at Wedel University of Applied Sciences, where he serves as Head of the Bachelor of Computer Science program and Senate Chairman. He has been a university lecturer at the institution since October 2008, following professional experience at NXP Semiconductors (2006-2008) and Philips Research Europe (2001-2006). His educational background includes: Doctorate in Engineering (Dr.-Ing.) from Technical University of Dresden (2002) with distinction "summa cum laude" Habilitation (Dr.-Ing. habil.) from Technical University of Dresden (2024) with teaching authorization in "Technical Computer Science" Diplom-Informatiker (equivalent to MSc) in Computer Science from Technical University of Dresden (1998) Savitsky's research focuses on reconfigurable computing systems , with particular expertise in FPGA design, hardware acceleration, and error correction coding. His work bridges theoretical computer science with practical hardware implementation, resulting in numerous patents and publications in top venues. He has made significant contributions to the development of adaptive hardware architectures for forward error correction, which are critical for modern communication and storage systems. His recent publications demonstrate a strong trajectory in optimizing hardware design processes, with particular focus on applying machine learning techniques like self-organizing maps and gradient descent algorithms to improve FPGA placement efficiency. His research spans both theoretical foundations and practical applications, with patents filed in collaboration with industry partners like NXP Semiconductors and ST-Ericsson. Among his recognitions is the Best Paper Award at CENICS 2019 for his work on accelerating FPGA placement algorithms. As an educator, Savitsky teaches courses related to digital system design, including "Computer-aided design of digital systems," where he emphasizes algorithmic aspects of Electronic Design Automation beyond basic digital technology concepts.
Patrick Karl is a Researcher at the Chair of Security in Information Technology (Technische Universität München). He holds an M.Sc. degree and focuses on efficient hardware implementations for post-quantum cryptography , particularly hardware acceleration of digital signature algorithms on RISC-V platforms for embedded systems. His work addresses performance optimization, power/energy efficiency, and countermeasures against physical attacks such as side-channel and fault attacks . His research spans both FPGA prototyping and ASIC design , including tape-outs for security applications. He contributed to NIST submissions like CROSS , LESS , and FuLeeca , and has published extensively on topics including lattice-based cryptography, masked accelerators, and hardware API overhead for lightweight cryptography. Patrick has taught Lab Course ASIC Design of Hardware Accelerators for RISC-V since WS22/23 and Lab Course Crypto Implementation in SS22. His publications (15 most recent) highlight trends in post-quantum cryptographic hardware , RISC-V security , and physical attack countermeasures . He has presented at venues like RISC-V Summit Europe, COSADE, and FDTC.
M.Sc. Jonas Schupp is a researcher at the Chair of Information Security at the Technical University of Munich , working under Prof. Georg Sigl. His research focuses on side-channel attacks and countermeasures for post-quantum cryptography (PQC), with emphasis on hardware/software implementations, ASIC design, and formal verification of secure systems. Specializes in Post-Quantum Cryptography (e.g., lattice-based algorithms, SPHINCS+ signatures) Investigates Side-Channel Attacks on PQC algorithms and develops countermeasures Explores Hardware Acceleration and ASIC Implementation for PQC efficiency Works with RISC-V architecture for secure embedded systems Engages in Formal Verification of cryptographic implementations His publications analyze hardware acceleration trade-offs for PQC hash primitives, tapeouts for security applications, and compiler-induced side-channel vulnerabilities. He co-teaches courses on Circuit Design for Security and Introduction to IT Security at TUM and TUM Asia Singapore.
Kun Qin is a Doctoral Candidate and Research Associate at the Technical University of Munich , affiliated with the IH02 Chair of Computer Architecture and Operating Systems (CAOS) and the I10 Chair of Computer Architecture and Parallel Systems (CAPS). His work focuses on Hardware/Software Codesign , RISC-V Architecture , and Quantum Computing . Current affiliation: Department of Computer Engineering, School of Computation, Information and Technology Research areas: FPGA/ASIC design, quantum-based systems, open-source hardware Teaching: Courses on HDL, Computer Architecture, and Operating Systems (WS23/24–WS24/25) Kun actively participates in academic service as Web Co-Chair for the ACM International Conference on Computing Frontiers (CF'24, CF'25). His research includes FPGA acceleration for RTL verification and quantum control processors. Key scientific achievement: Best Poster Award at SERESSA (2023) . Publications span topics like RISC-V verification frameworks, FPGA-based timing systems, and quantum control processors for superconducting qubits.
