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. 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.
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.
Dr. Janis Nötzel is a senior researcher at the Chair of Theoretical Information Technology (Technische Universität München) and leads his independent Emmy Noether research group. Previously, he held a postdoctoral position at Universitat Autónoma de Barcelona and contributed to 5G practical implementations at TU Dresden's 5G Lab. His research spans quantum information theory, physical layer security, and machine learning applications. Key focuses include Quantum channel capacities under adversarial conditions Entanglement-assisted communication Quantum software frameworks (QuNetSim, QuReed) Interplay between classical and quantum communication Security analysis for 6G networks Resource optimization in quantum systems Recent publications (2023-2025) showcase innovations in Quantum satellite communication architectures Hybrid quantum-classical clustering algorithms Photonic processor instability modeling Covert capacity of compound channels Quantum key distribution resilience Free-space Bessel beam communication He actively collaborates with 6G-life research hub and contributes to quantum network simulation tools. Grants include funding from DFG (Leibniz Program), BMBF (6G-life, Q.Link.X), and StMWi (6G Zukunftslabor Bayern).
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.
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.
Nele Mentens is a full professor at both KU Leuven and Leiden University, where she leads cutting-edge research in applied cryptography, hardware security, and secure embedded systems. At KU Leuven, she is affiliated with the Faculty of Engineering Technology and the Electrical Engineering Department (ESAT), leading the Emerging Technologies, Systems & Security (ES&S) research group at the Diepenbeek campus. Simultaneously, she holds a full professorship at Leiden University’s Leiden Institute of Advanced Computer Science (LIACS), focusing on applied cryptography and security. She has been instrumental in numerous national and international research initiatives, including Horizon Europe and NWO-funded projects. Full Professor, KU Leuven (since 2023) Full Professor, Leiden University (since 2020) Associate Professor, KU Leuven (2014–2023) Post-doctoral Researcher & Lecturer, KHLim / KU Leuven (2007–2014) Ph.D. in Engineering Science, KU Leuven (2007) M.Sc. in Electrical Engineering, KU Leuven (2003) Her research focuses on secure and efficient hardware design, particularly for cryptographic applications on FPGAs, reconfigurable architectures, IoT security, and neuromorphic computing. She explores physical attack resistance, side-channel analysis protection, and trusted computing architectures, with applications in healthcare, industrial monitoring, and endpoint AI. Her work bridges theoretical cryptography with practical hardware implementations, emphasizing energy efficiency and real-time performance. The 15 most recent publications reflect a strong trend toward secure, energy-efficient, and intelligent embedded systems. Topics include neuromorphic AI accelerators, trusted IoT architectures, dynamic reconfiguration for side-channel protection, and secure medical data processing. These works span disciplines such as computer architecture, cybersecurity, digital design, and embedded systems, with a focus on hardware-software co-design and real-world deployment. Nele Mentens has received recognition for her contributions, including: Best Paper Award, DATE'16 Best Paper Nomination, AsianHOST'17 Best Paper Award, CHES'19 She has supervised over 15 Ph.D. students and post-docs, both current and former, and has served as principal investigator in approximately 25 funded research projects. Her work has attracted significant grants from Horizon Europe, NWO, FWO, and national innovation programs. She actively contributes to the academic community through editorial roles in top journals and leadership in major conferences. Nele Mentens leads the ES&S research group at KU Leuven and collaborates closely with LIACS at Leiden University. Her team includes Ph.D. students, post-docs, and research experts working on projects like NimbleAI, NeuroSoC, and TrustedIoT. She has also established secure electronics labs through infrastructure grants and maintains strong international ties with institutions such as EPFL, Ruhr University Bochum, and ETH Zurich.
Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.
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.
Zahra Ebrahimi is a researcher in the field of approximate computing, reconfigurable accelerator design, and embedded systems. She joined the Chair of Embedded Systems at Ruhr University Bochum in April 2024, following her PhD research associate role at the Center for Advancing Electronics Dresden (Cfaed) from 2018 to 2024. Her work focuses on energy-efficient hardware/software co-design for edge-to-cloud computing, with applications in neural networks and bio-signal processing. She leads the BMBF-funded project X-DNet , collaborating with Huawei Research Center Munich. Education : B.Sc. and M.Sc. in Electrical Engineering from Sharif University of Technology, Iran Key Projects : ReAp (DFG), Re-Learning (ESF), X-ReAp (DFG), X-DNet (BMBF) Research Interests : Approximate computing, reconfigurable accelerators, embedded systems, SW/HW co-design, energy-efficient edge/cloud computing. Recent Trends : Zahra’s research emphasizes applying approximation techniques to neural networks for 5G/6G applications and designing specialized hardware like CGRAs for bio-signal processing and distributed computing. Collaborations : Academic-industry partnership with Huawei Research Center Munich.
Pengcheng Xu is a Researcher at the Technical University of Munich's Chair of Circuit Design under Prof. Ralf Brederlow, specializing in analog and mixed-signal circuit design. His work spans energy harvesting systems, neuromorphic hardware, and wireless sensor technologies, with strong industry connections including prior roles at Huawei and Fraunhofer EMFT. Education: Bachelor of Physics, Shanghai Normal University (2013) Master of Integrated Circuit Engineering, Tongji University (2016) Ph.D. in Electrical Engineering, Université catholique de Louvain (2021) Exchange Student, University of Erlangen-Nuremberg (2015) Xu's research focuses on practical applications of circuit design including RF energy harvesting for battery-less IoT sensors, neuromorphic accelerators for edge computing, and precision analog systems for electrochemical/ mechanical stress sensing. His work bridges theoretical circuit innovation with real-world implementation in semiconductor processes from 28nm FDSOI to emerging memory technologies. His publications demonstrate consistent high-impact contributions to IEEE journals and conferences including JSSC, ISSCC, and ESSCIRC, with particular expertise in impedance-aware rectifier design and low-power circuit architectures. Xu holds a pending European/US patent for RF energy harvesting systems. Awards and Recognition: Shanghai Outstanding Graduate Award (2013, 2016) Chinese Government Award for Outstanding Self-Funded Students Abroad (2020) Chinese National Scholarship (2012, 2014, 2015) Meritorious Winner, Mathematical Contest in Modeling (2013) Xu actively contributes to the academic community as IEEE Young Professionals Germany Chair (2023-2024), IEEE Design Automation Conference TPC member (2022-2024), and reviewer for multiple IEEE journals. He supervises student theses in analog circuit design and neuromorphic hardware through TUM's Chair of Circuit Design, which maintains strong industry partnerships with semiconductor companies.
Prof. Burkart Voss is a Professor at the Department of Electrical Engineering and Information Technology, Ernst Abbe Hochschule Jena. He serves as BaföG representative and Head of the Space Electronics specialization. His research focuses on microprocessor technology, space systems, and radiation-resistant electronics. Research Interests: Design of microprocessors and signal processors Space-qualified electronics development Application of commercial-off-the-shelf (COTS) components in space Single-event effects (SEE) mitigation in space electronics Key Projects: RAKS : Investigating commercial sensors for space navigation LUNTE : Laser-based testing of radiation effects on electronics Teaching: Leads courses on signal processors, processor design, and space systems engineering. Coordinates interdisciplinary autonomous model vehicle projects preparing for embedded world competitions. Grant Activities: Receives funding from German Federal Ministry for Economic Affairs (Project RAKS: 50RM1410).
Muhammad Awais Bin Altaf is a researcher specializing in biomedical engineering, machine learning, and wearable technology. His work focuses on low-power embedded systems for neurological and cardiovascular monitoring, including EEG processors for seizure detection and PPG-based blood pressure classification. He has collaborated extensively with co-authors like Wala Saadeh and Jerald Yoo on IEEE journals and conferences. His research interests include Biomedical signal processing Wearable health devices Machine learning for medical diagnostics Energy-efficient hardware design Neurological disorder detection Embedded systems for clinical applications Recent publications highlight trends in shallow neural networks, autoencoders, and hardware acceleration for real-time health monitoring. Key subfields span seizure prediction, stress detection, and impedance-adaptive sensors. Collaborations include institutions in Germany, Finland, and Pakistan. His work often integrates open-source toolflows and industry-standard chip design techniques, emphasizing practical implementations for wearable environments. Contributions to HDR imaging algorithms and biomedical SoCs demonstrate interdisciplinary expertise in signal processing and healthcare technology.
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)
Shuang Liu is a Researcher at the Institute of Computer Architecture and Computer Engineering, University of Stuttgart. Their work focuses on embedded systems, networks-on-chip (NoC), and chip design methodologies. They specialize in deadlock-free routing, fault-tolerant computing, and application-specific system synthesis using advanced techniques like integer linear programming. Research interests include optimizing NoC architectures for chiplet-based systems, co-design of floorplanning and routing topologies, and energy-efficient signal processing in medical electronics. Their recent work emphasizes formal verification methods and performance optimization in hardware-software co-design. Publications span topics such as deadlock prevention in NoC, ILP-based routing synthesis, and trade-offs in hearing aid ASIP design. Office hours are Monday 2–3 p.m. in Room 1.001, Pfaffenwaldring 5b, Stuttgart.
