جستجوی استاد راهنمای دانشگاهی

• Computer Engineering
• Reconfigurable Computing
• Embedded Systems
• +۴ مورد دیگر

Keyvan Shahin is a Professor in the Computer Engineering department at BTU Cottbus-Senftenberg. His work focuses on reconfigurable computing, embedded systems, robotics, and communication architectures. He advises multiple doctoral students including Amritha Premkumar and Muhammad Amin Qureshi. Education details are not explicitly listed in the provided text, but his research interests span adaptive UAV swarm systems, algorithm optimization for hardware architectures, and domain-specific processor design. His recent publications explore topics like modular UAV communication, polynomial connections in signal processing, and reconfigurable hardware acceleration techniques. Keyvan leads the Computer Engineering Group and contributes to projects such as the UBICO initiative. His technical expertise includes developing automated toolflows for CGRA (Coarse-Grained Reconfigurable Array) architectures and advancing adaptive processor design methodologies. He holds a Master of Science degree and can be reached at keyvan.shahin@b-tu.de . His office is located in Building 1C, Room 1.39.

• Digital Signal Processing (DSP)
• Field-Programmable Gate Arrays (FPGA)
• Machine Learning Hardware Acceleration
• +۱۳ مورد دیگر

Gian Carlo Cardarilli is a researcher specializing in digital hardware design and machine learning acceleration. His work focuses on FPGA implementations, Residue Number System (RNS) architectures, and reconfigurable computing for applications in wireless communication, edge AI, and fault-tolerant systems. Key collaborations with institutions like IEEE and ACM through publications. Active in translating theoretical algorithms into practical hardware solutions for real-time systems. Research interests include: Optimizing deep learning models for heterogeneous platforms. Developing radiation-hardened memory systems. Creating energy-efficient signal processing architectures. Advancing reconfigurable functional units for embedded processors. His article analyses span fields like Quantum Cellular Automata , Variable Fractional Delay Filters , and RNS-Based Position Estimation , reflecting a trend toward adaptive, low-power, and domain-specific hardware.

Christian Heidornمشاهده پروفایل

پژوهشگر

• Deep Learning
• Hardware-Software Co-Design
• Neural Networks
• +۵ مورد دیگر

Christian Heidorn is a Researcher at the Chair of Computer Science 12 (Hardware-Software Co-Design) within the Department of Computer Science at Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany. He has held this position since 2018 and teaches "Fundamentals of Computer Engineering" regularly across multiple semesters. His educational background includes an M.Sc. in Medical Engineering (2015-2018) and a B.Sc. in Medical Engineering (2012-2016), both from FAU. Born in 1989 in Dachau, Germany, he maintains an office in Room 02.128 at Cauerstr. 11, Erlangen. Dr. Heidorn's research focuses on the Application of Deep Learning on Tightly Coupled Processor Arrays and Invasive Computing . His work bridges medical engineering with computer architecture, particularly emphasizing neural network deployment on specialized hardware. His research projects include OpTC and KISS Invasive Computing, which optimize neural networks for embedded systems and processor arrays. His recent publications demonstrate a strong trend toward efficient neural network deployment on embedded systems, with emphasis on automotive applications (AURIX microcontrollers), hardware-aware neural network pruning, and processor array optimization. His work spans both theoretical neural architecture search and practical implementations for real-world applications. His notable scientific contributions include: Development of the OpTC toolchain for neural network deployment on automotive microcontrollers Hardware-aware evolutionary filter pruning techniques for CNNs ALPACA: An accelerator chip design for nested loop programs Efficient mapping of CNNs onto tightly coupled processor arrays Dr. Heidorn has supervised numerous Master's and Bachelor's theses on topics ranging from neural network compression to robotic hand control using EMG data and processor array optimization. His advising reflects his dual expertise in medical engineering and computer architecture, with many projects focusing on practical embedded applications of deep learning. He is actively involved in research projects related to invasive computing and tightly coupled processor arrays, with a particular focus on making deep learning more accessible on resource-constrained devices for automotive and medical applications.

• Computer Architecture
• Embedded Systems
• Parallel and Distributed Computing
• +۳ مورد دیگر

Jürgen Teich is a Professor at the University of Erlangen-Nuremberg, Department of Computer Science. His research focuses on computer architecture, embedded systems, and hardware-software co-design, with particular emphasis on energy-efficient and sustainable computing. He leads projects involving FPGA-based accelerators, neural networks on microcontrollers, and real-time systems optimization. His work spans topics such as approximation computing, MPSoCs (Multiprocessor Systems-on-Chip), and IoT device architectures. Key contributions include methodologies for optimizing resource allocation in heterogeneous systems and developing energy-harvesting solutions for embedded systems. Teich has authored numerous publications in top-tier conferences and journals, including DATE, FPL, and ACM Transactions. His research often collaborates with industry partners, emphasizing practical applications and open-source hardware.

Markus Weinhardtمشاهده پروفایل

استاد

• Reconfigurable Computing
• Compiler Development
• Image Processing
• +۳ مورد دیگر

Prof. Dr.-Ing. Markus Weinhardt is a Professor at the Faculty of Engineering and Computer Science at Osnabrück University of Applied Sciences. His research focuses on reconfigurable computing, compiler development, and image processing. He earned his Ph.D. from Karlsruhe Institute of Technology (2000) and held postdoctoral positions at Imperial College London (2000) and PACT XPP Technologies AG (2009). He leads the DFG-funded HiPReP project and organizes workshops like FSP 2016. Education: Ph.D. in Computer Science (Karlsruhe Institute of Technology), postdoctoral research at Imperial College London, and industry experience at PACT XPP Technologies AG. Research Interests: Reconfigurable architectures, FPGA-based acceleration, compiler optimization, and high-performance computing. Projects include HiPReP (high-performance reconfigurable processor) and HPVis (software optimization via FPGA coprocessors). Teaching: Courses on Hardware/Software Codesign, Programming, and Master’s projects in compiler design and hardware optimization. Key Contributions: Over 30 publications, including works on CHiPReP compilers, dynamic scheduling in reconfigurable arrays, and FPGA-accelerated algorithms.

Florian Meinertمشاهده پروفایل

استاد پژوهش

• Quantum Physics
• Ultracold Atoms
• Rydberg Atoms
• +۴ مورد دیگر

Florian Meinert is a Group Leader at the 5th Institute of Physics within the University of Stuttgart , Germany. Since 2017, he has been leading a research group focused on quantum simulation and ultracold atomic systems. He previously held postdoctoral positions at the University of Stuttgart and the University of Innsbruck, and completed his PhD at the University of Innsbruck under Hanns-Christoph Nägerl. Education: Diploma in Physics, Albert-Ludwigs-University Freiburg & University of Ottawa (2005–2012) PhD in Physics, University of Innsbruck (2012–2016) Postdoc, University of Innsbruck & University of Stuttgart (2016–2017) Research Interests: Florian's work lies at the intersection of quantum optics , ultracold atoms , and quantum simulation . His group explores Rydberg atoms for quantum computing, Bose-Einstein condensates for many-body physics, and quantum chemistry at ultracold temperatures. He has pioneered techniques for coherent control of Rydberg qubits and in situ observation of quantum reactions . Publications & Trends: His recent work (2020–2025) focuses on scalable quantum computing platforms using neutral Rydberg atoms , including the development of long-lived qubits and optical tweezer arrays . He has also advanced the understanding of quantum chemistry in Rydberg systems and ion-atom interactions in ultracold environments. Awards & Honors: IQST Early Career Network Fellow (2019) Liechtenstein Prize (2018) Fellow of the Elite Program of the Baden-Württemberg Stiftung (2018) Carl-Zeiss-Foundation Postdoctoral Fellowship (2017) IQOQI Thesis Prize (2017) DPG SAMOP Thesis Prize (2017) LFUI Best Student Paper Award (2015) Studienstiftung Scholarship (2007–2012) Teaching & Supervision: Florian has taught Advanced Atomic Physics I & II at the University of Stuttgart. While no specific PhD students are listed, his role as group leader implies active supervision. Lab & Team: He leads the Meinert Group at the 5th Institute of Physics, University of Stuttgart, focusing on experimental quantum optics and ultracold atomic systems.

• Computing-in-Memory
• Resistive RAM (RRAM) Technology
• Neuromorphic Computing
• +۷ مورد دیگر

Dr. Moritz Joseph serves as Professor and Chair of Software for Systems on Silicon at RWTH Aachen University's School of Electrical Engineering and Information Technology. His research group operates within the Institute for Communication Engineering (ICE), focusing on cutting-edge hardware architectures for next-generation computing systems. The chair maintains laboratory facilities in the ICT Cube 1 building at Kopernikusstr. 16 in Aachen, Germany. Professor Joseph's research spans Computing-in-Memory architectures , Resistive RAM technologies , and neuromorphic computing systems . His work addresses critical challenges in hardware acceleration for deep learning, with particular emphasis on reliability, security, and energy efficiency of novel memory-centric computing paradigms. His research bridges computer architecture, hardware security, and machine learning acceleration. Analysis of his recent publications reveals a strong focus on RRAM-based compute-in-memory systems, with increasing attention to security implications of thermal effects and side-channel attacks. His work demonstrates progression from fundamental memory characterization to system-level integration and security analysis, showing consistent innovation in memory-centric computing architectures over the past five years. Professor Joseph actively contributes to the development of simulation frameworks and evaluation methodologies for emerging memory technologies, as evidenced by his work on platforms like CIM-Explorer, Ratatoskr, and gem5 extensions. His research has significant implications for energy-efficient AI hardware and secure computing systems.

• Quantum Computing
• Quantum Information Theory
• Machine Learning
• +۴ مورد دیگر

Dr. Mohammad H. Ansari is a Research Scientist and Principal Investigator at Forschungszentrum Jülich's Peter Grünberg Institute (PGI-2), specializing in Theoretical Nanoelectronics. He leads the Quantum Device Theory group, with dual affiliation at RWTH Aachen's Institute for Quantum Information. Education : PhD in Theoretical Physics (2008) from University of Waterloo, Canada Postdoc Training : University of Waterloo (2008-2010), Institute for Quantum Computing (2010-2013), Delft University of Technology (2013-2016) His research spans quantum computing , circuit QED , and non-equilibrium quantum field theory , with recent work focusing on quantum annealing applications , error mitigation techniques , and superconducting qubit optimization . Current projects include quantum thermodynamics and machine learning integration in quantum systems. Recent publications demonstrate trends in quantum hardware design (2024), entanglement engineering (2024), and quantum-classical hybrid models (2023). His work bridges fundamental physics with applied quantum technologies , including photovoltaic cell simulations and quantum neural networks . Scientific Awards John Brodie Prize, Perimeter Institute Member of Canadian Association of Physics (CAP), American Mathematical Society (AMS), and American Physical Society (APS) As advisor, Dr. Ansari has mentored 16 students including Alwin van Steensel (PhD 2022), Xuexin Xu (PhD 2021), and Manabputra (MSc 2021). Current advisees include Siyu Wang (PhD 2024) and Giorgi Kvantrishvili (MSc 2024). The Quantum Device Theory group (10 members) collaborates with MIT, UCSB, and Syracuse University, focusing on large-scale quantum device modeling , quantum thermodynamics , and quantum field theory applications . Recent projects include quantum annealing for chromatin domain prediction and entanglement-enhanced quantum processors.

Xueyue Zhangمشاهده پروفایل

استادیار

• Quantum Science and Technology
• Superconducting Quantum Circuits
• Solid-State Spin Systems
• +۴ مورد دیگر

Xueyue Zhang is an Assistant Professor at Columbia University 's Department of Applied Physics and Applied Mathematics. Previously, she was a Miller Postdoctoral Fellow at UC Berkeley. Her research focuses on quantum technology development using superconducting circuits and silicon-based spin-photon interfaces. Education : Ph.D. in Applied Physics (Caltech, 2023), B.Eng. in Electrical Engineering (Tsinghua University, 2017) Research Interests center around: Quantum hardware engineering for scalable processors Spin-photon interactions in silicon platforms Topological protection in photonic quantum systems Quantum many-body simulation architectures Non-local quantum connectivity solutions Recent Publications demonstrate expertise in superconducting quantum simulation, topological waveguide QED, and silicon defect engineering for quantum networking. Key trends include photonic bandgap metamaterial applications and telecom-compatible quantum emitter development. Awards include: 2023 Rising Star in Physics 2023 Boeing Quantum Creator Prize Academic Leadership involves mentoring a diverse team of graduate and undergraduate researchers at the Zhang Lab in Schapiro Center for Engineering and Physical Science Research (CEPSR). She collaborates with global experts like Alp Sipahigil and Oskar Painter.

• Computer Networks
• Cryptography
• Embedded Systems
• +۳ مورد دیگر

Fayez Gebali is a Professor in the Department of Informatics at the University of Bergen. His research focuses on secure communication systems, cryptographic hardware design, and embedded systems. He has contributed extensively to areas such as network security, signal processing, and wireless communication protocols. His work spans theoretical and applied domains, including hardware security for IoT devices, authentication protocols for vehicular networks, and radar signal processing. Key research interests include optimizing cryptographic algorithms for resource-constrained devices, analyzing hybrid communication networks (e.g., FSO/RF), and developing machine learning-based authentication systems for healthcare. He has collaborated with institutions like KAUST (via co-author Mohamed-Slim Alouini) and has published over 130 papers. His recent work emphasizes practical solutions for rural broadband access, satellite security, and combating misinformation in healthcare contexts. Notable contributions: PUFGuard authentication protocol, systolic array designs for modular multiplication, and secure multi-hop mobile communication frameworks. Focus areas: IoT security, radar waveform optimization, and low-power hardware implementations.

• Digital Signal Processing
• FPGA Implementation
• Machine Learning Hardware Acceleration
• +۴ مورد دیگر

Rocco Fazzolari is a Professor at Sapienza University of Rome's School of Engineering, Department of Electrical Engineering and Information Technology. With over 50 publications spanning from 2010 to projected 2025 works, he maintains an active research program focused on digital signal processing, hardware acceleration, and machine learning implementations. His research interests center on FPGA implementation of communication systems, with particular expertise in Residue Number System applications, QAM/PSK demodulation techniques, and reinforcement learning hardware accelerators. His work bridges theoretical signal processing concepts with practical hardware implementations, often targeting communications and control systems applications. Recent publications demonstrate increasing focus on machine learning integration with traditional signal processing techniques. Analysis of his publication trends shows consistent output across multiple venues including IEEE Access, IEEE Transactions on Circuits and Systems, and various conference proceedings. His collaborative pattern reveals a core research group including Gian Carlo Cardarilli, Luca Di Nunzio, and Marco Re, with whom he has co-authored over 45 publications each. He has also organized multiple academic conferences including the International Conference of Yearly Reports on Informatics, Mathematics, and Engineering (ICYRIME) and the Sapienza Yearly Symposium of Technology, Engineering and Mathematics. Research Recognition: Extensive publication record in IEEE journals and major conferences Organizer of multiple international academic conferences Consistent collaborative research output with established research group Fazzolari's work demonstrates strong application focus in communications systems, with recent expansion into machine learning hardware acceleration. His research group appears to maintain stable funding and productivity, with publications showing progression from fundamental digital circuit design to more complex machine learning integrated systems over the past decade.

• Data Management Systems
• Database Systems
• Operating Systems
• +۴ مورد دیگر

Prof. Jana Giceva Makreshanska is a Professor of Database Systems at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. Her research focuses on systems support for data management, leveraging modern hardware architectures including operating systems, compilers, and computer architecture. She holds a PhD from ETH Zurich (2017) and previously served as faculty at Imperial College London. Notable awards include the ERC Starting Grant (2024), VMware Early Career Faculty Award (2019), and ETH Medal (2018). Education: PhD in Computer Science, ETH Zurich (2017) Research roles at Microsoft Research and Oracle Labs Research Interests: Her work addresses holistic system design challenges in data management, including optimizing query processing for modern hardware, compiler integration, and emerging architectures like chiplets. She explores efficient data processing techniques for big data and transactional systems. Recent Contributions: Her recent work includes innovations in disaggregated systems (HotOS 2023), chiplet-aware scheduling (2025), and universal graph data structures (VLDB 2022). Her publications span topics like NVMe array optimization, FPGA-based partitioning, and hybrid OLTP/OLAP systems. Awards: ERC Starting Grant (2024) Best Paper Runner-Up at VLDB 2022 ACM SIGMOD Highlights Award (2023) Teaching & Service: Current courses include 'Cloud-Based Data Processing' and 'Data Processing on Modern Hardware'. She chairs diversity initiatives at SoCC 2023, serves on program committees for OSDI, VLDB, and SIGMOD, and leads gender equality efforts at TUM. Labs/Teams: Maintains the Lehrstuhl III (I3) database systems group at TUM, collaborating on projects like LakeVilla and ARCAS.

Konstantin Lübeckمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Computer Architecture
• +۳ مورد دیگر

Konstantin Lübeck is a Researcher at the Chair of Embedded Systems, Department of Computer Science, University of Tübingen. He holds a B.Sc. and M.Sc. in Computer Science from the same institution (2015, 2018), with academic focus on computer engineering and embedded systems. He studied at Uppsala University in 2016 as an Erasmus student and received a Stiftung Industrieforschung scholarship for his Master's thesis in 2017. B.Sc. and M.Sc. from University of Tübingen Erasmus exchange student at Uppsala University (2016) Stiftung Industrieforschung scholarship recipient (2017) His research centers on machine learning accelerator performance modeling , combining computer architecture descriptions (from register-transfer to abstract diagrams) with DNN parameters for rapid design space exploration in neural network-hardware co-design. Key methodologies include analytical models for latency, throughput, and roofline analysis of AI accelerators. The 8 listed publications (2016-2025) reveal trends in: Neural network-hardware co-design Abstract architecture modeling Ultra-low power AI accelerators Performance representatives for benchmarking Formal hardware description languages AutoML for accelerator optimization Scientific contributions include Stiftung Industrieforschung scholarship (2017) He has lectured on computer architecture since 2018 for the Bosch Learning Company initiative at Tübingen's technology transfer center and supervised 12+ theses projects (4 completed) involving Pico-CNN frameworks, cache modeling, RISC-V implementations, and systolic array architectures.

• Analog Integrated Circuits
• Mixed-Signal VLSI Design
• High-Speed Data Converters
• +۷ مورد دیگر

Yan Zhu is a faculty member at the University of Macau , affiliated with the Analog and Mixed Signal VLSI Laboratory within the Faculty of Science and Technology. She has a strong research focus on high-performance analog and mixed-signal integrated circuits, particularly in data conversion and low-power design. Her research interests include: Analog and Mixed-Signal VLSI Design High-Speed Data Converters (ADCs) Noise-Shaping and Time-Domain Circuits PVT-Robust and Low-Power Circuit Techniques Compute-in-Memory and AI Hardware Acceleration The recent publications of Yan Zhu demonstrate a clear trend toward advanced ADC architectures such as time-interleaved, pipelined-SAR, and time-domain converters, with a strong emphasis on calibration, linearity, and energy efficiency. Her work frequently appears in top-tier journals like IEEE JSSC and conferences like ISSCC and CICC, indicating leadership in the field of analog circuit design. There is also a growing focus on machine learning hardware, particularly analog compute-in-memory systems for edge AI applications. No scientific awards or honors are mentioned in the provided text. Yan Zhu has made significant contributions through collaborative research, particularly with Chi-Hang Chan and Rui Paulo Martins , and has been involved in numerous projects related to ADC calibration, metastability, and high-speed sampling. While specific grant details are not listed, the volume and quality of publications suggest active funding support. She has not listed any advisees in the provided data. She is a core contributor to the Analog and Mixed Signal VLSI Laboratory at the University of Macau, where her team focuses on cutting-edge IC design for communication, sensing, and artificial intelligence applications.

Hans Peter Büchlerمشاهده پروفایل

استاد

• Quantum Physics
• Quantum Computing
• Condensed Matter Physics
• +۳ مورد دیگر

Prof. Hans Peter Büchler is a Professor of Physics at the University of Stuttgart's Institute for Theoretical Physics III. His research focuses on theoretical and applied quantum physics, particularly in quantum computing architectures using Rydberg atoms, topological phases in quantum systems, and the simulation of quantum many-body systems. He leads investigations into fault-tolerant quantum computing, quantum error correction, and the realization of novel quantum states such as supersolids and Haldane phases. His work bridges theoretical advancements with experimental realizations, including projects like QRydDemo, a Rydberg-based quantum computing demonstrator. Key areas of exploration include Rydberg blockade structures, functional completeness in quantum gates, and the application of optimal control theory to quantum systems. Büchler’s contributions span quantum simulation, topological quantum error correction, and the study of collective quantum phenomena in cold atomic gases. Publications since 2020 highlight advancements in Rydberg-based quantum technologies, including scalable quantum computing schemes, topological order in symmetric blockade systems, and the realization of fine-structure qubits in alkaline-earth atoms. His research also addresses fundamental questions about phase transitions, quantum fluctuations in supersolids, and the interplay of topology and interactions in quantum materials.