جست‌وجوی استاد و پژوهشگر

• Memristive Devices
• Printed Electronics
• Non-Volatile Memory
• +۳ مورد دیگر

Dr. Hongrong Hu is a Research Fellow at the Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), Germany, affiliated with the Electronic Devices and Systems research unit. Her work focuses on advancing printed memristive technologies for next-generation memory applications. Her research expertise spans: Memristive Devices and Resistive Random-Access Memory (ReRAM) Printed Electronics Fabrication (Inkjet/Laser Printing) Non-Volatile Memory Systems Metal-Oxide Semiconductor Materials High-Entropy Compounds for Memory Neuromorphic Computing Hardware Analysis of her 2021-2025 publications reveals a strategic progression from fundamental device characterization (e.g., noise properties in printed transistors) toward sophisticated material engineering (high-entropy Prussian Blue analogs, metal-organic frameworks) and neuromorphic applications. Her work consistently bridges materials science, electrical engineering, and nanofabrication to solve scalability challenges in printed memory devices. Scientific recognition: No awards or fellowships documented in available sources Dr. Hu's academic mentoring and grant activities are not publicly detailed, though her collaborative publications suggest active participation in KIT's research ecosystem. She contributes to the Electronic Devices and Systems unit's mission of developing innovative electronic solutions through printed and flexible technologies for real-world applications.

• Phase Change Memory
• Neuromorphic Computing
• Very Large Scale Integration
• +۴ مورد دیگر

Houpeng Chen is a Research Professor at the Chinese Academy of Sciences, specifically affiliated with the School of Microsystem and Information Technology in the Department of Microelectronics. With over two decades of research experience since the early 2000s, Chen has established himself as a leading expert in memory systems and circuit design, particularly in the areas of Phase Change Memory and neuromorphic computing. Chen's research primarily focuses on advanced memory technologies, with particular emphasis on Phase Change Memory (PCM) systems, neuromorphic computing architectures, and analog circuit design for memory applications. His work spans from fundamental circuit design for memory systems to advanced computing architectures that leverage novel memory technologies. A significant portion of his recent work explores in-memory computing paradigms and brain-inspired computing systems, demonstrating a strategic shift toward next-generation computing architectures that address the limitations of traditional von Neumann systems. Analysis of Chen's publication record shows a clear evolution from traditional circuit design toward more innovative memory-based computing architectures. His recent work demonstrates strong expertise in 3D cross-point memory systems, in-memory computing, and neuromorphic hardware implementations. The research shows consistent quality with publications in top-tier IEEE journals and conferences, indicating strong recognition within the semiconductor and memory research community. As evidenced by the authorship patterns in his publications, Chen has successfully mentored numerous graduate students and junior researchers who have gone on to become first authors on significant publications. His collaborative network includes extensive work with Zhitang Song, Qian Wang, and Xi Li, suggesting a well-established research group with strong internal collaboration.

• Database Systems
• Data Engineering
• Distributed Systems
• +۴ مورد دیگر

Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.

Reza Salkhordehمشاهده پروفایل

مدرس

• Operating Systems
• Solid-State Drives
• Data Storage Systems
• +۵ مورد دیگر

Reza Salkhordeh is a Lecturer and Postdoctoral Researcher at Johannes Gutenberg University Mainz, Germany, where he leads the Efficient Computing and Storage Group. He holds a Ph.D. in Computer Engineering from Sharif University of Technology (2018) and has been affiliated with Ferdowsi University of Mashhad and Sharif University of Technology. His research focuses on operating systems, storage systems, and non-volatile memory technologies, with a particular emphasis on high-performance computing and I/O optimization. He has taught courses such as Storage Systems and Advanced Topics in Operating Systems since 2020. His academic journey includes a B.Sc. from Ferdowsi University (2011), M.Sc. and Ph.D. from Sharif University (2013, 2018). He has held roles such as Technical Lead for the High-Performance Data Storage System (HPDS) project in Tehran, Iran, and has mentored 4 M.Sc. and 6 B.Sc. students. His work spans patented technologies like Reconfigurable Cache Architectures and Load Balancers for I/O caching systems. Research interests include heterogeneous memory management, storage system design, and optimizing I/O performance in distributed environments. His recent publications address challenges in NVMM utilization, garbage collection in SSDs, and I/O tracing for HPC systems. He is actively involved in conference committees (e.g., FAST, ARCS, SC) and has reviewed for top journals like IEEE TPDS and ACM Transactions on Storage. Notable recognitions include membership in Iran’s National Elites Foundation (2012–2015) and top rankings in national exams (3rd in PhD, 35th in MSc). His contributions to storage systems have advanced enterprise-grade architectures and decentralized file systems, with a focus on practical implementations for modern computing challenges.

• Integrated Circuits
• RF Circuit Design
• Low-Power Electronics
• +۳ مورد دیگر

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.

• Machine Learning
• Medical Image Analysis
• Computer Vision
• +۵ مورد دیگر

Junxi Chen is an academic affiliated with Dalian University of Technology's School of Software. His research focuses on advanced machine learning techniques, medical image analysis, and computer vision applications in healthcare. He has contributed significantly to adversarial machine learning, robust deep learning models, and medical image segmentation. His work bridges theoretical advancements with practical applications in medical diagnostics and data security. Key research areas include adversarial attacks/defense mechanisms, medical imaging segmentation (e.g., skin lesions, tumors), and innovative neural network architectures like UNet variants. He also explores secure data-hiding techniques in encrypted images and hybrid memory systems optimization. Publications span top venues such as CVPR, IEEE Transactions, and Medical Image Analysis journals. Collaborations with institutions like Dalian University of Technology and international co-authors highlight his global research impact.

• Spintronics
• Unconventional computing devices
• Neuromorphic computing
• +۹ مورد دیگر

Dr. Jae-Chun Jeon is a leading experimental physicist at the Max Planck Institute of Microstructure Physics in Halle, Germany, where he works within the NISE department (Nano-Systems from ions, spins and electrons). He joined the institute in 2018 after completing his postdoctoral fellowship at the University of Alberta, Canada. His research focuses on spintronics, unconventional computing devices, and cryogenic systems, with emphasis on developing novel memory and logic technologies for next-generation computing. Dr. Jeon earned his Ph.D. in condensed matter physics from the University of Alberta, Edmonton, Canada, in 2016. He continued his research as a postdoctoral fellow at the same institution, focusing on strongly correlated magnetic oxide materials for spintronic and neuromorphic applications before joining the Max Planck Institute. Dr. Jeon's research centers on unraveling the physics of complex systems including spintronics, correlated oxides, and atomically engineered materials to discover their advanced functionalities. He specializes in manipulating spin textures and states using spin electrons, with particular focus on current-induced domain wall motion and spin-orbit torque-induced magnetization switching for memory and logic applications. His work explores the potential of racetrack memory for both conventional binary memory and unconventional analogue systems such as probabilistic-bit and neuromorphic devices. His recent publications demonstrate a strong focus on advanced spintronic devices, particularly racetrack memory systems, domain wall logic, and Josephson junctions for quantum applications. The research spans fundamental physics of chiral domain walls, spin-orbit torque effects, and novel materials for spin-based computing. His work frequently appears in high-impact journals including Science, Nature family journals, and Advanced Materials, reflecting the significance of his contributions to the field. Dr. Jeon's research is supported by cutting-edge facilities including atomically thin film deposition systems, state-of-the-art electronics, and advanced device fabrication techniques such as electron beam lithography and ion beam etching/deposition. His work involves close collaboration with the research group led by Prof. Stuart Parkin, a director at the Max Planck Institute of Microstructure Physics.

• Neuromorphic Electronics
• Neuroscience
• Biointerfaces
• +۳ مورد دیگر

Dr. Hongwei Tan serves as Group Leader at the Department of Molecular Electronics, Max Planck Institute for Polymer Research (MPIP) in Mainz since November 2024. Previously, he was Research Fellow (2022-2024) and Postdoctoral Fellow (2017-2022) at Aalto University's Department of Applied Physics, following postdoctoral work at the University of Massachusetts Amherst on neurointerface devices. His academic credentials include: Bachelor of Physics, Nankai University (2011) Doctorate in Materials Physics and Chemistry, University of Chinese Academy of Sciences (2016), focusing on neuromorphic photomemristors Tan's research pioneers neuromorphic biointerfaces—integrating brain-inspired electronics with biological systems to enable energy-efficient computation and seamless biointeraction. His work targets transformative applications in brain-machine interfaces, prosthetics, and diagnostics through interdisciplinary convergence of neuroscience, materials science, and engineering. His publication portfolio (2015-2024) reveals a clear evolution from foundational optoelectronic memristor research toward complex neuromorphic systems for sensory processing. Key thematic trajectories include photomemristor-based vision systems, tactile coding with spiking nerves, and nanoionic human-machine interfaces, demonstrating consistent innovation in bioinspired hardware. As MPIP Group Leader, Tan directs research strategy and team mentorship within his neuromorphic electronics program, though specific advisees and grant details remain unlisted in available materials. His laboratory focuses on developing integrated neuromorphic systems—from novel materials and devices to algorithms—that facilitate bidirectional communication between electronic and biological neural networks for next-generation neurotechnologies.

Alastair F. Donaldsonمشاهده پروفایل

استاد

• Programming Languages
• Compilers
• Verification
• +۵ مورد دیگر

Alastair F. Donaldson is a Professor in the Department of Computing at Imperial College London, where he leads the Multicore Programming Group. His primary affiliation is with Imperial College London's Department of Computing within the broader Faculty of Engineering structure. He serves as a Program Committee Member for major conferences including ASE, PLDI, and POPL. His research spans Programming Languages , Compilers , Verification , Testing , and Multicore Programming , with significant contributions to randomized testing techniques. He pioneered GraphicsFuzz (acquired by Google in 2018) and developed innovative approaches like grammar mutation for parser testing, metamorphic fuzzing for C++ libraries, and specialized tools for GPU API validation. His work bridges theoretical foundations with industrial impact, particularly in compiler correctness and GPU computing. Analysis of his recent publications reveals a strong trend toward fuzzing infrastructure development (40%), GPU/compiler testing (30%), and formal methods integration (30%). His research increasingly focuses on large-scale automated testing for complex systems including WebGPU, Dafny, and Rust, while maintaining rigorous theoretical grounding in concurrency models and memory semantics. Donaldson has held significant leadership roles including General Chair for PLDI 2020 and Program Chair for ECOOP. His research has been supported through conference organizational roles and industrial collaborations, notably the GraphicsFuzz spinout. He actively contributes to the PL community through mentoring initiatives like PLMW and community-building efforts such as The PLDI Song. He leads the Multicore Programming Group at Imperial College London, focusing on practical tools for compiler and GPU driver validation. The group's work combines theoretical program analysis with real-world testing frameworks, maintaining strong industry connections through projects adopted by Google and other technology companies.

• Materials Science
• Thin Film Technology
• Nanotechnology
• +۷ مورد دیگر

Prof. Lambert Alff is an affiliate member in the Department of Materials Science at Technische Universität Darmstadt. His research focuses on advanced materials engineering, particularly in thin film technology, memristive systems, and energy storage solutions. He specializes in epitaxial growth techniques (Molecular Beam Epitaxy, Pulsed Laser Deposition) and investigates properties of ferroelectric oxides, superconductors, and nanomaterials. His work spans applications in microwave devices, neuromorphic computing, and high-performance batteries. Key research areas include defect engineering in perovskite oxides, spin dynamics in magnetic thin films, and radiation effects on electronic devices. He collaborates on projects like the WAKeMeUP initiative, exploring defect-stabilized materials and phase transitions under extreme conditions. His lab develops tunable varactors, memristive memory arrays, and hybrid materials for environmental remediation. Alff's team employs advanced characterization tools such as in-situ TEM, 4D-STEM, and X-ray spectroscopy. His recent studies address challenges in high-frequency electronics (sub-6 GHz), superconducting RF cavities, and stable Li-ion cathodes through surface modification strategies.

Saeideh Shirinzadehمشاهده پروفایل

پژوهشگر

• Microelectronics
• Computer Architecture
• Hardware Systems
• +۳ مورد دیگر

Dr. Saeideh Shirinzadeh is a Researcher at the Department of Emerging Technologies, Fraunhofer Institute for Systems and Innovation Research ISI. She holds a PhD (summa cum laude) in Computer Science from the University of Bremen, Germany (2018), and prior to her current role, she was a post-doctoral researcher at DFKI Bremen. Her work focuses on emerging microelectronics technologies, particularly in-memory computing architectures and non-volatile memory systems. Education: BSc/MSc (Electrical Engineering, University of Guilan, Iran), PhD (Computer Science, University of Bremen) Her research interests center on optimizing hardware systems using novel memory technologies, with applications in VLSI design, IoT, and energy-efficient computing. She has contributed to projects like FRAME (FMD innovation support) and the Graphene Flagship roadmap. Her publications span topics like logic synthesis for RRAM-based systems, endurance management in resistive memories, and PLiM architectures for IoT. She has received academic recognition including the summa cum laude distinction for her doctoral thesis. Current work involves analyzing technological trends in microelectronics and supporting innovation strategies for industrial applications.

Joachim Falkمشاهده پروفایل

پژوهشگر

• Electronic System Level Design
• Hardware-Software Co-Design
• Data-Flow Graphs
• +۶ مورد دیگر

Joachim Falk is a researcher at the Department of Hardware-Software Co-Design (Computer Science 12) at Friedrich Alexander University Erlangen-Nuremberg. With over 20 years of experience since joining the department in 2004, he specializes in electronic system level design, dataflow programming, and hardware-software co-design, contributing significantly to the field through publications, teaching, and research leadership. His educational background includes: Doctorate degree (Dr.-Ing.) in Computer Science from FAU (2014) Diploma degree in electrical engineering (data processing) from Georg-Simon-Ohm University of Applied Science Nuremberg (2002) Falk's research focuses on Electronic System Level Design, Hardware/Software Code Generation for Data-Flow Graphs, Compiler Optimizations, and Parallel Architectures. His work bridges theoretical computer science with practical embedded systems implementation, particularly through the SystemC framework and his contributions to the SysteMoC language. He actively explores energy-efficient computing approaches for dataflow networks and embedded systems, with recent work emphasizing self-powering networks, clock and power gating techniques, and multi-reader buffer implementations for heterogeneous architectures. His recent publication trends reveal a consistent focus on optimizing dataflow networks for energy efficiency and performance. Key themes include self-powering dataflow networks, innovative clock and power management techniques, buffer management strategies for heterogeneous many-core systems, and invasive computing approaches. His research demonstrates a strong connection between theoretical models and practical implementation challenges in embedded systems design. Dr. Falk teaches courses including 'Entwicklung interaktiver eingebetteter Systeme' (Development of Interactive Embedded Systems) for Winter Semester 2024/2025 and 'SystemC' for Summer Semester 2024, supervising multiple theses on security modeling at the electronic system level and sleep/wake-up strategies for hardware implementations of dataflow networks. His research is conducted within the framework of projects like SysteMoC (representation of computational models in SystemC) and SystemCoDesigner (design space exploration for embedded systems).

Chenchen Liuمشاهده پروفایل

استادیار

• High-performance computing for machine learning
• Brain-inspired computing
• Hardware-software co-design
• +۴ مورد دیگر

Dr. Chenchen Liu is an Assistant Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She is affiliated with the Computing Compass Laboratory and holds a Hans Fischer Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) under the focus group Enabling Neuromorphic Computing for Multi-Tenant AI. Her work bridges hardware-software co-design with neuromorphic computing. Ph.D., Electrical and Computer Engineering, University of Pittsburgh (2017) M.S., Electrical and Computer Engineering, Peking University (2013) Her research focuses on high-performance computing for machine learning through novel computer architecture and system designs, brain-inspired computing, machine learning security, non-volatile memory, and VLSI design. Key areas include: Neuromorphic hardware resilience and optimization Memristor-based neural network architectures Runtime scheduling for multi-tenant AI Security in neuromorphic computing Energy-efficient memory systems Recent publications explore memristor defect tolerance, ReRAM-based CNN training efficiency, and spiking network quantization. The work emphasizes hardware-software co-design for AI acceleration. NSF Career Award (2023) Best Poster Award, Machine Learning and Systems Conference (2022) Best Paper Award, IEEE Symposium on VLSI (2014) She contributes to academic service as TPC Chair/Track Chair for DAC, GLVLSI, Cloud Summit conferences and serves as Associate Editor for IEEE Transactions on Circuits and Systems (TCAS-1) and Neurocomputing journal.

Lars Breuerمشاهده پروفایل

پژوهشگر

• Materials Science
• Condensed Matter Physics
• Nanotechnology
• +۳ مورد دیگر

Lars Breuer is a Principal Investigator at the Collaborative Research Centre 1242 (CRC 1242) at the University of Duisburg-Essen, focusing on particle-induced excitations in the time domain. His work centers on interactions between highly charged ions and low-dimensional materials, particularly graphene, MoS2, and black phosphorus systems. Key Research Areas: Ion irradiation effects, ultrafast electronic response of 2D materials, charge exchange dynamics, and surface modification at atomic scales Techniques: Time-resolved spectroscopy, supersonic gas beam ion sources, sputtering analysis, and pump-probe experiments Recent publications highlight collaborations with Marika Schleberger's team and advancements in understanding nonequilibrium electron dynamics (2024) and ion-induced memory effects in 2D transistors (2023). His work bridges fundamental physics and applied material engineering through precise ion-beam manipulation. Notable contributions include: Developing ultrashort ion pulse generation for time-domain studies Characterizing defect-induced properties in Janus MoSSe structures Investigating metal-2D material interfaces for optoelectronic applications

Ilia Petrovمشاهده پروفایل

استاد

• Data Management
• Data Analytics
• High Performance Computing
• +۴ مورد دیگر

Prof. Ilia Petrov is a faculty member at Reutlingen University's Faculty of Computer Science and Engineering (INF), leading the Data-Management and -Analysis department. He holds the academic rank of Professor and has been affiliated with the university since 2012. His research focuses on advancing data management and analytics, particularly in the realms of database systems, hardware-optimized storage, and near-data processing. He leads the Daten-Management Labor (Data Management Lab), which explores innovations in computational storage, flash memory optimization, and high-performance database architectures. His research interests include the development of novel storage technologies, such as native computational storage and processing-in-memory systems, alongside efficient indexing methods (e.g., B-Tree variants) for multi-version databases. He has contributed to projects like DANSEN and nativeNDP, which aim to leverage modern hardware for accelerating database operations. Key themes in his work involve minimizing write amplification on flash storage, optimizing HTAP workloads, and enabling efficient near-data processing through FPGA and NVMe integration. Prof. Petrov's publications span topics including computational storage architectures, Bloom filter optimizations for range queries, and concurrency control in large-scale multi-socket systems. His work emphasizes the synergy between database software and hardware innovations, with applications in enterprise systems and big data analytics. He has collaborated on frameworks like NVMulator for non-volatile memory emulation and Cache-coherent shared locking mechanisms for smart storage systems. His lab's activities include the development of KV-store accelerators (e.g., nKV) and cost models for query optimization in key-value systems. He has also explored blockchain data structures and snapshot isolation techniques for database transactions. Current trends in his research address the challenges of real-time data processing, cross-layer data formats, and the integration of emerging storage technologies like computational storage drives.