جستجوی استاد راهنمای دانشگاهی

• Network Security
• Android Security
• Malware Analysis
• +۶ مورد دیگر

Vijay Laxmi is a Professor in the Department of Computer Science and Engineering at Malaviya National Institute of Technology (MNIT) Jaipur, India. With over a decade of active research publication from 2014-2024, Dr. Laxmi has established themselves as a prominent researcher in network security, Android security systems, and Network-on-Chip architectures. Their work demonstrates consistent collaboration with Manoj Singh Gaur and numerous doctoral students at MNIT Jaipur. Dr. Laxmi's research interests span Network Security, Android Security, Malware Analysis, Network-on-Chip Architectures, Routing Protocols, Side-Channel Attacks, Wireless Networks, and Mobile Security. Their work bridges theoretical security frameworks with practical implementations, particularly in mobile and embedded systems. Recent publications indicate a growing focus on AI-based security approaches including GAN applications for fuzzing and deep learning for image dehazing. The research trajectory shows increasing sophistication in security analysis techniques, evolving from basic malware detection to advanced side-channel attack analysis and sophisticated network security protocols. Recent publications demonstrate expertise in both theoretical frameworks and practical implementations with applications in real-world security challenges. Dr. Laxmi has mentored numerous graduate students including Vineeta Jain, Anugrah Jain, Sonal Yadav, Mohit Singh, and Gaurav Singal, who appear as co-authors across multiple publications. Their collaborative network extends to researchers at international institutions, indicating strong academic connections beyond their home institution.

• Microelectronics
• Nanotechnology
• RF Engineering
• +۳ مورد دیگر

Prof. Dr. Amelie Hagelauer holds a professorship in Micro- and Nanosystem Technology at the TUM School of Computation, Information and Technology, Technical University of Munich. Her work focuses on advanced electronics and systems integration across quantum computing hardware, resistive memory technologies, and high-frequency RF systems. She has contributed to innovations in superconducting qubit readout architectures, multi-level RRAM designs, and 3D-integrated CMOS-compatible quantum devices. Research interests span quantum hardware design, nanoelectronic devices, RF front-end systems, and emerging memory technologies. Her work emphasizes practical implementation challenges such as low-power operation, high-voltage handling in RF switches, and wafer-scale fabrication processes. Recent projects include D-band radar systems, energy-efficient 60 GHz transceivers, and antenna tuning solutions for 5G applications. Publications from 2023-2025 showcase advancements in resistive switching device characterization, mitigation of TLS losses in superconducting qubits, and reconfigurable AI accelerators using RRAM-based digital twins. Her work bridges theoretical device physics with practical integrated circuit design, addressing scalability and reliability in next-gen electronics. Awards and grants: None explicitly listed in provided texts. Active collaborations include EU-funded projects on quantum computing platforms and TUM's Electronic Photonic Integration initiatives. Leads research teams in microsystem technology with emphasis on cross-disciplinary approaches combining CMOS processes, MEMS, and quantum engineering.

• Neuromorphic Computing
• Hardware Accelerators
• VLSI Circuits
• +۵ مورد دیگر

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.

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +۳ مورد دیگر

Günter Rote is a Professor in the Department of Computer Science at Freie Universität Berlin, specifically within the Theoretical Computer Science group (Arbeitsgruppe Theoretische Informatik). He holds a formal academic title of Professor Dr. and is affiliated with the Faculty of Mathematics and Computer Science. His research focuses on theoretical computer science, computational geometry, algorithms, and discrete mathematics. Key research interests include geometric algorithms, optimization problems (e.g., shortest paths, traveling salesman problems), and algorithm design for parallel computing systems. His work spans topics such as systolic arrays, convex hulls, and combinatorial optimization. Rote’s contributions include foundational studies on computational geometry problems, algorithmic complexity, and practical applications in energy equity and infrastructure design. Publications highlight contributions to solving extremal equations, polygon transformations, and the quadratic assignment problem. He has been active in academic leadership, mentoring students, and contributing to computational science communities. His email is rote@inf.fu-berlin.de, and his office is located at Takustraße 9 in Berlin.

• Integrated Circuits
• Mixed-Signal Computing
• Neural Recording Systems
• +۱ مورد دیگر

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.

Jörg Henkelمشاهده پروفایل

استاد

• Embedded Systems
• Dependable Systems
• Low Power Design
• +۳ مورد دیگر

Prof. Jörg Henkel serves as Professor and Head of the Chair for Embedded Systems (CES) at Karlsruhe Institute of Technology (KIT), Germany. Previously a Senior Research Staff Member at NEC Laboratories Princeton, he holds a PhD from Braunschweig University (Summa cum Laude) and directs major research initiatives including the DFG's SPP 1500 program on 'Dependable Embedded Systems'. Research Focus: His work centers on embedded systems dependability , low-power design , and adaptive computing architectures . Through the SPP 1500 program and TR89 Collaborative Research Center, he pioneers fault-tolerance techniques and runtime adaptation for safety-critical applications, addressing energy efficiency challenges in hardware/software co-design. Scientific Recognition: IEEE Fellow (for contributions to low-power dependable embedded systems) Multiple Best Paper Awards (DATE 2008, ICCAD 2009, Codes+ISSS 2011/2014/2015) DAC 2014 Designer Track Best Poster Award DATE 2013 Best IP Award Leadership & Service: As Editor-in-Chief of IEEE Design&Test Magazine and former ACM TECS EiC, he shapes scholarly discourse. He has chaired premier conferences (ICCAD 2012/2013, ESWeek 2016) and delivered 10+ keynotes on embedded systems reliability. His DFG-coordinated projects secure substantial funding for national collaborative research. Technical Infrastructure: Leads the Chair for Embedded Systems (CES) within KIT's Institute of Technical Informatics, driving innovation in hardware/software co-design and invasive computing through the TR89 consortium.

• Embedded Systems
• Hardware-Software Co-Design
• Invasive Computing
• +۷ مورد دیگر

Prof. Dr.-Ing. Jürgen Teich is a full Professor and Chair for Hardware-Software Co-Design at the Department of Computer Science, Friedrich Alexander University Erlangen-Nuremberg (FAU). He serves as Head of Department Computer Science and Vice Dean of the Technical Faculty since August 2024, and has been Speaker of the FAU Research Center Embedded System Initiative (FAU ESI) since 2023. His educational background includes: Diploma degree in Electrical Engineering, University of Kaiserslautern (1989) Dr.-Ing. degree in Electrical Engineering, University of Saarland (1993) Habilitation (PD Dr.-Ing.) entitled "Synthesis and Optimization of Digital Hardware/Software Systems" (1996) Prof. Teich's research focuses on Embedded Systems , Invasive Computing , Hardware-Software Co-Design , and Reconfigurable Computing . His work spans from theoretical foundations to practical implementations, with particular emphasis on resource-constrained systems, many-core architectures, and energy-efficient computing. He has pioneered research in invasive computing paradigms that enable more efficient use of many-core processors by allowing applications to dynamically claim resources. His recent publications reveal a strong trend toward energy-efficient AI deployment on embedded devices , security of embedded systems , and novel memory technologies . There's a clear focus on practical implementations of machine learning on microcontrollers (TinyML), hardware acceleration for data processing, and innovative approaches to power management in self-powered systems. Among his notable scientific awards are: IEEE Fellow (since 2018) Member of Academia Europaea, Section Informatics (since 2011) Member of the National Academy of Science and Engineering (acatech) (since 2018) Member of the German Society of Humboldtians (since 2021) Prof. Teich has been Principal Investigator for numerous DFG-funded projects including SFB/Transregio 89 "Invasive Computing" (2010-2022), SFB 694, and multiple priority programs. He has coordinated large collaborative research efforts across Germany and internationally, with significant funding from DFG and other sources. His research group has produced influential work in embedded systems design and co-design methodologies. He leads the Hardware-Software Co-Design research group at FAU, which focuses on innovative approaches to embedded system design, invasive computing architectures, and efficient implementation of machine learning on resource-constrained devices. The group maintains strong collaborations with industry partners including Intel, Xilinx, and automotive companies.

Tsun-Ming Tsengمشاهده پروفایل

استاد

• Microfluidic Large-Scale Integration
• Optical Network-on-Chip
• Microfabrication
• +۳ مورد دیگر

Tsun-Ming Tseng is a Professor and principal investigator at the Chair of Electronic Design Automation at the Technical University of Munich (TUM). He leads the Emerging Technology Group and oversees multiple DFG/BMBF-funded research projects in the areas of microfluidic large-scale integration, optical network-on-chip design, and novel microfabrication techniques. Dr. Tseng's research focuses on design automation for emerging technologies, with particular expertise in three main areas: microfluidic large-scale integration, optical network-on-chip systems, and novel microfabrication processes. His work bridges the gap between electronic design automation and cutting-edge applications in bioengineering, photonics, and advanced manufacturing. His research group develops sophisticated algorithms and tools for optimizing design, reliability, and performance in these emerging domains. Analysis of Dr. Tseng's recent publications reveals a strong focus on practical implementation challenges in emerging technologies. His work spans both theoretical algorithm development and practical system implementation, with particular emphasis on reliability, performance optimization, and manufacturing considerations. The research shows increasing integration between different technology domains, particularly the convergence of microfluidics, optical networking, and electronic design automation. Dr. Tseng has been awarded multiple significant research grants including: "DE-TW-CloudWRONoC" (BMBF-NSTC project, PI, 2025-2028, EUR 797.7K) "DE-TW-PI3D" (BMBF-NSTC project, PI, 2024-2027, EUR 391.6K) "Physical Design for Microfluidic Large-Scale Integration" (DFG research grant, PI, 2024-2026, EUR 331.9K) Multiple other DFG and industrial projects totaling over EUR 3 million in funding He has successfully supervised numerous doctoral researchers and postdoctoral fellows, with current group members including Jiahui Peng, Debraj Kundu, Liaoyuan Cheng, and several others. Dr. Tseng leads the Emerging Technology Group at TUM, which focuses on developing design automation methodologies for next-generation technologies. The group maintains strong collaborations with international institutions, including partnerships with researchers in Taiwan and Hong Kong. The team operates state-of-the-art facilities for research in microfluidics, optical networking, and advanced microfabrication techniques.

• Optical Networks-on-Chip
• Wavelength-Routed Optical Networks
• Electronic Design Automation
• +۴ مورد دیگر

Zhidan Zheng is a researcher at the Technical University of Munich (TUM), working within the Chair of Electronic Design Automation led by Prof. Ulf Schlichtmann. His office is located in room 0509.05.911 at Arcisstr. 21, 80333 Munich, with direct contact available via email zhidan.zheng@tum.de and phone +49 (89) 289 - 23692. Zheng holds a Master of Science degree as indicated by his academic title M.Sc. and has been actively contributing to the field of optical interconnects and network-on-chip design. Zheng's research focuses on wavelength-routed optical networks-on-chip, with particular expertise in network topology optimization, fault tolerance mechanisms, waveguide routing algorithms, and bandwidth allocation strategies. His work addresses critical challenges in photonic integrated circuit design, including thermal variation effects, crosstalk mitigation, and lifetime extension for communication-intensive systems. Zheng has developed several innovative methodologies including ToPro+ for topology projection, LightR for fault-tolerant architectures, and WROXIM for network-level simulation. Analysis of Zheng's publication trends from 2021-2025 reveals a consistent focus on practical implementation challenges of optical networks-on-chip. His research has evolved from foundational topology design (Light, 2021) to increasingly sophisticated solutions addressing reliability (LightR, 2023) and comprehensive system integration (ToPro+, 2025). The work demonstrates strong collaboration with researchers including Mengchu Li, Tsun-Ming Tseng, and Ulf Schlichtmann across multiple high-impact venues including DAC, DATE, ICCAD, and ASP-DAC. Zheng actively contributes to the Electronic Design Automation research group at TUM, participating in projects related to analog EDA, emerging technologies, and optical networks. His research is situated within TUM's broader initiatives in photonic integration and high-performance computing architectures, working closely with Prof. Schlichtmann's team on funded projects in the optical NoC domain.

• Computer Architecture
• Hardware Acceleration
• Approximate Computing
• +۳ مورد دیگر

Akash Kumar is a Professor at Ruhr University Bochum, Germany, with affiliations to multiple institutions including TU Dresden and National University of Singapore. His research focuses on computer architecture, hardware acceleration, and approximate computing, with a strong emphasis on FPGA-based systems and neural network optimization. He has contributed extensively to embedded systems, mixed-criticality systems, and security in emerging technologies like reconfigurable nanodevices. His work spans cross-layer approximation methodologies, graph neural networks, and energy-efficient processing for edge AI. Collaborations with industry and academia highlight his leadership in VLSI design and secure hardware implementations. Notable contributions include frameworks for bounding time in mixed-criticality systems and resilient logic locking techniques. Research interests include hardware-software co-design, real-time systems, and efficient neural network architectures. His publications in top-tier conferences (DAC, DATE, FCCM) and journals (IEEE Trans. CAD, ACM TECS) underscore his impact in the field.

Stefan Hougardyمشاهده پروفایل

استاد

• Discrete Mathematics
• Combinatorial Optimization
• Approximation Algorithms
• +۶ مورد دیگر

Stefan Hougardy is a Professor at the Research Institute for Discrete Mathematics, part of the Mathematisch-Naturwissenschaftliche Fakultät at the University of Bonn. He is actively engaged in research and teaching, with a focus on discrete mathematics and combinatorial optimization. He contributes to academic governance through roles in examination boards, teaching mentoring, and faculty committees. Stefan Hougardy's research lies at the intersection of theoretical computer science and practical optimization. His primary interests include approximation algorithms, the Traveling Salesman Problem (TSP), Steiner trees, graph theory, and VLSI design automation. He develops efficient algorithms for NP-hard problems and analyzes their theoretical performance guarantees. His work often bridges theory and application, particularly in electronic design automation and mathematical programming. His recent publications demonstrate a strong focus on the complexity and approximation of combinatorial optimization problems. Key themes include the analysis of local search heuristics like k-opt for TSP, edge elimination techniques, fast matching algorithms, and optimal legalization in chip design. His work combines rigorous theoretical analysis with practical implementation and computational experiments. MPC 'Outstanding Paper of the Year' Award 2024 Stefan Hougardy supervises graduate students and leads seminars on discrete mathematics and optimization. He is involved in the Bonn International Graduate School of Mathematics and the Hausdorff Center for Mathematics, contributing to doctoral education and mentoring. While specific grant details are not listed, his sustained research output and leadership roles suggest active funding support. He also contributes to curriculum development and academic quality assurance through various institutional committees. He is affiliated with the Research Institute for Discrete Mathematics at the University of Bonn, a leading center for combinatorial optimization and algorithmic research. The institute is closely linked with the Hausdorff Center for Mathematics, fostering collaboration in discrete and applied mathematics.

H. Siegfried Stiehlمشاهده پروفایل

استاد

• Computer Vision
• Computational Neuroscience of the Visual System
• Cognitive Science
• +۳ مورد دیگر

Prof. Dr.-Ing. H. Siegfried Stiehl is a retired Senior Professor (until Sept 2021) at the Department of Informatics, University of Hamburg. He previously held roles including Dean of the Faculty of Mathematics, Computer Science, and Natural Sciences (2001–2006), Vice President for Research (2007–2013), and Head of the Image Processing Research Group. His academic journey includes a PhD from TU Berlin (1980) and a Habilitation in Computer Vision (1987). Education: 1973: Ing. Degree in Ingenieur-Informatik, Fachhochschule Furtwangen 1976: Diploma in Computer Science, TU Berlin 1980: Dr.-Ing. Dissertation on medical image processing, TU Berlin Research focuses on Computer Vision , Computational Neuroscience , and Cognitive Science , with contributions to medical image registration, 3D landmark detection, and biomechanical modeling. Key projects include the EU-funded 'COVIRA' consortium (1989–1995) and leadership in the SFB 950 'Manuscript Cultures' project (2015–2019). His 110+ publications span biomedical image registration, elastic deformation algorithms, and real-time signal processing. Notable collaborations include work with institutions like the University of Pennsylvania, University of Birmingham, and Philips Research. Leadership roles include organizing scientific events, serving on editorial boards (e.g., Biological Cybernetics), and founding the Interdisciplinary Nanoscience Center Hamburg (INCH) in 2001. His research has addressed challenges in neurosurgical interventions, VLSI implementation of neural networks, and interdisciplinary education.

Marc Reichenbachمشاهده پروفایل

استاد

• Integrated Circuits
• Embedded Systems
• VLSI Design
• +۷ مورد دیگر

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.

Tsung-Yi Hoمشاهده پروفایل

استاد

• Design Automation for Microfluidic Biochips
• Nanometer Integrated Circuits
• Embedded Systems
• +۳ مورد دیگر

Tsung-Yi Ho is a Professor in the Department of Computer Science at National Tsing Hua University, Taiwan. He holds the Hans Fischer Fellowship at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). His primary research focuses on design automation for microfluidic biochips and nanometer integrated circuits, emphasizing reliability, optimization, and interdisciplinary applications in bioengineering. Ho received his Ph.D. in Electrical Engineering from National Taiwan University in 2005. He has held positions at National Cheng Kung University and National Chiao Tung University before joining National Tsing Hua University. His work bridges algorithmic design with practical biochip fabrication, addressing challenges like contamination control, routing optimization, and fault tolerance in microfluidic systems. His research interests span design automation for emerging technologies, including paper-based biochips and 3D microfluidic architectures. He has pioneered methods for integrating hardware-software co-design principles into biochip development, enhancing both functionality and reliability. His contributions include novel routing algorithms, contamination mitigation techniques, and reliability-aware synthesis frameworks. Ho has authored over 100 publications, including influential papers in IEEE Transactions on CAD and ACM journals. He serves on the editorial boards of multiple top-tier journals and chairs professional chapters for ACM and IEEE. His awards include the Humboldt Research Fellowship, Dr. Wu Ta-You Memorial Award, and Best Paper Awards at VLSI Test Symposium and IEEE Transactions on CAD. His current projects involve optimizing control-fluidic co-design for paper-based biochips and developing AI-driven frameworks for microfluidic functionality prediction. He collaborates widely, leading cross-disciplinary initiatives at TUM-IAS and Taiwan's academic institutions.

Krishnendu Chakrabartyمشاهده پروفایل

استاد

• Testing and Design-for-Testability of Integrated Circuits
• Digital Microfluidics
• Biochips
• +۳ مورد دیگر

Krishnendu Chakrabarty is the John Cocke Distinguished Professor of Engineering and Professor of Computer Science at Duke University, chairing the Department of Electrical and Computer Engineering. His research focuses on integrated circuits testing, microfluidics, biochips, and cyberphysical systems. He holds honorary titles from institutions globally and has received over a dozen best-paper awards. Education: B.Tech from IIT Kharagpur (1990), M.S.E. and Ph.D. from University of Michigan (1992, 1995). Awards include Humboldt Research Award (2013), IEEE Computer Society Technical Achievement Award (2015), and AAAS Fellowship (2018). Research explores fault diagnosis in hardware, smart manufacturing, and biochip automation. His cyberphysical systems work includes hybrid microfluidic platforms for single-cell analysis and droplet-based bioassays. Collaborations with TUM-IAS focus on Microfluidic Design Automation (MDA). Editor-in-Chief of IEEE Transactions on VLSI Systems and former Editor of ACM Journal on Emerging Technologies. Active in academic leadership and interdisciplinary projects, emphasizing sustainability and advanced manufacturing.