Find Academic Supervisors

Ralf RamsauerView profile

Research Fellow

• Modern Operating System Architectures
• Embedded Systems
• Embedded Virtualisation / Hardware Partitioning
• +3 more

Dr.-Ing. Ralf Ramsauer is a postdoctoral researcher at the OTH Regensburg , affiliated with the Systems Architecture Research Group . His work focuses on Modern Operating System Architectures , Embedded Systems , and Mixed-Criticality System Design , with a particular interest in hardware/software co-design and real-time constraints. Academic Rank: Research Fellow Email: ralf.ramsauer@oth-regensburg.de Ramsauer's research explores Embedded Virtualisation , Hardware Partitioning , and Quantum-Classical Co-Design , often leveraging open-source development practices. His publications reveal trends in Database/OS Stack Optimization , Security Vulnerability Analysis , and Real-Time System Evaluation . Scientific Awards: 2013: Best Graduation Award in Computer Engineering, OTH Regensburg He has supervised numerous students across Bachelor's and Master's theses , including work on RISC-V Implementation , Linux Kernel Modules , and Hypervisor Optimization .

Alin-Sasa TisanView profile

Senior Lecturer

• Neuromorphic Systems
• Pervasive Healthcare
• Pattern Recognition
• +9 more

Alin-Sasa Tisan is a Senior Lecturer in the Department of Electronic Engineering at Royal Holloway, University of London. His research focuses on neuromorphic systems, pervasive healthcare, and sensor-based applications. He holds a PhD in Electronic Engineering from Technical University of Cluj Napoca (2008) and has certifications in teaching and learning from Anglia Ruskin University (2012). Key research areas include neuromorphic designs for smell recognition, body hydration monitoring, low-cost air quality systems, and FPGA-accelerated signal processing. His work contributes to UN Sustainable Development Goals related to affordable and clean energy, industry innovation, and climate action. Recent projects include developing air quality monitoring systems for Tanzania, creative sensing methods for indoor environments, and cross-disciplinary approaches to industrial electronics challenges. He serves as an External Examiner at the University of Huddersfield and has supervised multiple collaborative projects involving international teams.

Ayesha KhalidView profile

Senior Lecturer

• Hardware Security
• Post-Quantum Cryptography
• Lattice-Based Cryptography
• +5 more

Dr. Ayesha Khalid is a Senior Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen’s University Belfast (QUB), leading research in post-quantum cryptography within the Centre for Secure Information Technologies (CSIT). She holds a PhD from RWTH Aachen University (Germany), supported by a DAAD scholarship. Her expertise focuses on secure, efficient hardware/software implementations of lattice-based cryptography for embedded systems and IoT devices. Key research contributions include developing timing-attack resilient Gaussian samplers, lightweight post-quantum schemes for constrained environments, and hybrid quantum-safe communication protocols. She has led EU H2020 projects (SAFECrypto) and Innovate UK initiatives (AQuaSec), and collaborates with institutions like Rolls-Royce and Purdue University. Her work addresses vulnerabilities in cryptographic hardware through side-channel analysis and fault injection countermeasures. Dr. Khalid is an IEEE Senior Member, IACR member, and serves on technical committees for conferences like VLSI-SoC and ISCAS. Awards include the EEECS Teaching Fellowship (2017). She actively contributes to standards development and has delivered tutorials at FPL 2022 and Schloss Dagstuhl seminars. Her research bridges theoretical cryptography with practical implementation challenges in the quantum era. Education: PhD (Magna Cum Laude) in Applied Cryptography from RWTH Aachen University Key Projects: EPSRC UK Quantum Communications Hub, Rolls-Royce Cyber Security Collaboration Expertise: FPGA-based cryptographic accelerators, post-quantum algorithm hardening, and secure embedded systems design

• Information Security
• Cybersecurity
• Software Testing
• +7 more

Dr. Stephan Neuhaus is a Senior Lecturer at the Zurich University of Applied Sciences (ZHAW), School of Engineering, where he contributes to the Research Focus Information Security. His work bridges applied research and practical implementation in cybersecurity, with a focus on software security, embedded systems, and human factors. Research Focus: Information Security Institution: ZHAW School of Engineering Location: Winterthur, Switzerland His research interests span information security, software testing, embedded systems security, network transparency, and usable security. He investigates how security mechanisms can be effectively implemented in real-time systems and how users interact with security-critical systems like password entry and phishing detection. His recent publications, from 2016 to 2025, reflect a strong trend in applied cybersecurity research. Key themes include fuzzing for GNSS modules, real-time security policies using BPF on FPGAs, privilege handling frameworks, and network observability. These works appear in top venues such as IEEE and ACM conferences, demonstrating technical depth and relevance to both industry and academia. Dr. Neuhaus has led and contributed to several applied research projects, including Malicious Node (lateral movement detection), OptiPhish (phishing awareness training), ATLAS (data management for pharma), and Identity Management for Wireless Networks. These projects highlight his leadership and collaboration in interdisciplinary, real-world security challenges. He is actively involved in research teams and collaborates with colleagues across institutions, particularly in projects involving secure embedded systems and network security. His work often involves FPGA implementations, software toolchains, and human-centered security evaluation.

• Embedded Systems
• Machine Learning
• Internet of Things (IoT)
• +3 more

Prof. Dr. Gregor Schiele is a Full Professor at the University of Duisburg-Essen , where he leads the Embedded Systems Department of Computer Science since 2014. His research focuses on self-organizing embedded systems in the Internet of Things (IoT), with emphasis on local embedded intelligence using deep learning and neural networks, as well as programming abstractions for adaptive systems . PhD in Computer Science (2007) from the University of Stuttgart Prior affiliations: Insight Centre for Data Analytics (Ireland), National University of Ireland, Galway, University of Mannheim Current research explores Embedded Machine Learning , FPGA-based AI Accelerators , and Energy-Efficient IoT Systems , with recent publications on quantization-aware training , configuration-aware power management , and medical signal processing . His 15 most recent articles highlight expertise in FPGA optimization , soft sensors , and adaptive architectures for time-series forecasting and fluid flow estimation . Key collaboration networks include the Elastic AI Ecosystem , ZaKI.D , and TransfAIr projects. He teaches courses like Programmieren in C , Internet of Things: Protocols and System Software , and Embedded Systems , while supervising research assistants and student teams in the IoT Garage initiative.

Christoph NiemannView profile

Researcher

• Reliability Engineering
• Nanometer CMOS Technologies
• FPGA Design and Optimization
• +5 more

Dr.-Ing. Christoph Niemann is a researcher at the Institute of Computer Engineering within the School of Computer Science and Electrical Engineering at the University of Rostock. His work bridges computer engineering and biomedical applications, with a focus on hardware design for medical devices and energy-efficient computing systems. Dr. Niemann's primary research interests include reliability and aging in nanometer CMOS technologies and contributions to increasing the energy efficiency of FPGA-based applications . His work spans both theoretical hardware design and practical biomedical implementations, particularly in the field of deep brain stimulation systems. His research demonstrates a unique intersection of computer engineering principles applied to critical medical technologies. Analysis of Dr. Niemann's recent publications (2020-2025) reveals a strong focus on energy efficiency in implantable medical devices, particularly for deep brain stimulation applications. His work combines FPGA optimization techniques with biomedical requirements, creating hardware solutions that address the stringent power constraints of implantable systems. The publications show consistent collaboration with Dirk Timmermann, Christian Haubelt, and Franz Plocksties, indicating a well-established research group focused on medical device engineering. Dr. Niemann has made significant contributions to the STELLA platform for deep brain stimulation research and has developed novel approaches for sensor-based adaptive voltage scaling on FPGAs. His work demonstrates both theoretical innovation in hardware design and practical implementation in medical contexts.

Santosh NagarakatteView profile

Professor

• Compilers
• Programming Languages
• Verification
• +4 more

Santosh Nagarakatte is a Professor and Undergraduate Program Director at the Department of Computer Science, Rutgers University, New Brunswick. He joined Rutgers in January 2013 after obtaining his Ph.D. in Computer Science from the University of Pennsylvania. His research spans hardware-software interfaces including compilers, programming languages, verification, and computer architecture, with a focus on building efficient, robust, and secure software systems. Education: Ph.D. in Computer Science, University of Pennsylvania His research interests include compilers, programming languages, verification, computer architecture, numerical methods, and formal methods. He leads projects on correctly rounded math libraries (RLIBM), verified eBPF ecosystems, lightweight formal methods for LLVM, and tools for parallel programming and debugging. His publications demonstrate a consistent focus on numerical accuracy, verification, and systems building, with recent work emphasizing correctly rounded math functions, eBPF verification in the Linux kernel, and debugging tools for numerical errors. This research bridges theoretical foundations with practical implementations across compilers, architectures, and security domains. Scientific Awards: ACM Distinguished Member (2023) eBPF Foundation Research Gift (2024) Intel Corporation Research Gift (2024) ACM SIGPLAN Dissertation Awards (2018, 2022 as advisor) Distinguished Paper Awards at PLDI, POPL, CGO, ICSE NSF CAREER Award (2015) Google Research Award (2014) IEEE Micro Top Picks (2010, 2013) He leads the Research on Abstractions for Programming and Learning (RAPL) group, advising PhD and Master's students. His research is supported by grants from NSF, Intel, Google, Facebook, and the eBPF Foundation, focusing on verified systems, numerical accuracy, and compiler technologies.

Juan Escárate MuñozView profile

Researcher

• Machine Learning for Control Systems
• Reinforcement Learning
• Electrical Motor Drives
• +6 more

Juan Escárate Muñoz is a doctoral researcher affiliated with the Electromechanics and Power Electronics (EPE) group in the Electrical Engineering department at Eindhoven University of Technology (TU/e) . His work focuses on integrating artificial intelligence with control systems to enhance electrical motor drives. Specializing in reinforcement learning for real-time optimal control Developing FPGA-based acceleration of AI algorithms Contributing to energy-efficient industrial systems Research trends include magnetic saturation effects , cross-coupling dynamics , and adaptive control strategies . Key applications span motor drives and power electronics . Collaborates with the EAISI institute and contributes to the UN Sustainable Development Goals (SDG7: Affordable Energy; SDG9: Industrial Innovation).

• Signal Processing
• Machine Learning
• Computer Science
• +3 more

Tao Zhang is a researcher affiliated with Westlake University , focusing on Computer Science and Signal Processing . He holds a PhD from Tianjin University (2004) and has contributed extensively to areas like Machine Learning , Deep Learning , and Optimization Algorithms .

• Embedded Systems
• FPGA
• High Performance Computing
• +5 more

Alberto Dassatti is a Full Professor at HES-SO University of Applied Sciences Western Switzerland and Director of the Reconfigurable & Embedded Digital Systems (REDS) Institute. He is affiliated with the School of Engineering and Management of the Canton of Vaud. His work focuses on embedded systems, FPGA applications, high-performance computing, and topological data analysis. He has led and collaborated on numerous projects funded by institutions like Innosuisse and HES-SO Rectorat, totaling over CHF 1.2 million in grants. His research spans computational storage systems, medical sensor technologies, and signal processing frameworks for wireless communication. Key projects include developing the CarbonViz energy visualization tool, advancing topological methods for AI reliability, and creating the BB-Sensors system for neonatal vital monitoring. He has contributed to open-source frameworks like giotto-tda and gr-dnn, emphasizing interdisciplinary collaboration between hardware and software domains. His publications span topics such as FPGA-GPU communication, real-time embedded systems optimization, and heterogeneous computing architectures. He actively participates in conferences like NeurIPS and IEEE SOC, showcasing innovations in reconfigurable computing and energy-efficient systems.

Amor NafkhaView profile

Researcher

• Physical layer security
• Hardware Security
• Reconfigurable Computing
• +4 more

Amor Nafkha is a Researcher at the Rennes Institute of Electronics and Telecommunications (IETR), specializing in Physical layer security, Hardware Security, and Reconfigurable Computing. His work focuses on advancing Cognitive Radio, Software Defined Radio (SDR), and 5G/6G technologies. He has contributed to pioneering research in spectrum sensing, hardware vulnerability analysis, and reconfigurable systems. His research interests include developing robust security mechanisms against side-channel attacks, optimizing SDR architectures for energy efficiency, and applying machine learning techniques to device activity detection in massive MTC networks. Key projects include the ANR-POSEIDON initiative and the WONG5 document on 5G integration for machine-type communication. Notable contributions include multi-screaming-channel attacks leveraging frequency diversity and hybrid spectrum sensing frameworks validated on USRP platforms. His work bridges theoretical signal processing with practical hardware implementations, emphasizing real-world deployment challenges.

Oscar GustafssonView profile

Associate Professor

• Digital Circuit Design
• Field-Programmable Gate Array (FPGA)
• Application-Specific Integrated Circuit (ASIC)
• +7 more

Oscar Gustafsson is an Associate Professor at Linköping University, affiliated with the Department of Electrical Engineering (ISY) and the Division of Electronics and Computer Engineering (ELDA). His research focuses on energy-efficient computations in digital circuits, particularly in ASIC and FPGA architectures, with applications in communication systems and hardware acceleration for AI. The 2024 publications highlight his work on FPGA implementations for dynamic programming in hybrid vehicles, hardware-aware neural network optimizations, FFT architectures for MIMO systems, Python-based finite word-length effect simulations, and CPU-FPGA co-design strategies. These works intersect with Computer Engineering Artificial Intelligence Signal Processing Computer Arithmetic Embedded Systems domains. He leads the Division of Computer Engineering at Linköping University, emphasizing collaborative research including Swedish-Chinese 5G technology partnerships.

• Industrial Automation
• Power Electronics
• Internet of Things (IoT)
• +3 more

Dr. Francisco Zayas Gato is a researcher at the University of A Coruña (UDC), affiliated with the Ferrol Engineering School within the Department of Systems and Automatic Engineering. He actively contributes to the Cybernetic Science and Technology Research Group, focusing on industrial automation, power electronics, and IoT applications. Teaching roles in Industrial Automation, Power Electronics, and Embedded Systems Key research areas: IoT integration, renewable energy systems, control theory Collaborations: European Union, Navantia, EMALCSA, Red.es His research spans diverse projects including solar energy systems, wireless data acquisition, and digital twin implementations. Recent publications demonstrate expertise in industrial IoT applications, control system design, and educational technology solutions. Scientific contributions appear in journals such as Sensors, Applied Intelligence, and Logic Journal of the IGPL. His work frequently involves multidisciplinary collaborations with institutions like Universidad de Castilla-La Mancha and European Commission research programs.

Jonathon HareView profile

Professor

• Representation Learning
• Deep Learning
• Computer Vision
• +6 more

Jonathon Hare is a Professor in the School of Electronics & Computer Science at the University of Southampton. He holds a BEng degree in Aerospace Engineering and PhD in Computer Science, both from the University of Southampton. His research is centered around representation learning with the goal of developing techniques that allow machines to understand and utilize data to fulfill human information needs. His research spans three main areas: Novel representation techniques including differentiable neural architectures for counting and working with unordered sets; Understanding representations through biological inspiration, exploring how neural architectures relate to biological systems; and Applications of representation in geospatial intelligence and document analysis. He has made a strong commitment to open science, with many projects having open-source implementations, including the OpenIMAJ software that won the prestigious 2011 ACM Multimedia Open Source Software Competition. His recent publications (2024-2025) show a strong focus on hardware-efficient neural networks, representation learning, and explainable AI. His work addresses critical challenges in neural network memory configuration, FPGA acceleration, and stable algorithm learning using mathematical constraints. His research shows growing emphasis on practical implementation challenges and the intersection of theoretical neural network concepts with real-world hardware constraints. Vice Chancellors Teaching Award (2015) Faculty award for innovative teaching (2013-14) 2011 ACM Multimedia Open Source Software Competition winner Shortlisted for Blackboard and VLE Awards (2016, 2017, 2020) Fellow of the Higher Education Academy (FHEA) Senior Member of IEEE (SMIEEE) As Doctoral Programme Director, Professor Hare supervises over 150 PhD students across multiple disciplines including Computer Science, Geography, Engineering, and the MINDS interdisciplinary program. He leads undergraduate Computer Vision and postgraduate Differentiable Programming/Deep Learning modules, and has been instrumental in developing new machine learning curriculum including modules on Deep Learning, Natural Language Processing, and Machine Learning Technologies. His teaching excellence has been widely recognized through multiple awards and nominations. He also serves on the editorial board of IET Image Processing journal since 2020. Professor Hare is actively involved in several research groups including Vision, Learning and Control; International Centre for Ecohydraulics Research; Centre for Machine Intelligence; and Southampton Marine and Maritime Institute. His current projects include SemanticNews, JASH – AYURDA (Fish for Life), International Centre for Spatial Computational Learning, and various marine and geospatial research initiatives.

Christian HochbergerView profile

Professor

• Reconfigurable Computing
• FPGA Architecture
• Embedded Systems
• +3 more

Prof. Dr.-Ing. Christian Hochberger is a Professor at Technische Universität Darmstadt, affiliated with the Department of Computer Science. His research focuses on reconfigurable computing, FPGA architecture, embedded systems, and hardware/software co-design. He teaches courses such as 'Rechnersysteme I/II' and 'High-Level Synthese.' University: Technische Universität Darmstadt Department: Department of Computer Science Research interests span FPGA-based acceleration, CAD tools for reconfigurable systems, and energy-efficient computing. His work frequently addresses challenges in hardware design, parallelization, and fault tolerance. Publications from 2022-2024 highlight contributions to memristive devices, genetic circuit design automation, and CGRA optimization. His recent work emphasizes experimental methodologies and novel techniques in resistive switching and memristor-based FPGAs. Awards: None explicitly listed in the provided text.