Find Academic Supervisors

• Embedded Systems
• Artificial Intelligence
• FPGA Optimization
• +2 more

Chao Qian, M.Sc., is a researcher and PhD student at the Department of Embedded Systems, University of Duisburg-Essen, since April 2020. He holds a bachelor's degree in Electrical Engineering from the University of Electronic Science and Technology of China (2015) and a master's degree in Embedded Systems from the University of Duisburg-Essen (2020). His research focuses on enabling artificial intelligence (AI) in reconfigurable hardware like FPGAs for energy-efficient and high-performance embedded systems. He investigates methods to deploy pre-trained neural networks on FPGAs, targeting traditionally resource-constrained embedded systems. Recent publications highlight his work on Configuration-aware FPGA power management Quantized transformers for time-series forecasting Soft sensor design for fluid flow estimation LSTM acceleration on embedded FPGAs In projects like "KI-Sprung: LUTNet" and "Elastic AI", he contributes to adaptive machine learning in pervasive computing. Contact: Email: chao.qian@uni-due.de Phone: +49 203 379 2390 Address: Bismarckstr. 90 (BC), Room BC 104, Duisburg

• Embedded Machine Learning
• Neural Architecture Search
• Signal Processing
• +4 more

Christopher Ringhofer serves as a Researcher and PhD candidate at the Intelligent Embedded Systems department within the Faculty of Engineering and Computer Science at the University of Duisburg-Essen since April 2020. His work focuses on developing energy-efficient AI solutions for embedded platforms with current projects funded by the German Federal Ministry of Education and Research. He earned his BSc in Applied Informatics (2017) and MSc in Distributed Dependable Systems (2020) from the same institution, following three years of industry experience in IoT development at ithinx GmbH. His doctoral research centers on automated neural architecture search for signal processing on constrained devices. Ringhofer's research explores evolutionary algorithms for constructing latency-optimized neural networks targeting microcontrollers and embedded FPGAs, with primary applications in digital audio processing for studio/live environments. His work bridges hardware constraints with deep learning requirements through techniques like precomputed convolutional layers and hardware-aware NAS. He actively contributes to academic instruction through the Bachelor's course 'Embedded Systems' and specialized student projects on 'AI-based Neurosignal Processing', maintaining consistent teaching involvement since Winter Semester 2020/21. Current research projects include 'TransfAIr: Transfer Approaches for Artificial Intelligence in Industry' (since May 2024) and previous work on 'LUTNet' and 'KI-LiveS' initiatives. His technical contributions focus on the IoT Garage infrastructure and Elastic AI ecosystem development for pervasive computing environments.

• Hardware Security
• Internet of Things (IoT)
• Physical Unclonable Functions (PUFs)
• +3 more

Armin Babaei is a Ph.D. researcher at the University of Duisburg-Essen (Uni DUE) within the Department of Computer Science, focusing on hardware security solutions for IoT devices. He holds an M.Sc. in Communications Engineering from RWTH Aachen and previously served as CTO in a startup for two years. Education: M.Sc. in Communications Engineering (RWTH Aachen) since 2015 His research integrates Physical Unclonable Functions (PUFs) with FPGA technology to develop secure, low-power authentication mechanisms for IoT systems. Publications highlight innovations in reconfigurable security architectures and spatial PUF implementations. Current research interests emphasize balancing security requirements with computational constraints in embedded systems. Key themes include: Hardware security for resource-constrained devices PUF-based authentication protocols Energy-efficient cryptographic solutions Contact: ababaei2000@gmail.com

Kenneth B. KentView profile

Professor

• Hardware/Software Co-Design
• Virtual Machines
• Reconfigurable Computing
• +7 more

Professor Kenneth B. Kent is a full-time academic at the Faculty of Computer Science , University of New Brunswick, holding the Barrett Chair in Entrepreneurship for Digital Transformation and directing the IBM Centre for Advanced Studies - Atlantic. His research focuses on Hardware/Software Co-Design , Virtual Machines , Reconfigurable Computing , and Embedded Systems , with collaborations at the Institute for Visual Computing, Hochschule Bonn-Rhein-Sieg. Ph.D. & M.Sc. - University of Victoria B.Sc. - Memorial University of Newfoundland Kent's work bridges FPGA architecture optimization, cloud computing resource management, and Java virtual machine enhancements. He supervises numerous graduate students including Alireza Azadi (PhD) and Scott Young (MCS), while mentoring alumni like Maria Patrou (PhD) and Konstantin Nasartchuk (PhD). His recent publications (2020-2014) span FPGA modeling ( VTR 8.0 ), garbage collection interference, and workflow-aware storage systems. He teaches advanced topics including Virtual Machines and FPGA CAD , while maintaining technical expertise in runtime optimization and reconfigurable architectures. Outside academia, Kent is known for his 1968 Mustang restoration and extensive cycling adventures across the Greek Islands , Newfoundland, and the Canadian Maritimes.

• Geometric Algebra
• Clifford Algebra
• Medical Imaging
• +5 more

Silvia Giuseppina Franchini serves as a Contract Teacher in the Department of Biomedicine, Neuroscience and Advanced Diagnostics at the University of Palermo's School of Medicine and Surgery. Her academic career demonstrates a strong interdisciplinary focus bridging advanced mathematical frameworks with practical medical applications. Dr. Franchini's research interests include: Geometric and Clifford Algebra applications in medical imaging Hardware acceleration for geometric algebra operations Machine learning approaches for medical diagnosis, particularly for Crohn's disease Embedded systems design for real-time image processing Robotics control systems using conformal geometric algebra Medical image analysis and 3D reconstruction Her publication record spanning fifteen years reveals an evolution from foundational hardware implementations to sophisticated medical applications. She has developed specialized architectures including the GAPPCO system, ConformalALU coprocessor, and GAPP compiler, demonstrating innovative approaches to implementing geometric algebra in hardware for medical imaging. Her recent work emphasizes machine learning applications for Crohn's disease classification, showing how mathematical frameworks can translate to clinical diagnostic tools. Dr. Franchini maintains active research engagement with publications continuing through 2022, indicating ongoing contributions to the field of medical imaging technology development. Her work consistently addresses computational challenges while maintaining clinical relevance, particularly in gastroenterology through her Crohn's disease research.

Mukul ShirvaikarView profile

Professor

• Real Time Systems
• Image and Signal Processing
• Deep Learning and Neural Networks
• +5 more

Dr. Mukul Shirvaikar is a Professor of Electrical Engineering and Undergraduate Coordinator at the University of Texas at Tyler, affiliated with the College of Engineering. He holds a Ph.D. from the University of Tennessee (1993), M.Sc. from the University of Maine (198?), and B.Tech. from IIT Banaras Hindu University (India). A Senior IEEE Member, he serves on editorial boards for journals like Journal of Real Time Image Processing and Journal of Medical Imaging and Health Informatics , and participates in ABET accreditation processes. His research focuses on real-time systems, image/signal processing, deep learning, medical imaging, and engineering education. He pioneered the 'Back-To-Basics' student retention program (2005–2008) and developed an electrical engineering freshman experience course. Dr. Shirvaikar has two patents and authored a textbook on real-time systems (2017). Industrial experience includes roles at Texas Instruments (real-time imaging systems) and Pressco Technology (ultra-high-speed inspection systems). Key contributions include FPGA-based biomedical image analysis, embedded AI systems, and curriculum innovations integrating FPGA technology. His recent work emphasizes real-time video analysis for infrastructure monitoring, crop disease detection via smartphone apps, and hardware-aware neural architecture search.

Bassam ShaerView profile

Professor

• Embedded System Design
• VLSI Design and Test
• Digital Signal Processing
• +5 more

Dr. Bassam Shaer is a Professor in the Department of Electrical and Computer Engineering at the University of West Florida (UWF), affiliated with the Hal Marcus College of Science and Engineering. He has over 20 years of experience in academia, with a focus on embedded systems, VLSI design, and distance learning methodologies. Shaer earned his Ph.D. and M.S. in Electrical Engineering from the University of South Florida and a B.S. from the University of Florida. Educational Background: Ph.D. in Electrical Engineering, University of South Florida (Tampa, FL) M.S. in Electrical Engineering, University of South Florida (Tampa, FL) B.S. in Electrical Engineering, University of Florida (Gainesville, FL) Research Interests: Dr. Shaer's work spans embedded system design , VLSI testing and partitioning , analog/digital electronics , and distance-learning laboratory development . His innovations include energy conservation systems, robotic ball collection mechanisms, and intelligent tracking telescopes. He emphasizes practical applications of microcontrollers and FPGA-based solutions. Teaching: Shaer teaches courses such as Electronic Circuits , VLSI , Digital Computer Architecture , and Microprocessor Applications , integrating hands-on lab experiences. He has developed interactive distance-learning systems to enhance accessibility in engineering education. Professional Contributions: He has reviewed over 10 academic journals and contributed to projects like solar array positioning systems and LabVIEW-based plotter controls. His research often bridges theoretical engineering principles with real-world applications in robotics, renewable energy, and automated systems. Labs/Teams: He collaborates with initiatives like the Science Olympiad and Science Showcase , promoting interdisciplinary engineering education and innovation.

Mario BadrView profile

Assistant Professor

• Computer Architecture
• Parallel Computing
• Heterogeneous Computing
• +3 more

Mario Badr is an Assistant Professor (Teaching Stream) in the Department of Computer Science at the University of Toronto, part of the Faculty of Arts and Science. His research focuses on computer architecture, with an emphasis on simulation tools for evaluating multi-core, heterogeneous, and energy-harvesting systems. He has contributed to methodologies for analyzing memory behavior, synchronization performance, and cache coherence in modern architectures. Key research areas include parallel computing, performance analysis of GPU offloading, and energy-efficient computing models like the EH framework. His work often involves developing synthetic traffic models (e.g., SynFull) to assess architectural designs under realistic conditions. These tools enable early-stage exploration of trade-offs in processor architectures, particularly for mobile and embedded systems. Dr. Badr’s publications span topics such as cache behavior modeling, multi-core scalability, and intermittent computing. His research bridges theoretical analysis and practical implementation, with applications in both academic and industry contexts. While no specific awards or grants are listed, his contributions to architecture evaluation frameworks reflect a strong focus on advancing computational efficiency and system design.

• Cyber-physical Systems
• Cyber-security for Smart Grids
• Digital Twin
• +3 more

Dr. Muhammad Musthafa Roomi Sheik Fareed is a Research Fellow with expertise in Cyber-physical Systems and Cyber-security for Smart Grids. His work focuses on Digital Twin technology, Power Electronic Converters, and Power System Automation. He holds a Ph.D. in Electrical and Electronic Engineering from Nanyang Technological University, Singapore. Education Ph.D. in Electrical and Electronic Engineering, Nanyang Technological University, Singapore His research emphasizes enhancing security in smart grid systems through IEC 61850 standards and open-source tools like OpenPLC. Recent studies include analyzing false data injection attacks and developing robust control mechanisms for parallel generators in smart grids. His publications highlight contributions to both theoretical frameworks and practical case studies in industrial cyber-security. No scientific awards or grants are listed. He has not disclosed advising roles for students or affiliations with specific labs/teams in the provided text.

Stefan SchuppView profile

Researcher

• Hybrid Systems
• Formal Verification
• Reachability Analysis
• +5 more

Stefan Schupp is a postdoctoral researcher affiliated with RWTH Aachen University's Theory of Hybrid Systems Group , led by Professor Dr. Erika Ábrahám. He is temporarily working in the AG Sicherheitskritische Systeme group in Münster but plans to return to RWTH's Hybrid Systems Group. His research focuses on reachability analysis for linear hybrid systems, state set representations (via the Hypro project), and contributions to SMT-solving through the SMT-RAT library's ICP module. He is also part of the UnRAVeL research training group, investigating state set representations for hybrid systems analysis. Teaching involvement includes lecturing on Modelling and Analysis of Hybrid Systems (2015–2019) and Data Structures and Algorithms (2014). He has organized practical terms on state set techniques, robotic exploration, solar power plant calibration, and web applications for exercise generation. He co-organized the Schüleruni 2013 and MINT Camp events. His publications emphasize automotive safety verification, hybrid systems competition methodologies, and controller code validation. Current projects include Hypro (C++ library), SMT-RAT (constraint solver), and contributions to formal verification tools.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

Professor Ian Harris is a faculty member in the Department of Computer Science at the University of California, Irvine (UCI), affiliated with the School of Information and Computer Sciences. His research focuses on secure hardware/software systems, natural language processing for security, and AI-driven solutions for cybersecurity challenges. He holds a B.S. (1990), M.S. (1992), and Ph.D. (1997) in Computer Science from the Massachusetts Institute of Technology and UC San Diego, respectively. His work spans AI/ML, natural language understanding, cybersecurity, and privacy. Key projects include developing cyber test ranges for IoT security, detecting social engineering attacks via scam signatures, and creating empathy-driven conversational models for mental health support. Notable achievements include UCI's fourth-place finish in the eCTF competition (2022) and the Embedded Security Team's 11th global ranking (2024). He also contributes to diversity initiatives in tech, emphasizing equitable access to STEM fields. Recent publications highlight advancements in adversarial AI defense (e.g., jailbreak attack detection) and distributed LLM inference systems for mobile devices. His research bridges theoretical computer science with practical applications in embedded systems security and healthcare technology.

• System Security
• Fault Injection
• Fuzzing
• +5 more

Erik van der Kouwe is an Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, holding dual appointments in the Computer Systems department and the Network Institute. His research focuses on system security, fault injection, fuzzing, and embedded systems security. He contributes to both theoretical advancements and practical implementations of security mechanisms. Key research areas include mitigating vulnerabilities through hardware-software co-design, exposing compiler optimization flaws that evade sanitizers, and developing efficient fuzzing frameworks for malware analysis. His work often bridges academic research with real-world applications in embedded systems and operating systems. Recent publications highlight innovations like InvisiGuard for microcontroller security, hwdbg for hardware debugging frameworks, and Enviral for environment-aware malware analysis. His research has been published in top-tier venues such as IEEE Transactions, ACM conferences, and workshops like EuroSEC. Teaching responsibilities include Advanced Operating Systems, Secure Programming, and Software Security courses. Collaborations span international teams working on topics like compiler hardening, embedded system resilience, and vulnerability benchmarking.

• Highly Parallel Architectures
• Power Electronics
• Control Systems
• +5 more

Francesco Gregoretti was a former faculty member at the Polytechnic of Turin , affiliated with the Department of Electronics and Telecommunications (DET) . He contributed to teaching and academic leadership, including roles as Course Owner for Applied Electronics in recent academic years (2019/20, 2020/21) and as a collaborator in Computer Engineering courses. His doctoral program involvement spanned Electronics and Communications Engineering cycles from 2003/2004 (19th cycle) to 2012/2013 (28th cycle) . Research interests focused on highly parallel architectures , DC-DC switch-mode power conversion , and heterogeneous computing platforms . He led research groups like CodeSimulink (automatic synthesis of analog circuits) and projects addressing high-performance software acceleration. Key projects include: ASPIDA (2002–2005) – Asynchronous open-source processor development Sviluppo di un ambiente di coprogettazione hardware/software basato su Simulink (2001–2003) – EU-funded Design of HW and SW Platforms for Sensor Networks (2007–2008) – Commercially funded Publications emphasize power electronics and parallel computing, with recent works (2021, 2020) on real-time monitoring and digital control frameworks. Earlier contributions (1993–1994) explored SIMD array processors for high-performance computing. No scientific awards are listed. However, he co-invented a patent ( Magnetic application in cancer research and therapeutic aid ) with Romano Borchiellini, Maurizio Bressan, Umberto Lucia, Bartolomeo Montrucchio, and Emilio Paolucci, reflecting interdisciplinary engagement. His academic narrative includes decades of project management and teaching, with a focus on merging theoretical research (parallel architectures) with applied engineering (power systems and sensor networks). The listed doctoral cycles and teaching roles highlight sustained contributions to academic training in electronics and telecommunications.

• Computer Science
• Model Checking
• Performance Evaluation
• +4 more

Professor Boudewijn R.H.M. Haverkort is affiliated with the University of Twente's Digital Society Institute and Faculty of Electrical Engineering, Mathematics, and Computer Science. His research focuses on formal methods, performance evaluation, and thermal management in computing systems. He has contributed to advancing model checking techniques, data center workload modeling, and temperature-aware scheduling algorithms. His work intersects computer science, embedded systems, and cyber-physical systems. Research interests include: - Model Checking and Formal Verification - Performance Evaluation of Distributed Systems - Thermal Management in Real-Time Systems - Machine Learning Applications for System Workload Analysis Notable contributions include developing statistical model checking frameworks and temperature prediction methods recognized by the Harvey Rosten Award in 2020. He has supervised 27 research projects, though student names are not listed here. His collaborations span thermal investigations in ICs, cyber-physical system design, and digital communication systems in conflict resolution contexts. Labs/Teams: Active in the Digital Society Institute and MESA+ Institute, collaborating with TechMed Centre on interdisciplinary projects.

Jay P. LimView profile

Lecturer

• Computer Science
• Numerical Analysis
• Floating Point Arithmetic
• +3 more

Jay P. Lim is a Lecturer in the Department of Computer Science at Yale University. His research focuses on numerical methods for high-precision mathematical computations, particularly in the design and implementation of correctly rounded math libraries for various floating-point representations. He specializes in polynomial approximation techniques, error detection in numerical computations, and compiler optimizations for cryptographic libraries. His work emphasizes cross-platform compatibility, algorithm efficiency, and formal verification of mathematical functions. Research areas include floating-point arithmetic standards, posit number systems, and the application of CORDIC algorithms for trigonometric functions. Notable contributions include RLIBM-Prog and RLIBM-ALL frameworks for generating high-performance math libraries, as well as tools for debugging numerical errors in posit-based computations. His publications span topics from 32-bit floating-point optimizations to compiler-level security verifications. Currently affiliated with Yale's Computer Science Department, his office is located at Dunham 427, 10 Hillhouse Avenue, New Haven, CT. No specific lab or team affiliations are explicitly mentioned in the provided materials.