Find Academic Supervisors

Ali RoulaView profile

Associate Professor

• Signal Processing
• Computer Pattern Recognition
• Biomedical Engineering
• +12 more

Ali Roula is an Associate Professor in Biomedical Engineering at the Faculty of Computing, Engineering and Science, University of South Wales. He previously served as Head of Electrical and Electronic Engineering and is currently seconded as an Innovation Champion. His academic background includes a PhD in Computer Science and an M.Eng. in Electronic Engineering, with professional affiliations in IEEE, IET, and as a Senior Fellow of the Higher Education Academy. His teaching portfolio includes foundational and advanced courses such as C programming, Project Management, Product Innovation (Master's level), and Advanced Digital Signal Processing. His research is centered on signal processing and computer pattern recognition applied to biomedical challenges, with a strong emphasis on innovation and real-world impact. Dr. Roula's recent research has focused on rapid diagnostic platforms, particularly through KESS and Welsh Government grants, including the development of a multi-pathogen point-of-care test and a rapid COVID-19 test with GX Design Group. His publications from 2020 to 2025 reflect a multidisciplinary approach, combining AI, virtual reality, telecommunications, and optical engineering to solve problems in healthcare and diagnostics. He has received several prestigious awards, including: FCES Faculty's Best Supervisor Award (2017) MediWales Start-up of the Year (2020) University Alliance Innovation Award (2021) Innovation in STEM Award (2022) His supervisory and grant leadership includes multiple KESS projects with Cymtec Ltd., Global Laser, and TANGIO Ltd., as well as collaborations with Cardiff University and HPC Cymru. He has contributed to public understanding of science through media engagements with BBC, Channel 4, and Welsh Crucible, discussing neurotechnology and assistive innovations. Dr. Roula leads research in areas such as transcranial stimulation, electromagnetic induction tomography, FPGA-based systems, and AI-driven diagnostics, contributing to UN Sustainable Development Goals in health and innovation.

Thomas EisenbarthView profile

Associate Professor

• Applied Cryptology
• Embedded systems security
• Secure computer architecture
• +7 more

Thomas Eisenbarth is an Affiliate Associate Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI), with a collaborative appointment in the Department of Computer Science. Since August 2017, he has also served as a Professor at the Institute for IT Security, University of Lübeck, Germany. His research spans applied cryptology, embedded systems security, side-channel analysis, and cloud security. Ph.D. in ECE, Ruhr University Bochum, Germany Former researcher at Center for Cryptology and Information Security (CCIS), Florida Atlantic University Member of Horst Görtz Institute for IT Security (HGI) during doctoral studies His research focuses on applied cryptology , secure computer architecture , side-channel and physical attacks , and cloud and virtualization security . He leads research efforts in identifying and mitigating microarchitectural vulnerabilities, particularly in cryptographic implementations. His work is closely tied to the Vernam Group at WPI. The most recent publications highlight trends in cache-based side-channel attacks , particularly in cloud and trusted execution environments (e.g., SGX). His research demonstrates practical attacks on cryptographic implementations, including false dependency exploits and cross-VM leakage, while also proposing countermeasures like CacheShield and MASCAT. The recurring themes include microarchitectural vulnerabilities , hardware-software interaction risks , and privacy threats in virtualized environments . Scientific contributions and recognitions include: Best Paper Award, CHES 2008 Editor, Springer’s Journal of Cryptographic Engineering Guest Editor, IEEE Embedded Systems Letters Special Issue on Embedded System Security General Co-Chair: CARDIS 2020, CARDIS 2021, CHES 2013 TPC Co-chair: CARDIS 2017, SPACE 2017, LightSEC 2014, WESS 2013 PC Member: USENIX Security, IEEE S&P, CRYPTO, EuroCRYPT, and others Thomas Eisenbarth has advised and collaborated with numerous researchers and students, many of whom are co-authors on his publications. His service includes extensive editorial and conference leadership roles. He has not received major grants explicitly mentioned in the text, but his sustained publication record and leadership suggest active funding. He is involved in promoting research exchanges with Europe, particularly Germany, through DAAD, DFG, and Fulbright programs. His research lab, the Vernam Group , focuses on practical side-channel analysis, secure hardware design, and cryptographic implementations. The team works on both offensive techniques (e.g., cache attacks) and defensive mechanisms (e.g., masking, threshold implementations). They utilize FPGA platforms, cloud environments, and embedded systems to evaluate real-world security.

• Internet of Things (IoT)
• Embedded Systems
• Precision Agriculture
• +3 more

Deniz Taşkın is an Associate Professor at the Department of Computer Engineering , Trakya University , Turkey. He has served in various academic and administrative roles since 2002, including Head of Department of Information Technology (since 2024) and Deputy Head of Department (since 2016). Education: PhD in Computer Engineering (2007) Master's in Computer Engineering (2004) BSc in Computer Engineering (2002) Edirne Science High School (1998) Research Interests: His research focuses on Internet of Things (IoT) , Embedded Systems , and Precision Agriculture , with applications in sensor networks, wireless communication, and agricultural monitoring. He explores 3D Modeling techniques for optical systems and contributes to Cybersecurity in multimedia and IoT environments. Publication Trends: Deniz has published 15+ articles on IoT architectures, Bluetooth Low Energy sensor networks, 3D calibration methods, and video encryption. His work bridges Digital Circuit Design with Agricultural Technology , emphasizing energy efficiency and real-time data processing. Scientific Awards: 2017 Trakya Project Market: Electric Motor Driver for Autonomous Vehicles 2016 TÜBİTAK: Electromobile Category Runner-Up and Promotion Award 2015 TÜBİTAK: Solar-Powered Vehicle Design Award 2015 Edirne Governorship: Provincial Achievement Certificate Advising and Grants: He has supervised 5 PhD theses and 7 Master's theses , including projects on IoT in Precision Agriculture and FPGA Circuit Design . As a principal investigator, he led 8 scientific projects funded by Trakya University and KOSGEB, focusing on embedded systems, data security, and smart automation. Labs and Teams: He contributes to IoT Research and Embedded Systems Development at Trakya University, collaborating with multidisciplinary teams in agricultural technology and digital design. His work includes designing self-powered sensor nodes and FPGA-based hardware.

Kofi AppiahView profile

Senior Lecturer

• Edge Computing
• Neuromorphic Hardware
• Embedded Computer Vision
• +6 more

Dr. Kofi Appiah serves as a Senior Lecturer in the Department of Computer Science at the University of York, where he contributes to undergraduate and postgraduate teaching while leading departmental initiatives as Microsoft Certification Coordinator and Undergraduate Admissions Tutor. His academic credentials include: PhD in Computer Science, University of Lincoln, UK MSc in Computer Science, University of Oxford, UK MSc in Electrical & Electronic Engineering (System-on-Chip specialization), Royal Institute of Technology (KTH), Sweden BSc in Computer Science, University of Science and Technology, Ghana Dr. Appiah's research bridges hardware and AI with core interests in Edge Computing, Neuromorphic Hardware, and Embedded Computer Vision. His work extends to Health Informatics, Medical Devices, and Machine Learning applications where he develops efficient systems for real-world deployment. This interdisciplinary approach connects computer vision with healthcare diagnostics, environmental monitoring, and energy-aware computing solutions. Analysis of his recent publications reveals a strong trajectory in applying neuromorphic principles to practical challenges, including wildlife conservation through video analytics, medical image classification for cancer detection, and energy-efficient smart-home systems. His work consistently integrates deep learning with hardware acceleration for edge deployment across diverse domains from power grid security to elderly care. As an academic supervisor, Dr. Appiah has guided research degree students as Director of Studies and Second Supervisor. His departmental leadership includes critical roles in admissions and certification programs. He actively contributes to the Vision, Graphics and Learning (VGL) research group, collaborating on computer vision and machine learning advancements within York's Department of Computer Science.

• Machine learning
• Computer vision
• Pattern recognition
• +7 more

Eric Granger is a Professor in the Department of Systems Engineering at École de technologie supérieure (ETS) in Montreal, Quebec, Canada. He holds a B.Sc.A. from UQAM, and M.Sc.A. and Ph.D. from Polytechnique Montréal. Dr. Granger leads research at the LIVIA – Imaging, Vision and Artificial Intelligence Laboratory and holds the Distech Controls Research Chair on Embedded Neural Networks in a Controller for Connected Buildings. His research spans intelligent and autonomous systems with applications in health technologies. Dr. Granger's expertise includes machine learning, computer vision, pattern recognition, deep models, multimodal information fusion, domain adaptation, weakly supervised learning, and adaptive intelligent systems. His specific applications focus on face analysis and recognition, biometrics, medical imaging, affective computing, computer security, and video surveillance. Analysis of his recent publications reveals a strong focus on person re-identification across different modalities (visible-infrared), weakly supervised object localization, depression detection from facial analysis, and medical image analysis. His work consistently addresses domain adaptation challenges and develops innovative neural network architectures for real-world applications in surveillance and healthcare. Dr. Granger has supervised numerous doctoral students across various research topics including adaptive anomaly detection, signature verification, face recognition, medical image segmentation, and domain adaptation. His research is supported by multiple grants and collaborations with industry partners like Distech Controls. He leads the LIVIA research laboratory which focuses on imaging, vision, and artificial intelligence applications. The lab works on projects involving embedded neural networks for connected buildings, video surveillance systems, medical imaging applications, and affective computing solutions.

• Embedded Systems
• Neural Networks
• Machine Learning
• +3 more

Tamás Majoros is an Assistant Professor at the Faculty of Informatics, University of Debrecen , specializing in Embedded Systems and Neural Networks . His work bridges hardware design and biomedical applications. Primary Affiliation: Faculty of Informatics, University of Debrecen Academic Rank: Assistant Professor Major research areas include: Embedded systems for real-time data processing Neural networks in EEG signal analysis FPGA-based hardware acceleration for medical devices Particle physics collaborations (e.g., sPHENIX) His publications reveal a focus on EEG-based activity recognition , motor imagery classification , and radiation detector stability . Recent work emphasizes edge AI and low-cost neural interfaces . Scientific Awards: None explicitly mentioned. No details provided about advising or grants.

István László OnigaView profile

Associate Professor

• Embedded Systems
• Artificial Neural Networks
• Field-Programmable Gate Arrays (FPGA)
• +6 more

Dr. István László Oniga is an Associate Professor at the University of Debrecen , affiliated with the Faculty of Informatics and the Department of Information Systems and Networks . His research focuses on intelligent embedded systems , neural network implementations , and eHealth/ambient assisted living technologies. Research Areas : Embedded systems design, FPGA-based neural networks, eHealth systems, ambient assisted living, assistive robotics. Students : Levente Philipp, Ferenc Héjja, Laura Juhasz, Peter Polgar, Korteby Mohamed Amine Talbi, Djamila Xie Yu. Email : oniga.istvan@inf.unideb.hu Recent publications highlight his work in AI-powered cyber-physical systems , EEG signal processing , and real-time activity recognition using wearable sensors. His research integrates deep learning , FPGA hardware acceleration , and machine learning frameworks to advance healthcare and robotics applications. University Infrastructure : The Faculty of Informatics at the University of Debrecen emphasizes research in real-time communication, sensor networks, and distributed systems, aligning with Dr. Oniga’s expertise in embedded systems and machine learning.

• Computational Mechanics
• Viscoplasticity
• Neural Networks in Engineering
• +6 more

Associate Professor Dr. Marcus Stoffel is a faculty member at RWTH Aachen University, serving within the Faculty of Mechanical Engineering as head of the Chair and Institute of General Mechanics. His research integrates advanced computational mechanics with cutting-edge machine-learning techniques, focusing on viscoplastic material modeling, neural network surrogates, biomechanical systems, and neuromorphic computing. Education: While explicit degrees are not listed in the text, his title of Dr. and rank of Associate Professor at a leading German technical university imply doctoral and post-doctoral qualifications in mechanical engineering or a closely related discipline. Research Interests: Computational viscoplasticity and damage mechanics Physics-informed neural networks and spiking neural architectures Machine-learning-enhanced finite element methods Crashworthiness and structural optimization Biomechanics of musculoskeletal tissues and medical devices Sustainable neuromorphic computing for engineering applications Publication Trends: Across 2023–2025, Stoffel’s articles reveal a rapid pivot toward AI-accelerated engineering, coupling neuromorphic processors with traditional solid-mechanics problems such as viscoplastic beam response and membrane instabilities. Recurrent themes include surrogate modeling via spiking neural networks, generative learning for design optimization, and data-driven biomechanical analyses of spinal instrumentation and cartilage. Scientific Awards: No specific awards are mentioned in the provided text. Funding & Teams: While individual grants are not detailed, the extensive publication output and involvement in interdisciplinary projects (e.g., neuromorphic computing, biomechanics, crashworthiness) indicate robust external funding. He leads the Institute of General Mechanics, supervising a research group that bridges theoretical mechanics, numerical methods, and experimental biomechanics. Laboratories & Facilities: Stoffel’s team operates within the Institute of General Mechanics at RWTH Aachen, equipped with advanced finite-element computing resources, experimental biomechanical rigs (e.g., spine test rigs and bioreactors), and neuromorphic hardware platforms for sustainable AI research.

Arturo Morgado EstevezView profile

Professor

• Neuromorphic Engineering
• Robotics
• Computer Architecture
• +5 more

Arturo Morgado Estevez is a Professor at the University of Cadiz, working in the Department of Automation, Electronics, Architecture and Computer Networks Engineering. His primary affiliation is with the Engineering school at the University of Cadiz, where he leads research in the TEP940 Applied Robotics research group. His work spans multiple technical domains with a strong focus on neuromorphic engineering and robotics applications. His research interests encompass Neuromorphic Engineering, Robotics, Computer Architecture, Real-Time Computing, FPGA Design, Bio-inspired Computing, Address-Event-Representation Systems, and Embedded Systems. Morgado Estevez specializes in developing spike-based processing systems that mimic biological neural networks, particularly focusing on applications in robotics, computer vision, and sensor systems. His work bridges the gap between biological inspiration and practical engineering implementations, with particular emphasis on real-time performance and hardware efficiency. An analysis of his recent publications reveals a strong trend toward applied robotics and embedded systems, with increasing focus on medical applications, assistive technologies, and energy efficiency. His research has evolved from fundamental neuromorphic architectures to practical implementations in prosthetics, industrial inspection, and environmental monitoring systems. Many of his recent works combine machine learning techniques with specialized hardware implementations for specific application domains. Morgado Estevez has been actively involved in educational initiatives, particularly in computer science education and robotics teaching methodologies. His work includes developing innovative teaching approaches for programming languages and engineering education, with several publications focused on educational technology and pedagogical methods. His laboratory work centers around the TEP940 Applied Robotics research group, where he has developed multiple FPGA-based implementations of neuromorphic systems. His team has created complete spike-based architectures from Dynamic Vision Sensors to robotic motor control, demonstrating practical applications of bio-inspired computing in real-world robotic systems. The research group has made significant contributions to Address-Event-Representation processing and its implementation on parallel computing platforms.

Pascal GiardView profile

Associate Professor

• Wireless Communications
• Signal Processing
• Machine Learning
• +7 more

Pascal Giard is an Associate Professor in the Department of Electrical Engineering at École de technologie supérieure (ÉTS) , part of the Université du Québec network. He holds a Ph.D. in Electrical Engineering from McGill University and has a strong background in wireless communications, signal processing, and embedded systems. Education: Ph.D., Electrical Engineering, McGill University (2016) M.Eng., Electrical Engineering, ÉTS (2009) B.Eng., Electrical Engineering, ÉTS (2006) His research focuses on modern error-correcting codes, particularly polar codes , and spans embedded systems, blockchain, and IoT security. He has developed novel decoding algorithms and architectures for polar codes and chaos-based random number generators for IoT platforms. Recent publications include work on restart mechanisms for polar code decoding , energy-proportional polar decoders , and secure hardware architectures for SECP256K1 . He leads research projects on low-latency communication protocols and hardware security. Scientific Awards: Best Experimental-Demonstration Award at IEEE CASS & ReSMiQ Innovation Day 2015 As a supervisor, he has guided numerous students in thesis projects related to polar codes, blockchain, and wireless communication systems. He also serves as an Associate Editor for IEEE Communications Letters and has reviewed for multiple IEEE journals and conferences.

• Hardware Security
• Secure Processors and Systems Design
• Post-Quantum Cryptography
• +4 more

Michel Kinsy is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence and Director of the Secure, Trusted, and Assured Microelectronics (STAM) Center. His work bridges hardware security, cryptographic systems, and efficient computing architectures. Education: PhD in Computer Science from Massachusetts Institute of Technology His research focuses on hardware security , including secure architectures, trusted execution environments, quantum-proof cryptography, polymorphous architectures, and zero-trust computing systems. Recent projects explore privacy-preserving machine learning, zero-knowledge proofs, and homomorphic encryption acceleration. Key publication trends include: Hardware security for post-quantum cryptography (2020-2025) Secure distributed systems (2021-2025) Privacy-preserving machine learning implementations (2018-2025) Root-of-trust mechanisms in edge devices (2019-2023) Cryptographic protocol acceleration (2020-2024) Scientific Recognition: MIT Presidential Fellow CRA-WP Inaugural Skip Ellis Career Award He teaches graduate-level courses in computer architecture, research methodology, and thesis/dissertation advising, with recent offerings including CSE 792 Research , EEE 599 Thesis , and CEN 799 Dissertation . As STAM Center director, Kinsy leads initiatives in secure microelectronics and collaborates with hardware/software co-design teams. His research website provides detailed project information: https://stamcenter.asu.edu

Arindam MukherjeeView profile

Associate Professor

• Internet of Things (IoT)
• Fog Computing
• Real-Time Systems
• +4 more

Dr. Arindam Mukherjee serves as an Associate Professor in the Department of Electrical and Computer Engineering at the University of North Carolina at Charlotte, College of Engineering. His office is located in EPIC 2336, and he can be reached at amukherj@charlotte.edu or by phone at 704-687-8417. His educational qualifications are as follows: Ph.D. from the University of California at Santa Barbara (2002) M.S. from the University of California at Santa Barbara (2000) B.Tech. from Jadavpur University, India (1996) Dr. Mukherjee's research spans Smart System Architectures, Internet of Things (IoT), and Fog Computing. He investigates cooperative and autonomous mobile systems, real-time system software, and database management for Big Data. His work includes scheduling algorithms for IoT systems and the integration of edge, fog, and cloud computing paradigms for real-time applications in the Big-Squared Data space. His publication record from 2003 to 2018 shows an evolution from VLSI design and bioinformatics to contemporary IoT and fog computing. Early work focused on logic synthesis, biochip testing, and FPGA-based bioinformatics implementations, while recent contributions address power management in heterogeneous processors, energy-efficient communications for smart buildings, and the synergistic integration of edge, fog, and cloud computing for real-time IoT data processing. No scientific awards are mentioned in the provided text. There is no information available regarding students advised or research grants. Similarly, no specific research labs or collaborative teams are described in the source material.

Hamed TabkhiView profile

Associate Professor

• Real-time Edge Intelligence
• Computer Architecture
• Domain-Specific Computing
• +7 more

Hamed Tabkhi serves as Associate Professor in the Department of Electrical and Computer Engineering at the University of North Carolina at Charlotte's William States Lee College of Engineering. He directs the Transformative Computer Systems and Architecture Research (TeCSAR) laboratory, focusing on real-time edge computing solutions for community safety and smart infrastructure. University: University of North Carolina at Charlotte School: William States Lee College of Engineering Department: Electrical and Computer Engineering Office: EPIC 2162 Contact: htabkhiv@uncc.edu | 704-687-0291 His research spans Real-time Edge Intelligence, Domain-Specific Computing, and Cyber-Physical Systems with emphasis on privacy-preserving AI for public safety. Core projects include AI for Highway Work Zone Safety (NSF-funded), Real-time Edge Intelligence for Smart Communities, and Scalable Reconfigurable Architecture for Deep Learning. His work integrates computer architecture innovations with community-driven applications in transportation safety, healthcare diagnostics, and retail security. Analysis of recent publications (2023-2025) reveals strong focus on human-centric video analytics, with 60% of works addressing anomaly detection through pose estimation and transformer architectures. Significant contributions appear in medical imaging (OCT-SelfNet frameworks) and transportation systems (real-time bus prediction, highway safety). His research consistently bridges hardware acceleration with real-world deployment constraints. NSF SaTC: $2M grant for community safety systems NSF CPS: $500K grant for highway work zone safety Collaborations with Leidos, AWS, Xilinx, and community stakeholders As TeCSAR director, he mentors graduate students in hardware-software co-design for edge AI systems, emphasizing FPGA acceleration and real-time processing. His lab maintains active GitHub repositories with open-source tools for edge computing. Current projects address predictive road maintenance, digital twins for power electronics, and AI-driven policing technologies developed through community co-creation processes.

• Telecommunications
• Visible Light Communication
• Signal Processing
• +4 more

Boris Malčić is a Senior Lecturer at the Department of Telecommunications within the Faculty of Electrical Engineering at the University of Banja Luka. His research focuses on telecommunications, visible light communication, signal processing, and wireless network optimization. Key research areas include steganography, 5G waveform analysis, and IoT applications. Recent work examines audio signal quality in optical communication and FPGA-based synchronization for wireless systems. Active in international projects like NewFocus CA19111 COST Action , emphasizing next-gen optical wireless technologies. Publications span IEEE conferences and journals, addressing topics from energy-efficient Wi-Fi protocols to localization methods in wireless sensor networks.

• Machine Learning
• Computational Intelligence
• Data Mining
• +5 more

María Dolores Pérez Godoy is a full-time Professor in the Department of Computer Science at the University of Jaén. She contributes to the Andalusian Interuniversity Institute for Data Science and Computational Intelligence and leads the Intelligent Systems and Data Mining research group. PhD in Computer Science (2010) - University of Jaén Thesis: Hybrid cooperative-competitive evolutionary methods for radial basis function networks Research Focus: Computational Intelligence, Time Series Forecasting, Data Mining, Evolutionary Algorithms, Neural Networks, and Big Data Applications. Her work bridges theoretical advancements in RBFN design with practical implementations in agriculture (olive oil price forecasting) and resource-constrained systems. Recent Article Trends: 2025 work addresses multilabel imbalance with diffusion models. 2024 contributions include tools like Nets4Learning platform, DESReg library, and data governance frameworks. Earlier studies explore transformer models, clustering for crop mapping, and data stream classification. Technical Contributions: Developer of GRNN multi-series forecasting, data stream neural network implementations, and MEFASD-BD multi-objective evolutionary algorithms.