Dewar Finlay is a Professor of Electronic Systems and Head of the School of Engineering at Ulster University . He previously served as Research Director for the School of Engineering and Interim Associate Dean for Research & Impact within the Faculty of Computing, Engineering and the Built Environment. His work bridges healthcare technology and computational engineering. Education: BEng in Electronic Systems, Ulster University PhD in Computing, Ulster University Research Interests: His research focuses on healthcare technology with emphasis on computerised ECG analysis and deep learning applications in cardiology . He explores AI-driven diagnostics , signal processing , and medical device validation through projects like DTNet+ Digital Twin Network and IoT-Driven Cybersecurity Framework for Intrusion Detection in Drones . Scientific Awards: Best Poster (2024) - Calibrated Uncertainty AI in ECG Analysis Early Career Investigators Award (2022) - British Society for Heart Failure Grants & Collaborations: He has secured funding from EU Horizon 2020 , RCUK , DEL , and InvestNI . Current projects include AI-assisted echocardiography for congenital heart defects in Sub-Saharan Africa and federated learning frameworks for cardiac healthcare.
Zhao Zhigang is an Associate Professor at the School of New Materials and New Energy, Shenzhen University of Technology, where he has been employed since May 2017. Previously, he served as a Lecturer at the School of Optoelectronic Engineering, Shenzhen University (2013-2017) and completed postdoctoral research at Shenzhen University (2010-2012) after earning his PhD from Huazhong University of Science and Technology. His academic journey began with undergraduate and master's studies at PLA Ordnance Engineering College (now Army Engineering University). His educational background includes: PhD in Optical Engineering, Huazhong University of Science and Technology (2005-2010) Master's in Optical Engineering, PLA Ordnance Engineering College (2002-2005) Bachelor's in Military Optoelectronic Engineering, PLA Ordnance Engineering College (1995-1999) Zhao's research focuses on hyperspectral imaging systems and machine learning applications for material classification. His work emphasizes embedded image data acquisition and processing using ARM and FPGA platforms, with significant contributions to micro-hyperspectral imaging technology. His research spans three primary areas: hyperspectral image processing on ARM/FPGA systems, machine learning applications in spectral analysis, and embedded AI implementations on FPGA/Zynq platforms. This interdisciplinary work bridges optical engineering, computer vision, and hardware design. Analysis of his recent publications reveals a strong emphasis on hyperspectral data compression techniques , machine learning applications for spectral analysis , and embedded system implementations . His work demonstrates a consistent focus on practical applications of hyperspectral imaging in fields ranging from food quality assessment to battery health monitoring, with increasing incorporation of deep learning techniques in recent years. His scientific recognition includes: Multiple teaching awards at Shenzhen University of Technology (2019-2024) Shenzhen City high-level professional talent designation (2016) Numerous national competition awards as student supervisor (2016-2023) Outstanding Paper Award at Shenzhen Optical Society (2010) Zhao has secured substantial research funding as Principal Investigator, including horizontal projects (2023-2024), Shenzhen Postdoctoral Research Funding (2019-2020), and Shenzhen Basic Research Projects. He has successfully guided students in academic competitions, resulting in five national first prizes. His research group maintains strong industry connections through multiple school-enterprise cooperation projects focused on practical applications of hyperspectral imaging technology. His laboratory work centers on FPGA-based embedded systems for hyperspectral imaging, with recent projects developing micro-hyperspectral spectrometers for UAV platforms, real-time video processing systems, and specialized hardware for spectral data acquisition and compression. These efforts demonstrate a clear trajectory from fundamental optical engineering toward practical applications of machine learning in spectral analysis.
Dr. Ji Sun Shin is a Professor in the Department of Computer and Information Security at Sejong University, where she has been faculty since 2012. Her research bridges theoretical cryptography with practical security applications across multiple domains including IoT, smart devices, and critical infrastructure systems. Education: Ph.D. in Computer Science, University of Maryland at College Park (2009) B.S. in Computer Engineering, Seoul National University (2001) Professor Shin's research focuses on applied cryptography and network security with particular expertise in authentication systems. Her work spans password-based key exchanges , keystroke dynamics authentication , privacy-preserving protocols , and IoT security . She has made significant contributions to provably secure cryptographic protocols including HB/HB+ protocols, forward-secure identity-based signatures, and functional signatures. Her research addresses both theoretical foundations and real-world implementation challenges in cryptographic systems. Analysis of her recent publications reveals a strong trend toward privacy-preserving techniques in distributed systems, with significant work in federated learning security, blockchain applications for IoT, and efficient cryptographic implementations. Her research demonstrates consistent evolution from theoretical cryptography toward practical security solutions for emerging technologies like smart grids, drone systems, and smartphone authentication. Research Leadership: Principal Investigator of the Information Security Lab at Sejong University Active research collaboration across multiple domains including smart cities, healthcare systems, and critical infrastructure security Extensive patent portfolio with numerous domestic and international patents related to location verification, blockchain security, and authentication systems Professor Shin's laboratory focuses on practical security implementations with research areas spanning smartphone security, short-range communication protocols, IoT authentication mechanisms, and smart car security systems. Her team develops fundamental security technologies that address both theoretical security guarantees and real-world usability constraints.
Randy Verdecia-Peña is a researcher specializing in wireless communication and 5G technologies, focusing on millimeter-wave (mmWave) systems, software-defined radio (SDR), and network protocols. His work emphasizes practical experimentation with advanced signal processing techniques, including machine learning for channel estimation and hardware prototyping for integrated access and backhaul (IAB) architectures. Key contributions include phased array-aided 5G prototypes, flexible layer 2 protocols, and cooperative relay node design in both indoor and outdoor environments. Collaborations frequently involve hardware validation and performance analysis across frequencies like 26 GHz and 60 GHz.
Stefano Markidis is a leading researcher in High-Performance Computing (HPC) and quantum computing. His work focuses on developing advanced simulation frameworks, such as the Neko framework for computational fluid dynamics, and optimizing algorithms for heterogeneous architectures. He collaborates extensively with institutions and researchers globally, contributing to fields like plasma physics, quantum systems, and machine learning applications. His research emphasizes scalability, performance optimization, and the integration of cutting-edge technologies like GPU acceleration and quantum computing. Key research interests include extreme-scale simulations, quantum algorithms, and in-situ data analysis techniques. He has published over 200 articles, with recent work addressing challenges in NISQ systems, tensor network simulations, and CUDA-based performance enhancements. His contributions span theoretical and applied domains, bridging computational methods with real-world applications in fusion energy, materials science, and space exploration. Notable collaborations include projects with Philipp Schlatter, Niclas Jansson, and the NISQ application development community. Markidis also explores hybrid frameworks combining classical and quantum computing, aiming to leverage emerging hardware for scientific breakthroughs.
Ada Fort is an Associate Professor in the Department of Information Engineering and Mathematics at the University of Siena, Italy. Her research focuses on advanced sensor systems, including environmental monitoring, biomedical instrumentation, and IoT applications. She holds a Laurea in Electronic Engineering (1989) and a Ph.D. in Nondestructive Testing (1992) from the University of Florence. Key research areas include wearable sensors for air quality monitoring, magnetic detection of contaminants, and QCM-based biosensors. Her work integrates machine learning for signal processing and fault detection in industrial and environmental systems. Notable projects involve self-sufficient IoT nodes powered by solar energy and low-cost sensor networks for agriculture and healthcare. Publications highlight innovations in sensor design, data imputation for environmental monitoring, and entropy-based security systems. Her contributions span interdisciplinary fields, linking engineering principles with applications in healthcare, agriculture, and climate science.
Michail Maniatakos is a Global Network Associate Professor of Electrical and Computer Engineering at NYU Tandon and a Research Associate Professor at NYU Abu Dhabi, serving as Program Head of Computer Engineering. He holds a PhD in Electrical Engineering from Yale University. His primary affiliations include the NYU Center for Cybersecurity (CCS) and directs the Modern Microprocessor Architectures (MoMA) Lab. Education: B.Sc. in Computer Science (University of Piraeus, 2006), M.Sc. in Embedded Systems (University of Piraeus, 2007), M.Sc. in Computer Engineering (Yale, 2008), M.Phil. in Electrical Engineering (Yale, 2009), and Ph.D. in Electrical Engineering (Yale, 2012). Research focuses on encrypted computation, industrial control systems security, and 3D printing security. His work is funded by the U.S. Office of Naval Research, DARPA, and Abu Dhabi's Department of Education and Knowledge. He has authored numerous IEEE/ACM publications, holds patents on privacy-preserving data processing, and serves on conference technical committees. Recent articles explore hardware security, adversarial machine learning, and privacy-preserving computation. His teams have developed secure microprocessor architectures and frameworks for ICS vulnerability analysis. Awards include Senior Member of IEEE. Teaching includes courses like Computer Organization and Architecture and Hardware Security , emphasizing design, security, and ethical implications of emerging technologies. Active in research initiatives such as the NYUAD secure microprocessor project and ICSFuzz framework development. Labs/Groups: MoMA Lab (specializing in microprocessor architectures and security), affiliated with NYU CCS. Collaborates on projects like TREBUCHEt (FHE accelerators) and ICSML (industrial control ML frameworks).
Alexandra Dmitrienko is a researcher at the University of Würzburg's Institute of Computer Science. Her work focuses on cybersecurity, privacy-preserving technologies, and secure machine learning systems. She has collaborated extensively with institutions like TU Darmstadt and the University of California. Her research spans federated learning security, IoT device protection, Tor network analysis, and mobile platform vulnerabilities. Key contributions include defenses against poisoning attacks in federated learning, analysis of contact discovery exploits in messengers, and practical SGX cache attack mitigations. She has authored over 90 publications across top conferences like NDSS, CCS, and USENIX Security, and contributed to open-source tools like DNNShield and ClearMark for model ownership verification.
Sam Amiri is a Lecturer in Microelectronics / Embedded Electronics at Loughborough University. He holds a BSc in Software Engineering (2007, Iran), M.Sc. in Embedded Systems (2010, Masaryk University), and Ph.D. in Electrical/Electronic Engineering (2014, Masaryk University). Prior to his current role, he worked as a Researcher at Queen’s University Belfast (2015-2017) and the University of Bristol (2017-2018). His research focuses on hardware design, embedded systems, signal/image processing, and FPGA-based solutions for real-time systems. Key research areas include intrusion detection systems for vehicular networks using binarized neural networks, RISC-V processor optimization for neural networks, and FPGA-based acceleration of CNN models. His work also explores reliability engineering in wind turbine drivetrains and heterogeneous computing architectures combining FPGAs with CPUs. Publications span topics like embedded cybersecurity, low-power edge computing, and hardware-software co-design for real-time applications. His recent work emphasizes lightweight neural networks on constrained hardware and fault tolerance in aerospace systems. No specific awards or grants are listed in the provided information.
Minseok Kwon is a Professor and Associate Chair in the Department of Computer Science at Rochester Institute of Technology (RIT), part of the Golisano College of Computing and Information Sciences. He holds a Ph.D. and M.S. from Purdue University and a B.S. from Seoul National University. His research focuses on network systems and protocols (including programmable networks, P4 software testing, SDN, and in-network security) as well as machine learning systems (especially workload prediction). He is a member of the RIT Center for Cybersecurity. Professional Contributions Technical committee member for Computer Communications Journal and multiple IEEE conference program committees (e.g., HPCC 2021, ICC 2021-2021). Publicity Chair for ICNP 2008 and NPSec 2005. Research Highlights Recent work emphasizes programmable data planes for green data centers, security in wireless server networks, and efficient packet processing using P4. Notable projects include P4Kube (in-network Kubernetes load balancing) and CuVPP (filter-based routing optimization). His publications span topics from network latency measurement tutorials to cutting-edge SDN implementations. Awards & Recognition Recipient of the Best Paper Award at IEEE Cluster 2020 for CuVPP . Teaching & Advising Current courses include CSCI-261 (Algorithms), CSCI-788 (MS Project), and supervising graduate co-op placements. His advising focuses on advanced network systems and cloud computing.
Jan Huisken is a Humboldt Professor for Multiscale Biology at the Georg-August-Universität Göttingen, affiliated with the Johann Friedrich Blumenbach Institute of Zoology and Anthropology. His research focuses on advanced light sheet microscopy techniques for biomedical and developmental biology applications. Role: Humboldt Professor University: Georg-August-Universität Göttingen Department: Johann Friedrich Blumenbach Institute of Zoology and Anthropology Research interests include light sheet microscopy , biomedical imaging , and developmental biology with a strong emphasis on zebrafish models. He develops tools for tissue clearing , image processing , and 3D microscopy . The 15 most recent publications analyze innovations in light sheet microscopy, tissue clearing protocols, and computational methods for image restoration. These works span fields such as optical imaging , developmental cardiology , computational biology , and biomedical instrumentation . Huisken contributes to open-source microscopy systems like 'Flamingo' and 'BigFUSE,' aiming to democratize access to advanced imaging technologies. His work integrates engineering, computer science, and biology to solve complex imaging challenges.
Dr. Almas Shintemirov is a Research Fellow at Aalto University's Department of Electrical Engineering and Automation, specializing in robotics, control systems, and human-robot interaction. His research focuses on intelligent robotics, with emphasis on Real-time motion prediction for collaborative robots Nonlinear control algorithms for safe human-robot interaction Open-source robotic hardware design Deep learning applications in autonomous systems
Mauro Andreolini is a University Researcher at the Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia. He teaches Operating Systems and Secure Software Development courses within the Computer Science degree program. His research focuses on Cybersecurity , Network Security , Machine Learning in Security , and Cloud Computing . His recent publications analyze Data Privacy through geohashing and clustering, Adversarial Attacks in cybersecurity, and Moving Target Defense architectures. He has also contributed to frameworks for Automated Security Assessments using deductive reasoning and Realistic Botnet Detection benchmarks. Andreolini's work addresses Graph Neural Networks in intrusion detection, n-Gram Analysis for automotive network security, and Side-Channel Vulnerabilities in USB devices. He collaborates with researchers like Artioli, Ferretti, Marchetti, and Colajanni on projects spanning Adversarial Machine Learning , Secure Software Development , and Cloud-Based Monitoring .
Prof. Dr. Raif Bayır is a Turkish academic at Karabük University's College of Engineering, Department of Mechatronics Engineering. With a career spanning 2000-2024, he has held continuous full-time faculty positions from Assistant Professor to Professor. His research focuses on Robotics, Hybrid/Electric Vehicles, and Artificial Intelligence. Doctorate: Gazi University (2005) - Electronics & Computer Education Postgraduate: Gazi University (1998) - Electronics & Computer Education Undergraduate: Gazi University (1995) - Electronics & Computer Education His work integrates Artificial Intelligence techniques into Electric Vehicle systems, Robotics, and Agricultural Engineering applications. Recent publications emphasize Deep Learning for Mask Detection, Real-Time Battery Monitoring, and Autonomous Navigation Systems. Scientific awards include: 2017 METU Line-Following Robot 1st Prize 2016 TÜBİTAK Domestic Product Award 2015 TÜBİTAK Electromobil Best Design Prize He has advised over 20 graduate theses on topics spanning Electric Vehicle Components, Beehive Monitoring Systems, and Intelligent Control Applications. His research teams have developed multiple TÜBİTAK-supported projects including Automotive Test Stands and Battery Management Systems.
Professor Minyue Fu is an Honorary Professor in the School of Engineering at the University of Newcastle, Australia, specializing in Electrical and Computer Engineering. With over 30 years of research experience, he has established himself as a leading expert in control systems and signal processing, having published over 500 research papers with an H-index of 55. His academic journey began with a Bachelor's degree from the University of Science and Technology of China, followed by M.S. and Ph.D. degrees from the University of Wisconsin-Madison. Prof. Fu's research interests span a broad range of topics in control theory and signal processing. His work consistently focuses on fundamental theoretical problems with practical applications in diverse fields including power systems, sensor networks, multi-agent systems, and cyber-physical systems. He has made significant contributions to distributed control algorithms, stochastic systems, quantization effects in control, and networked systems. His recent publications (2021-2024) demonstrate continued productivity and relevance in the field, with research spanning decentralized optimal control, anomaly detection in cyber-physical systems, cart-pole control systems, and mean-field games. These works reflect his ability to bridge theoretical control concepts with practical engineering challenges, particularly in the context of modern networked and distributed systems. Fellow of IEEE (2004) Fellow of IFAC (2022) Fellow of Engineers Australia Fellow of Chinese Association of Automation (2018) Throughout his career, Prof. Fu has held significant editorial positions including Editor of IEEE Transactions on Signal Processing (2010-2014) and Associate Editor for several prestigious journals. His research has been supported by numerous grants, though specific details aren't provided in the current text. His laboratory work has focused on practical implementations of control algorithms in various systems, demonstrating the real-world applicability of his theoretical contributions. Prof. Fu has also supervised numerous students throughout his career, though specific names aren't listed in the available information.
