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Aliyu AbubakarView profile

Lecturer

• Deep Learning
• Image Analysis
• Medical Imaging
• +5 more

Dr. Aliyu Abubakar is a Lecturer in Computer Science at Teesside University's Department of Computing & Games. He holds a PhD in Computer Science from the University of Bradford (2023), an M.Sc. in Cyber Security (2015), and a B.Sc. in Computer Science from Gombe State University (2012). Prior to his current role, he was a Postdoctoral Research Associate at the University of Liverpool (2022–2024). His research focuses on deep learning applications in medical imaging, cybersecurity, and pattern recognition. He is a member of the Nigeria Computer Society (NCS) and the Cyber Security Experts Association of Nigeria (CSEAN). Education: B.Sc. Computer Science, Gombe State University (2008–2012) M.Sc. Cyber Security, University of Bradford (2014–2015) Ph.D. Computer Science, University of Bradford (2018–2022) Research Interests: Abubakar’s work spans medical image analysis (e.g., burns assessment, kidney transplantation viability, malaria detection), cybersecurity (deepfake detection, P2P network security), and electrical engineering (partial discharge analysis). His deep learning models address challenges in healthcare diagnostics and industrial safety. Recent studies include human deception detection via facial image comparison and automated liver viability assessment using pre-trained neural networks. Key Trends in Publications: His 2020–2024 articles emphasize cross-disciplinary applications of deep learning, particularly in healthcare and electrical systems. Collaborations span institutions like the University of Liverpool and NHS clinical partners, reflecting his commitment to translational research. Awards & Grants: While no specific awards are listed, his high citation counts (e.g., 32 citations for malaria detection work) highlight impactful contributions. Grants and funding details are not disclosed in the provided texts. Labs & Teams: Active collaborations with multidisciplinary teams in medical imaging and cybersecurity, as evidenced by co-authored papers with clinicians and electrical engineers.

Dawn MacIsaacView profile

Associate Professor

• Biomedical Engineering
• Software Engineering
• Knowledge Engineering
• +1 more

Dr. Dawn MacIsaac is an Associate Professor in the Department of Computer Science at the University of New Brunswick’s Faculty of Computer Science, where she has served for over 14 years. She holds a PhD and Master of Science in Engineering from the same institution. Her research focuses on Biomedical Engineering, Software Engineering, Knowledge Engineering, and Signal Processing, particularly in the context of myoelectric control systems and biomedical signal analysis. Dr. MacIsaac has authored 49 peer-reviewed publications and supervised 25 graduate students. Her work emphasizes improving the performance and robustness of myoelectric prosthetics through advanced machine learning techniques, signal processing algorithms, and adaptive control strategies. Notable contributions include developing the Myosim 2.0 EMG simulation tool and pioneering self-supervised learning approaches for pattern recognition in unclear-label environments. She currently serves on the Editorial Board of the Journal of Electromyography and Kinesiology and is a Professional Engineer (PEng) registered with the Association of Professional Engineers and Geologists of New Brunswick (APEGNB). Her research bridges engineering and healthcare, addressing challenges in signal quality assessment, fatigue monitoring, and human-machine interface design. Key themes in her publications include enhancing EMG signal analysis for medical applications, optimizing control systems for prosthetic devices, and advancing methodologies for automated biosignal evaluation. Her interdisciplinary work impacts rehabilitation technology, clinical diagnostics, and biomedical instrumentation.

Yazhou TuView profile

Assistant Professor

• Cyber-Physical System Security
• Side Channel Analysis
• Sensing and Automation Security
• +3 more

Yazhou Tu is an Assistant Professor in the Department of Computer Science and Software Engineering at Auburn University. He specializes in Cyber-Physical System Security, Side Channel Analysis, and Privacy in embedded systems. His work focuses on addressing vulnerabilities in sensors, actuators, and IoT devices to enhance security and privacy in critical systems. Education: Ph.D. Computer Science, University of Louisiana at Lafayette M.S. Software Engineering, Tsinghua University B.S. Software Engineering, Wuhan University Research Interests: Dr. Tu’s research explores cutting-edge topics such as adversarial control of embedded systems, acoustic and magnetic side-channel attacks, and privacy risks in robotic and biomedical systems. He develops novel defense mechanisms like ADC-Bank and Transduction Shield to counteract physical signal injection attacks. Key Contributions: His work spans sensor security, IoT exploitation, and healthcare technology, with notable studies on 3D printer IP theft, vehicle control via smart glasses, and keystroke tracking via audio. He also investigates physics-informed ML for porous media and glucose monitoring systems. Awards & Grants: No specific awards or grants listed in the provided data, but his prolific publication record highlights sustained research excellence. Labs & Collaborations: Engaged in Auburn’s Center for Artificial Intelligence and Cybersecurity Engineering and other interdisciplinary initiatives focused on secure embedded systems and cyber-physical infrastructure.

Bin HuView profile

Assistant Professor

• Resilient Cyber-Physical Systems (CPS)
• AI-Human Collaboration Safety & Security
• Big Data & Machine Learning-Based Control
• +5 more

Dr. Bin Hu is an Assistant Professor in the Department of Engineering Technology at the University of Houston (2022–present) and previously held the same position at Old Dominion University (2019–2022). He earned a Ph.D. in Electrical Engineering from the University of Notre Dame (2016), an M.S. in Control Science from Zhejiang University (2010), and a B.S. in Electrical Engineering from Hefei University of Technology (2007). His research focuses on resilient Cyber-Physical Systems (CPS), AI-human collaboration safety, machine learning-driven control, and vehicular networks. Key projects include NASA-funded work on adaptive human-autonomy responsibility allocation (2023–2026) and NSF-supported AI-human collaboration in autonomous vehicles (2020–2023). He has secured over $1.2 million in grants, including a $700k NASA grant (pending) and an ONR Rapid Solutions grant ($42k). Dr. Hu’s work bridges control theory, machine learning, and human factors. Recent contributions include safer leader-follower robotic systems, fault-tolerant UAV control, and studies on driver trust in AI-driven vehicles. His research has been recognized through awards like the 2024–2025 APeX Excellence Speaker Series. Teaching experience includes courses on C++ programming and electrical engineering fundamentals at both the University of Houston and Old Dominion University. He advises multiple undergraduate and graduate students in projects ranging from LiDAR systems to cyber-physical security. Key Labs/Teams : NAIL Lab (Networked Autonomous Intelligence & Learning), part of the University of Houston’s Cybersecurity and Robotics initiatives. Grants : NASA TTT Grant ($700k), NSF CHS Grant ($500k), ONR RSLP Project ($42k).

Vinicius Prado da FonsecaView profile

Assistant Professor

• Robotics
• Tactile Sensing
• Robotic Manipulation
• +4 more

Vinicius Prado da Fonseca is an Assistant Professor in the Department of Computer Science at Memorial University of Newfoundland. His research focuses on advanced robotics, tactile sensing technologies, and human-robot interaction. His work integrates machine learning and artificial intelligence to improve robotic manipulation, prosthetic control, and haptic interfaces. Education background includes: Ph.D. in Electrical and Computer Engineering from University of Ottawa (2020) M.Sc. in Systems and Computing from Military Institute of Engineering, Brazil (2013) B.Sc. in Computer Science from Federal University of Tocantins, Brazil (2010) Research interests emphasize tactile perception systems, compliant robotic grippers, and bio-inspired sensor modules. Notable contributions include the BioIn-Tacto tactile sensing framework and studies on myoelectric control for upper-limb prosthetics. His work bridges theoretical machine learning with practical robotics applications in manufacturing, healthcare, and assistive technologies. Publications span over 30 peer-reviewed articles focusing on tactile datasets, sensor design, and robotic control algorithms. Recent trends show increasing emphasis on multimodal data fusion, active learning for sensor optimization, and human-centric robotics applications. Currently no listed scientific awards or grants, but maintains active collaborations in prosthetic development and industrial automation projects.

Jana-Rebecca RehseView profile

Assistant Professor

• Business Process Management
• Process Mining
• User Behavior Mining
• +5 more

Prof. Dr. Jana-Rebecca Rehse serves as Assistant Professor for Management Analytics at the University of Mannheim Business School, where she leads the Chair of Management Analytics within the Information Systems department. Her academic work bridges theoretical research with practical business applications, focusing on data-driven approaches to business process optimization. Her primary research interests encompass User Behavior Mining , Process Mining , and AI applications in business process management . Rehse investigates how organizations can leverage process mining techniques to extract meaningful insights from event logs, with particular attention to conformance checking, process resilience assessment, and the practical implementation challenges businesses face when adopting these technologies. Her work frequently addresses the intersection of human behavior and process execution, examining how user interactions with IT systems can be analyzed to improve process design and user experience. Analysis of her recent publications reveals a clear research trajectory toward increasingly sophisticated integration of artificial intelligence with traditional process mining techniques. Starting with foundational work on reference model mining and process discovery methodology, her research has evolved to address cutting-edge applications of generative AI, explainable AI, and predictive analytics in business process contexts. The majority of her work appears in top-tier information systems and business process management journals including Information Systems, Process Science, and ACM Transactions publications, demonstrating her significant contributions to the field. Professor Rehse actively collaborates with industry partners including Siemens and MEHRWERK, offering thesis opportunities and research projects that address real-world business challenges. Her current call for applications includes work-study programs at Siemens and master thesis topics focused on conformance checking in cooperation with MEHRWERK. She has recently introduced innovative thesis topics exploring the use of Generative AI for Emotion Identification, reflecting her forward-looking research agenda that anticipates emerging technological trends and their business implications.

Mohammad KarimView profile

Professor

• Optical Computing
• Information Processing
• Pattern/Target Recognition
• +5 more

Mohammad Karim, PhD, is a Professor in the Department of Electrical & Computer Engineering at the University of Massachusetts Dartmouth (UMass Dartmouth), affiliated with the College of Engineering. He previously held leadership roles including Provost, Executive Vice Chancellor for Academic Affairs, and Chief Operating Officer at UMass Dartmouth. His career includes serving as Vice President of Research at Old Dominion University (2004–2013), Dean of Engineering at City College of New York (2000–2004), and Head of Electrical and Computer Engineering at the University of Tennessee (1998–2000). He is an elected fellow of multiple prestigious organizations, including IEEE, OSA, and SPIE. Dr. Karim holds a BS Honors in Physics from the University of Dhaka (1976) and MS in Physics (1978) and MS/PhD in Electrical Engineering (1979/1981) from the University of Alabama. His research focuses on optical computing, pattern recognition, electro-optical systems, and sensors, with funding from agencies like the Office of Naval Research and NASA. He has authored 19 books, over 365 papers, and 13 book chapters, and served as guest editor for 36 journal special issues. His awards include Fellowships from IEEE, OSA, and the Bangladesh Academy of Sciences. He mentors students in MS/PhD programs and advises on research proposals. His work spans academic leadership, technical innovation, and interdisciplinary collaboration in engineering and optics.

Janet DongView profile

Professor

• Robotics
• Autonomous Vehicles
• Advanced Manufacturing
• +4 more

Janet Dong is a Professor in Mechanical and Materials Engineering at the University of Cincinnati and Director of the UC Center for Robotics Research. Her work spans robotics, autonomous systems, and advanced manufacturing with applications in healthcare, space exploration, and urban infrastructure. She holds a PhD in Mechanical Engineering from Columbia University. Her research integrates: Autonomous vehicles and mobile robots Dynamic systems control Medical device innovation Industry 4.0/5.0 technologies Robotic exoskeletons and augmentation Her recent publications focus on practical robotics applications including lunar rovers, tick collection robots, medical exoskeletons, and urban maintenance systems. Research trends emphasize autonomous navigation, human-robot collaboration, space exploration technologies, and biomechanical augmentation. She leads multiple funded projects including a $1.29M Department of Energy grant for waste sorting automation and Ohio Bureau of Workers Compensation grants for human-robot safety. Her lab develops robotic solutions for lunar infrastructure, medical devices, and industrial automation.

Fatemehsadat TabeiView profile

Assistant Professor

• Machine Learning
• Smart Sensors
• IoT
• +3 more

Dr. Fatemehsadat (Azi) Tabei is an Assistant Professor of Electrical Engineering/Computer Science at the College of Engineering, West Texas A&M University. She holds a B.S. and M.S. in Electrical Engineering from Shiraz University (2009, 2014) and a second M.S. and Ph.D. from Texas Tech University (2020, 2021). Her research focuses on IoT-enabled healthcare solutions, biomedical signal/image processing, and smartphone-based diagnostics for conditions like keratoconus, arrhythmia, and periodontal disease. She is a co-investigator on two U.S. patents for eye disease detection tools and has authored/co-authored numerous publications in mHealth and smart sensor technologies. Dr. Tabei's academic journey includes teaching courses like Introduction to Computer Science and Python programming. She is an active member of IEEE and IEEE Women in Engineering. Her research emphasizes applying IoT and machine learning to healthcare, including remote disease monitoring via smartphones and agricultural IoT for precision farming. Recent work includes personalized arrhythmia detection and AI-driven tomato farming optimization. Dr. Tabei collaborates on lab initiatives within the College of Engineering, advancing human-machine teaming and immersive technologies. Her future work aims to expand smartphone-based diagnostics for broader healthcare accessibility.

Alejandro Melendez-CalderonView profile

Senior Lecturer

• Neurorehabilitation Engineering
• Biomedical Robotics
• Human Sensorimotor Control
• +3 more

Dr. Alejandro Melendez-Calderon is a Senior Lecturer at the University of Queensland (UQ), holding dual affiliations with the School of Electrical Engineering and Computer Science (primary appointment) and the School of Health and Rehabilitation Sciences. He also serves as a Principal Research Fellow at the Jamieson Trauma Institute, leading the Rehabilitation and Outcomes theme. With over 19 years of interdisciplinary experience in robotics, biomedical engineering, and clinical research, his work focuses on human augmentation technologies, neuromuscular control, and neurorehabilitation engineering. He earned his PhD in Robotics and Biomedical Engineering from Imperial College London (2011) and has held roles at institutions such as Northwestern University, ETH Zurich, and industry leader Hocoma AG. His lab, the NeuroEngineering, Rehabilitation and Medical Robotics Lab at UQ, develops innovative solutions for movement disorders, wearable robotics, and sensorimotor rehabilitation. Research interests include human-robot interaction strategies, computational modeling of motor control deficits, and clinical translation of assistive technologies. His recent work emphasizes adaptive robotic systems for gait training, competition-driven physical effort modulation, and sensor-based assessments for stroke and spinal cord injury populations. Publications highlight contributions to movement smoothness metrics, robotic exoskeleton control algorithms, and interdisciplinary frameworks for sensorimotor evaluation. Collaborations span academic, clinical, and industrial partners to bridge engineering innovation with clinical needs.

Ding ZhouView profile

Assistant Professor

• Industrial Design
• STEM Education
• Human-Computer Interaction
• +3 more

Dr. Ding Zhou is an Assistant Professor at the School of System Design and Intelligent Manufacturing (SDIM) at Southern University of Science and Technology (SUSTech). He holds a PhD in Industrial Design from Queensland University of Technology (QUT), Australia (commenced 2018), and prior academic roles include Associate Professor at Nanjing University of the Arts (2009–2018). His research focuses on cross-disciplinary innovation at the intersection of Industrial Design and STEM Education, emphasizing human-centric technology integration. He has conducted visiting research at the University of Virginia (2012) and Victoria University of Wellington (2014–2015). Education: PhD in Industrial Design, Queensland University of Technology (QUT), Australia (ongoing since 2018) M.A. and B.A. in Industrial Design (exact institutions unspecified) Research Interests: Dr. Zhou’s work bridges design science and education, prioritizing how technology enhances human experiences. Key areas include 3D printing applications in STEM education, exoskeleton robotics, computational design aesthetics, and user-centric design processes. He advocates for pedagogical frameworks that cultivate students’ transdisciplinary problem-solving skills through integrated STEM tasks. Articles Trends (2022–2025): Recent publications highlight advancements in 3D printing for education and manufacturing, human-robot interaction, and smart classroom technologies. Notable themes include scalable computer vision systems for posture detection, topology optimization in additive manufacturing, and BIM-enabled construction processes. Labs & Teams: He leads the Design Science and Educational Innovation Lab (DSEIL), focusing on translating theoretical research into practical educational tools and technologies.

Luca SchenatoView profile

Professor

• Control Systems
• Distributed Optimization
• Federated Learning
• +5 more

Luca Schenato is a Full Professor in the Department of Information Engineering at the University of Padova. His research focuses on distributed control systems, federated learning, multi-agent optimization, and wireless communication protocols. He has extensive experience in developing algorithms for cyber-physical systems, with applications in robotics, smart grids, and sensor networks. Education and Appointments section lists his academic journey but lacks explicit details. He has held positions related to control systems and information engineering throughout his career. Research interests include: Design of resilient wireless control systems Federated learning architectures for edge computing Distributed optimization under communication constraints Robotics and multi-agent coordination Smart energy management systems His recent publications (2021–2025) demonstrate a strong focus on: Over-the-air federated learning innovations High-speed wireless control systems (e.g., 1 kHz Wi-Fi control) Resilient distributed optimization algorithms Human-centric building automation He has contributed to numerous projects related to networked control systems and has organized conferences like ECC13. His work emphasizes bridging theoretical control principles with practical industrial applications.

Micael DerelövView profile

Associate Professor

• Industrial Engineering
• Product Development
• Reliability Engineering
• +5 more

Micael Derelöv is an Associate Professor at Linköping University's Department of Management and Engineering (IEI), specializing in Product Realisation (PROD). His work focuses on optimizing safety, reliability, and efficiency in industrial and aerospace systems. He contributes to sustainable product development through advanced methodologies in design optimization and failure analysis. His research integrates robotics, manufacturing systems, and systems engineering to address challenges in collaborative assembly, aircraft design, and risk management. Dr. Derelöv’s research interests include multi-objective optimization for balancing safety and weight in aircraft systems, industrial safety demonstrators, and reliability-centric design processes. He has developed frameworks for evaluating design concepts and identifying potential failures in early-stage engineering projects. His work also explores the application of genetic algorithms in concept synthesis and the use of qualitative modeling for risk assessment. His publications highlight trends in industrial safety, systems reliability, and aerospace engineering. Recent work includes advancements in safe collaborative robotics on assembly lines and methodologies for industrial safety demonstrators. Earlier contributions address cost optimization in reliability-focused design and bio-mechatronic product development. While no specific scientific awards are listed, his active research and academic role reflect a commitment to advancing engineering practices. He collaborates on student projects, such as a recent initiative designing a pressure-resistant device for space exploration, demonstrating engagement in applied and interdisciplinary research. Micael Derelöv’s affiliation with the Department of Management and Engineering positions him at the intersection of academic research and industrial innovation, particularly within the Product Realisation group. His work emphasizes sustainable, integrated approaches to product development, blending theoretical insights with practical applications in manufacturing and aerospace sectors.

Arthur ter HofstedeView profile

Professor

• Business Process Automation
• Process Mining
• Data Quality
• +3 more

Arthur ter Hofstede is a Professor in the Faculty of Science and Head of the School of Information Systems at Queensland University of Technology (QUT). He previously held a Professorship at Eindhoven University of Technology (2010–2018) and has been a Visiting Professor at Tsinghua University (China), Sapienza University of Rome (Italy), and Sun Yat-sen University (since 2015). His research focuses on Business Process Automation (BPM), Process Mining, and Data Quality, with particular emphasis on the YAWL workflow system. He has supervised numerous PhD students in areas like process-data governance and gamified data cleaning. His work bridges academia and industry, addressing challenges in healthcare, insurance, and logistics. Key contributions include frameworks like ProcessProfiler3D for performance visualization and OrgMining 2.0 for organizational model mining. Awards include prestigious visiting scholar appointments and leadership roles in international collaborations. Education: Ph.D. and M.Sc. from Katholieke Universiteit Nijmegen (Netherlands). Research projects include Australian Competitive Grants on topics like cost-aware BPM and liquid process model collections. Notable publications explore event log imperfection patterns, privacy-preserving process mining, and robotic process automation integration. Grants: DP150103356 (Liquid Process Model Collections), DP120101624 (Cost-aware BPM) Labs/Teams: Centre for Data Science, YAWL Development Team International Collaborations: Sapienza University, Tsinghua University, Sun Yat-sen University

Ana PaivaView profile

Professor

• Social Robotics
• Affective Computing
• Virtual Agents
• +5 more

Ana Paiva is a Full Professor in the Department of Computer Science and Engineering at the University of Lisbon's Instituto Superior Técnico (IST), and coordinator of the GAIPS research group at INESC-ID, now focused on AI for People and Society. She holds a Katherine Hampson Bessell Fellowship at Harvard University's Radcliffe Institute for Advanced Study. Her work centers on creating socially intelligent AI and robots through agent-based approaches, emphasizing emotional and cultural competencies. Key research areas include Social Robotics, Affective Computing, and Pro-social Computing. She leads projects like the EU-funded ANIMATAS ITN network and Portugal's AMIGOS/AGENTS initiatives. Notable contributions include the FAtiMA Toolkit for developing emotional AI agents and pioneering work in hybrid human-machine societies. She has received awards such as the EurAI Fellowship (2019) and the Blue Sky Ideas Award at AAAI’18. Her research explores how machines can foster prosocial behaviors like altruism and cooperation, with applications in education, therapy, and ethical AI design. Her advising includes PhD students like Fernando Santos (Victor Lesser Award winner) and Elmira Yadollahi (ACM award recipient). Current projects focus on inclusive robotics for mixed-ability classrooms and ethical frameworks for human-robot collaboration.