Emanuele Piuzzi is an Associate Professor in Electrical and Electronic Measurements at the Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome. He holds an M.S. and Ph.D. in Electronic Engineering from Sapienza University, with a thesis on electromagnetic field exposure and a dissertation on human exposure to wireless telecommunication systems. His research focuses on dielectric spectroscopy, TDR applications, impedance pneumography, UWB radar for physiological monitoring, and electromagnetic field safety assessment. He is a reviewer for IEEE Transactions on Instrumentation and Measurement and a member of IEEE and the Italian Electrotechnical Committee. Teaching includes courses on 'Electrical Measurements' for Electronic and Biomedical Engineering students. Research interests span biomedical engineering, telecommunications, and medical imaging, with a strong emphasis on practical applications like non-invasive monitoring systems and material characterization. His work on radar systems for medical diagnostics and sensor networks for structural health monitoring demonstrates interdisciplinary innovation. Over 150 publications highlight contributions to electromagnetics, biomedical devices, and metrology.
Prof. Tao Gu is a Professor at the Department of Computing, Macquarie University, Sydney. He holds affiliations with the Future Communications Research Centre, Hearing Research Centre, and Smart Green Cities Research Centre. His research focuses on IoT, Ubiquitous Computing, Mobile Computing, Embedded AI, Wireless Sensor Networks, and Big Data Analytics. He earned his Ph.D. in Computer Science from the National University of Singapore (2006), M.Sc. in Electrical and Electronic Engineering from Nanyang Technological University, and B.Eng. in Automatic Control from Huazhong University of Science and Technology. Education: PhD (NUS), MSc (NTU), B.Eng (HUST) Affiliations: School of Computing, Macquarie University Research Themes: IoT, Embedded AI, Wireless Communication His research emphasizes innovative sensing and connectivity solutions. Notable projects include LoRa network optimization, mobile deep learning frameworks (MDLdroid), and acoustic-based health monitoring systems. He has received awards such as the IEEE SMARTCOMP 2016 Best Paper Award and the Ten Years CoMoRea Impact Paper Award at PerCom 2013. Current work explores secure device pairing, energy-efficient LoRa protocols, and multi-modal sensing using WiFi/RF signals. His lab develops systems like AudioGuard for intrusion detection and AIMSafe for driver behavior analysis. Ongoing projects address data-driven economies, hearing healthcare analytics, and non-intrusive human activity sensing.
Mei-Hua Lee is an Associate Professor in the Department of Kinesiology at Michigan State University. She holds a Ph.D. from The Pennsylvania State University and leads the Motor Development and Learning Lab (SDLab). Her work focuses on motor development across the lifespan, particularly how infants and young children learn to interact with their environment through reaching and grasping behaviors. She integrates kinematic analysis, biofeedback, and qualitative methods to study motor skill acquisition and its implications for motor learning and rehabilitation theories. Her research explores the transition from spontaneous movements to goal-directed actions in infancy, leveraging advanced technologies like wearable sensors and machine learning for activity classification. She has pioneered body-machine interface systems to enable assistive device control for individuals with severe motor impairments. Lee’s studies also address methodological challenges in motor learning research, including missing data practices and the design of rigorous experimental protocols. Key collaborations involve interdisciplinary teams from engineering, neuroscience, and pediatrics. Her lab actively develops novel tools for early detection of developmental disorders through movement analysis and promotes translational research in neurorehabilitation technologies.
Robert Lagerström is a Professor at KTH Royal Institute of Technology specializing in Industrial Information Systems. His research focuses on developing automated cyber security frameworks and threat modeling methodologies for large-scale IT environments. Key areas include vulnerability analysis of IoT, automotive systems, and critical infrastructure, with a particular emphasis on probabilistic modeling and simulation languages like vehicleLang and powerLang. He collaborates extensively with industry partners across automotive, banking, energy, and cloud sectors to validate practical security solutions. His work emphasizes model-based security analysis that avoids disrupting live systems, enabling safe testing of cyber defenses. Notable contributions include the securiCAD tool for enterprise cyber security management and the PatrIoT framework for agile threat research in IoT ecosystems. Lagerström's research integrates enterprise architecture modeling with security behavior analysis, addressing challenges in digitalization and agile development environments. He has pioneered methods for quantifying attack resilience through probabilistic threat simulations and has published widely on topics such as LPWAN security, SCADA system vulnerabilities, and ethical considerations in offensive cyber training. His work bridges theoretical cyber security concepts with real-world implementation challenges in both traditional IT and emerging technologies like blockchain and microservices architectures.
Chao Li is a Lecturer of Chinese at the Georgia Institute of Technology's School of Modern Languages, part of the Ivan Allen College of Liberal Arts. He has been with Georgia Tech since 2001, focusing on teaching Chinese language at all proficiency levels and co-directing the Chinese Language for Business and Technology (LBAT) Program since 2006. His expertise includes developing online Chinese language courses, creating multimedia content, and compiling grammatical notes for effective language learning materials. Chao Li holds a Master of Arts in International Relations from Beijing Institute of Foreign Affairs and a Bachelor of Arts in Economics and Chinese from Yunnan University, China. He is a key figure in advancing online Chinese language education at Georgia Tech, emphasizing the integration of technology into language pedagogy. His work bridges language instruction with business and technology contexts, reflecting his commitment to practical, industry-relevant language training. His professional contributions include pioneering efforts in online course development, particularly in designing interactive and adaptive learning environments. While no formal awards are listed, his role in shaping Georgia Tech's Chinese language program highlights his significant impact on language education innovation. Chao Li’s teaching philosophy centers on student engagement through culturally immersive and technologically enhanced methodologies.
Tom Luk R Michoel is a Professor at the Computational Biology Unit within the Department of Informatics at the University of Bergen. His research focuses on bioinformatics, computational biology, and machine learning applied to understanding gene regulation and causal relationships in biological systems. He teaches in both the Bachelor and Master programs in Informatics, including courses like BINF301 and MNF130. Research Interests: Michoel’s work explores how genetic variation influences gene expression and disease mechanisms. He develops machine learning algorithms to infer causal gene regulatory networks from large-scale genomic data, emphasizing causal inference over mere correlations. His recent projects include analyzing plasma protein networks linked to cardiovascular disease and applying Bayesian networks to understand gene-disease associations. Publications: His most recent work (2025) focuses on causal protein networks in myocardial infarction risk and network-driven frameworks for coronary artery disease studies. He also contributes to methodological advancements like integrating graph neural networks with metabolic models. Current Activities: Michoel leads the development of tools like Findr.jl for network inference and teaches short courses on causal inference in drug discovery. His lab collaborates on projects involving single-cell analysis, multi-tissue genomics, and systems pharmacology.
Jason Gu is a Professor in the Department of Electrical and Computer Engineering at Dalhousie University, cross-appointed to the School of Biomedical Engineering. His research integrates robotics, control systems, and biomedical engineering to develop innovative solutions for mobile robotics, surgical systems, and rehabilitation technologies. His primary research domains include: Robotics : Mobile robotics, surgical robots, rehabilitation assistive devices, wireless control systems, and multi-sensor data fusion. Biomedical Engineering : Artificial eye implant control, medical robotic devices, and rehabilitation technology design. Control Systems : Real-time intelligent control, nonlinear systems theory, and embedded control applications. Alternative Energy : Development of novel energy technologies and systems. Analysis of his recent publications (2023-2025) reveals a strong convergence of AI with robotics, particularly in vision-language models for human-robot interaction, semantic SLAM for dynamic environments, and medical image processing. His work shows increasing emphasis on lightweight algorithms for UAVs, neural interfaces, and energy-efficient control systems for aerospace applications. His distinguished honors include: IEEE Canada President (2020-2021) and President-elect (2018-2019) Fellow of the Engineering Institute of Canada (FEIC) Fellow of the Canadian Academy of Engineering (FCAE) Professional Engineer (PEng) designation Professor Gu leads a dynamic research laboratory developing advanced robotic platforms including the PA10 Portable General-Purpose Intelligent Arm and B21r Mobile Robotic System. His team actively pursues real-world applications in surgical robotics, terrain perception for legged robots, and alternative energy systems through industry-academic partnerships and competitive research grants.
Celia Lopez Ongil is an Associate Professor in the Department of Electronic Technology at the University Carlos III of Madrid. She also serves as Acting Director of the University Institute of Gender Studies, Deputy Director of the Institute of Gender Studies, and Deputy Vice-Rector for Internationalization. Her research focuses on wearable technologies with a gender perspective, affective computing for social impact, and embedded systems design. Key projects include the EMPATÍA-CM initiative addressing gender-based violence through multimodal affective computing and the WEMAC dataset for emotion recognition. She leads the Microelectronic Design and Applications (DMA) research group and has contributed to interdisciplinary fields like biomedical sensors, radiation-hardened electronics, and hardware-software co-design. Education: Ph.D. in Electronics (university unspecified), with a strong background in both technical and gender studies disciplines. Research interests span wearable electronics, gender-inclusive technology development, and ethical AI applications. Her work integrates hardware innovation with social science to address societal challenges, particularly in health monitoring and violence prevention. Publications emphasize cross-disciplinary methodologies, with over 50 articles in journals like Sensors and IEEE Transactions. Notable contributions include adaptive emotion recognition systems and fault-tolerant embedded architectures. She actively participates in international conferences on technology ethics and gender studies in STEM. Labs/Teams: Microelectronic Design and Applications (DMA) group, Institute of Gender Studies, and collaborations with OPTOS CubeSat space instrumentation projects.
Mohamed Bayoumy is an Assistant Professor at the University of Pittsburgh, specializing in optical fiber sensors, materials science, and their applications in extreme environments like nuclear reactors and energy systems. His work focuses on developing radiation-resistant sensors, machine learning-enhanced monitoring, and distributed fiber sensing technologies. He actively contributes to advancing sensor design for harsh conditions and energy sectors. Research interests include distributed fiber optic sensing, radiation tolerance in materials, machine learning for sensor data analysis, and applications in nuclear engineering, additive manufacturing, and hydraulic fracturing. His projects often integrate optical fibers for real-time monitoring of temperature, strain, and environmental parameters. Presentation topics include survey-based studies on remote education during the pandemic, project-based learning in optics, and sensor behavior prediction using neural networks. A patent on optical fiber-based sensing for electrical cables and radiation detection reflects his innovation in applied sensor technologies. Key collaborations involve institutions like Oak Ridge National Laboratory (Daw, Carpenter), Penn State (Ohodnicki, Buric), and international partners. His work bridges fundamental materials science with applied engineering solutions for energy and infrastructure challenges.
Christos Tachtatzis is a Professor in Applied Artificial Intelligence in the Department of Electronic and Electrical Engineering at the University of Strathclyde. He rejoined the university in 2011, was awarded a Chancellor’s Fellow in 2016, promoted to Senior Lecturer in 2018, Reader in 2021, and Professor in 2024. He leads key strategic initiatives including the Measurement, Digital and Enabling Technologies (MDET) Strategic Theme, co-directs the Laboratory for Innovation in Autism, and serves as Strathclyde lead for the UKRI AI CDT SUSTAIN. He is also a member of the HealthTech Cluster and advises The Data Lab and the Scottish Government on AI applications in agriculture and natural resources. Research Interests: His research spans applied AI with focus on computer vision, multimodal learning, domain adaptation, and explainability. These are applied to sustainable agri-food systems (livestock and arable), digital health, advanced manufacturing, and cybersecurity. His technical expertise includes deep learning, time series analysis, anomaly detection, remote sensing, hyperspectral imaging, and edge/cloud computing analytics. Recent Research Trends: His recent publications reflect a strong trend toward interdisciplinary AI applications, including environmental monitoring via satellite imagery inpainting, urban CO2 emission modeling, synthetic data generation for power grids, infant behavioral analysis, and precision livestock farming using monocular depth estimation. These works highlight his focus on real-world, data-driven solutions across environmental, health, and industrial domains. Scientific Awards: Innovate UK KTP Engineering Excellence Award (2021) Finalist, Herald Higher Education Awards – Outstanding Business Engagement (2022) Strathclyde Team Medal for Innovation in Autism (2018) SIN 2014 Best Paper Award Advising and Grants: He is actively involved in supervising research and leading externally funded projects from UKRI, InnovateUK, and H2020. He is Principal Investigator on multiple grants including Deep Learning for Woodland Soil Biodiversity, FLORA-SAGE (federated learning in agriculture), and the UKRI AI CDT SUSTAIN. He is a co-investigator on projects in digital dairy, infant interaction, and species assessment. He welcomes PhD students and regularly advertises opportunities through SUSTAIN and his professional networks. Labs and Teams: He co-directs the Laboratory for Innovation in Autism and leads the MDET Strategic Theme. He is embedded in the SUSTAIN CDT and collaborates extensively with the HealthTech Cluster, contributing to interdisciplinary research at the intersection of AI, engineering, and societal challenges.
Themos Stafylakis is an Associate Professor at the Department of Informatics of Athens University of Economics and Business (AUEB), a role he assumed in 2023. He also serves as the head of the Machine Learning and Voice Biometrics departments at Omilia (Cyprus and Greece) since 2018. Additionally, he collaborates as a researcher at the Archimedes unit of the "Athena" Research Center. His academic journey includes a PhD in voice applications from the School of Electrical and Computer Engineering of NTUA (2011), a master's from Imperial College London (2005), and a bachelor's from NTUA (2004). Professional experience includes postdoctoral research at ÉTS University of Montreal (2011-2013) and CRIM research center (2011-2016), followed by work at the University of Nottingham on audiovisual speech recognition under a Marie Sklodowska-Curie fellowship (2016-2018). His research focuses on speaker recognition, audiovisual speech processing, and machine learning applications. Key contributions include advancements in speaker verification, diarization, and self-supervised learning techniques. He has contributed to datasets like KAN-AV and participated in challenges such as NIST-SRE and ASVspoof. Educations: Bachelor's: School of Electrical and Computer Engineering, NTUA (2004) Master's: Imperial College London (2005) PhD: NTUA (2011) His research interests span speaker recognition, audiovisual speech analysis, and machine learning innovations. Notable achievements include developing robust speaker verification systems and contributing to spoofing detection methodologies. He has received a Marie Sklodowska-Curie Individual Fellowship (2016-2018). His work bridges academia and industry, with applications in biometric systems, conversational AI, and multimodal data analysis. Collaborations include institutions like CRIM, ÉTS, and the Athena Research Center.
Dr. Rimcy Palakkappilly Alikunju is a Lecturer in Electrical and Electronics Engineering at the School of Computing and Engineering, University of West London. She holds a PhD from University College London and has over a decade of experience in higher education, having taught at institutions in both the UK and India, including Uxbridge College and Toc H Institute of Science and Technology. Her educational background includes: PhD in Engineering, University College London, UK MSc in VLSI & Embedded Systems BSc in Electronics & Communication Engineering Her research focuses on cutting-edge areas in electronics and sensor systems. Key interests include Sensor Technologies , CMOS Image Sensors , Dual-Energy X-ray Imaging , Analog and Digital VLSI , and the Internet of Things (IoT) . These areas reflect her expertise in both hardware design and advanced imaging applications. Although no recent publications are listed in the provided text, her research profile suggests strong alignment with applied electronics, embedded systems, and smart sensing technologies, particularly in industrial and biomedical contexts. She contributes to academic instruction through key programs such as: BEng (Hons) Electrical and Electronic Engineering MSc Electronic and Robotic Engineering MSc Industrial Internet of Things There are no listed scientific awards or fellowships in the provided information. Dr. Alikunju has supervised or advised no publicly listed students in the provided text. She has not been associated with any specific research grants or funding projects in the available description. She is not noted to be part of any named research lab or team, though her work likely aligns with research groups in electronics and IoT within her school.
Pari Delir Haghighi is a Senior Lecturer in the Department of Human Centred Computing at Monash University's Faculty of Information Technology. She holds a PhD in Computing (2010) and a Bachelor of Computer Science (Honours) from Monash University (2004). Her research focuses on ubiquitous computing, context-aware systems, mobile health, and AI-driven decision support. She has pioneered solutions for chronic disease management, emergency response systems, and healthcare data integration. Notable projects include developing the DO4MG ontology for emergency management, AI-powered construction safety systems, and mobile interventions for eating disorders. She has led 13 research projects, including collaborations on digital health, safety engineering, and body image interventions. Her work aligns with UN Sustainable Development Goals related to health and education. Teaching contributions include awards for Honours supervision (2014) and student learning excellence (2020). She teaches courses such as Mobile and Distributed Computing Systems and Systems Analysis and Design. Pari actively organizes conferences like the International Conference on Advances in Mobile Computing and Multimedia. Her lab, the Data Visualisation and Immersive Analytics Research Lab, explores innovative visualisation techniques. Current research emphasizes culturally inclusive digital health interventions, LLM auditing for harmful content detection, and IoT middleware benchmarking. She advises on interdisciplinary projects, fostering collaboration between IT and healthcare sectors.
Pablo Luis López Espí is a Professor at the University of Alcalá, affiliated with the Signal Theory and Communications Department. He leads the Radiation and Sensing Group (RSG) and specializes in electromagnetic field analysis, antenna design, optimization algorithms, and biomedical engineering applications. His research integrates theoretical models with practical systems engineering approaches. He earned his Ph.D. in 2008 with a thesis on optimizing algorithms for water quality indicators, developing associated measurement and communication systems. His academic career includes contributions to wireless power transfer, environmental monitoring, and medical sensing technologies. Key research areas include electromagnetic exposure mapping, miniaturized antennas, and systems engineering for biomedical devices. Notable projects involve smartphone-based EMF assessment and wireless energy harvesting for medical implants. His work bridges telecommunications, environmental science, and healthcare through interdisciplinary approaches. Publications span over 15 years, focusing on antenna optimization, EMF safety, and biomimetic techniques. Awards and grants are not explicitly mentioned in the provided data.
Dr. Teresa Maria Canavarro Menéres Mendes de Almeida is an Assistant Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (University of Lisbon), and a researcher at INESC-ID. She specializes in circuit theory, signal processing, and biosensor technologies. Her teaching focuses on core electrical engineering courses such as Circuit Analysis, Analog/Digital Filters, and Electronics fundamentals, for which she has received multiple teaching excellence awards from the IST Pedagogical Council (2013-2019). Her research interests span resistive circuits, magnetoresistive biosensors, and biochip-based microsystems. She has developed educational materials like problem sets and lecture slides on filters and circuits, alongside applied work in biomedical embedded systems and sensor modeling. Collaborations include projects on portable biosensing platforms and noise analysis in biochip elements. Notable achievements include a series of teaching excellence awards highlighted for courses like 'Circuit Theory and Fundamentals of Electronics' and 'Analog and Digital Filters.' Her work bridges theoretical circuit analysis with practical applications in biomedical engineering and digital signal processing.
