Louis-Philippe Morency is an Associate Professor at Carnegie Mellon University's Language Technology Institute (LTI) within the School of Computer Science. He leads the Multimodal Communication and Machine Learning Laboratory (MultiComp Lab), focusing on computational foundations for analyzing human communicative behaviors during social interactions. His research integrates computer vision, machine learning, and social psychology, with applications in mental health and robotics. Education: Ph.D. in Computer Science from MIT's CSAIL. Previously held research roles at the University of Southern California's Computer Science Department. Research Interests include: Multimodal Machine Learning Artificial Social Intelligence Mental Health Computational Analytics Notable Awards: AI’s 10 to Watch (IEEE Intelligent Systems) NetExplo UNESCO Award 10+ Best Paper Awards at IEEE/ACM Conferences Advising 7 current Ph.D. students across interdisciplinary areas like multimodal learning and healthcare AI. Teaches advanced courses on multimodal machine learning and affective computing. His work has been featured in media outlets including The Economist, Wall Street Journal, and NPR. Labs/Teams: Director of MultiComp Lab, collaborating with interdisciplinary teams on multimodal systems and real-world social behavior analysis.
Peter Svensson is a Professor in electroacoustics at the Department of Electronic Systems, NTNU. His research focuses on audio technology, room acoustics, and acoustic signal processing. He has supervised numerous PhD and master’s students in these fields. Teaching responsibilities include master's courses such as TTT4170: Audio Technology, TTT01: 3D-sound, and PhD courses like TT8302: Room Acoustics. He has co-supervised international theses at institutions like Princeton University and Chalmers University. Research interests encompass digital hearing protection, modal signal processing, and network-based audio transmission. His work bridges theoretical acoustics with practical applications in multimedia systems and architectural acoustics. No scientific awards are explicitly mentioned in the provided materials. Advising highlights include 12+ students across topics like edge diffraction modeling and spherical microphone arrays. Software and measurement datasets are available for academic use.
Dr. Daciana Iliescu is an Associate Professor at the School of Engineering, University of Warwick, and serves as Admissions and Widening Participation Manager. Her research focuses on precision optical measurements, biomedical imaging, and flow visualization, complemented by expertise in opto-electronics and information engineering. She has contributed to interdisciplinary projects involving sensor technology, biomedical informatics, and fuzzy logic-based risk assessment. Her teaching spans LabVIEW/Matlab, multimedia communications, and internet engineering. Key research projects include potato storage disease detection using gas sensors and bone investigation via optical methods. She has secured grants from AHDB, European Commission, and EPSRC for projects like EUPHOROS and BIOD. Her work bridges engineering and applied sciences, emphasizing practical solutions for food security, medical diagnostics, and sensor innovation. Publications span optical engineering, biomedical informatics, and fluid dynamics, showcasing her versatility across disciplines. She oversees admissions strategies to enhance diversity in engineering education.
Dr. Emmanuel Emmanuel is a Lecturer in the School of Engineering, Computing and Mathematics at the University of Plymouth, part of the Faculty of Science and Engineering. His research focuses on the intersection of artificial intelligence, medical imaging, and network engineering. He holds a PhD and has contributed significantly to advancing AI-driven solutions for neurodegenerative diseases (e.g., Alzheimer’s, Parkinson’s), autism diagnosis, and stroke risk prediction. His work also extends to optimizing multimedia service quality in 5G and SDN/NFV networks. Key research interests include machine learning applications in healthcare diagnostics, explainable AI for medical decision-making, and quality-of-experience (QoE) optimization in telecommunications. He has authored over 50 peer-reviewed articles, with recent studies emphasizing AI-based diagnosis tools, biomarker development, and adaptive network management systems. Dr. Emmanuel’s technical contributions span MRI analysis, EEG signal processing, and data-efficient deep learning models. His projects often bridge clinical needs with technical innovation, such as developing XAI frameworks for MRI interpretation and optimizing video streaming quality in heterogeneous networks. He is actively involved in interdisciplinary collaborations, including the LiveWell initiative promoting ICT-based interventions for Parkinson’s patients. Contact: Room 208D Smeaton Building, emmanuel.jammeh@plymouth.ac.uk.
Daniel Schneider is a Research Fellow at Philipps-Universität Marburg, affiliated with the Department of Distributed Systems within the Faculty of Mathematics and Computer Science. He is part of Prof. Bernd Freisleben's research group, where he began his PhD in 2020. Education : Daniel holds a Master of Science (M.Sc.) and is currently pursuing his PhD under Prof. Freisleben's supervision. Research Interests : His work focuses on deep neural networks applied to multimedia data analysis, spanning both wildlife monitoring and music information retrieval . In wildlife research, he develops AI-driven systems for analyzing camera trap images to identify European mammals and birds, addressing ecological data challenges. For musicology, he specializes in automating the transcription of historical organ tablature notation into modern formats, preserving cultural heritage through computational methods. Publications : His recent papers highlight trends in leveraging deep learning for interdisciplinary applications: from real-time species recognition at the edge (Bird@ Edge) to systems like DeepTab for music notation digitization. These contributions bridge computer science innovations with ecological and cultural preservation domains. Advising & Grants : As a PhD student, Daniel is advised by Prof. Freisleben. No grants are explicitly mentioned in the provided texts. Labs & Teams : He is a core member of the Distributed Systems and Intelligent Computing research group at Philipps-Universität Marburg.
Hicham Bellafkir is a Research Fellow at Philipps-Universität Marburg, affiliated with the Department of Mathematics and Computer Science (Fb12) and the Distributed Systems group (AG Freisleben). He initiated his PhD in 2021 under Prof. Bernd Freisleben's supervision, focusing on deep neural networks and the analysis of multimedia data. His work integrates advanced machine learning techniques with applications in biodiversity monitoring, wildlife data analysis, and sensor networks. Current research interests include leveraging neural networks for image and audio recognition in ecological contexts. Contact: Office 04C02 (H|04 Institutsgebäude), reachable at hicham.bellafkir@uni-marburg.de . Research contributions span bioacoustics, camera trap image processing, and disruption-tolerant networking, with publications emphasizing interdisciplinary applications of AI in environmental science. Education: M.Sc. in Computer Science (exact degree not specified) Key Projects: Nature 4.0 sensor systems, QUICL networking protocols, bird species recognition frameworks Labs/Teams: AG Freisleben's Distributed Systems Group
Francisco M. Delicado Martínez is an academic researcher specializing in telecommunications, wireless networks, and IoT applications. His work focuses on Quality of Service (QoS) mechanisms, Software Defined Networks (SDN), and blockchain integration in fog computing environments. He has contributed to optimizing resource allocation in OFDMA and IEEE 802.16 networks, and developed IoT-based systems for environmental monitoring (e.g., glyphosate detection, agrochemical spray drifts, and Aedes aegypti surveillance). His recent research emphasizes low-cost IoT ecosystems and blockchain-driven e-government services for public administration and construction management. Research interests span across wireless communications, network optimization, and distributed computing architectures. His publications address challenges in contention resolution, bandwidth request mechanisms, and machine learning integration for disease vector monitoring. Collaborations include projects on fog computing orchestration, SDN-based network enhancements, and multimedia transmission over TDMA/TDD wireless networks. Key contributions include the S-HIDRA architecture (blockchain & SDN for fog computing), DriftGLY and SpectroGLY IoT systems, and the MosquIoT framework for mosquito population monitoring. His work bridges theoretical network protocols with practical applications in agriculture, public health, and smart infrastructure.
Dr. Zhidong Xiao serves as Principal Academic (Associate Professor) at Bournemouth University's National Centre for Computer Animation within the Faculty of Media and Communication. With over ten years of leadership experience including roles as Programme Leader, Head of Education, and Deputy Head of Department, he drives academic strategy and research innovation in computer animation and digital media. His work bridges technical excellence with creative industry applications through extensive collaborations across the UK and China. Dr. Xiao's educational foundation includes a PhD in Computer Graphics (2010) and postgraduate certificates in Education Practice (2010) and Research Degree Supervision (2011) from Bournemouth University, complemented by a BEng (Hons) in Thermodynamics from Taiyuan University of Technology, China (1994). PhD in Computer Graphics, Bournemouth University (2010) PGCE in Education Practice, Bournemouth University (2010) PGCE in Research Degree Supervision, Bournemouth University (2011) BEng (Hons) in Thermodynamics, Taiyuan University of Technology (1994) His research spans Computer Graphics, Motion Capture, Artificial Intelligence, and Virtual Reality with focus on physics-based simulation, sign language recognition, and motion synthesis. Recent work integrates partial differential equations with machine learning to solve animation challenges in facial realism, deformation simulation, and 3D reconstruction. His interdisciplinary approach connects computer science with creative industries, healthcare applications, and educational technology while advancing core techniques in neural rendering and motion analysis. Analysis of his 15 most recent publications reveals consistent innovation in physics-based animation techniques (40%), motion capture processing (25%), and neural approaches to 3D reconstruction (35%). Key trends include the fusion of analytical physics models with deep learning architectures, development of efficient real-time simulation methods, and expansion into accessibility applications through sign language recognition systems. Scientific recognitions include: Fellow of British Computer Society (2023) Fellow of Higher Education Academy (2011) Best Poster Award at Pacific Graphics 2014 He maintains active peer review roles for EPSRC, ESRC, IEEE Transactions on Multimedia, and ACM SIGGRAPH conferences. Dr. Xiao has supervised seven PhD students to completion while currently guiding Alexandra Sergeeva Alexdottir's research on Phantom Touch phenomena. His grant portfolio demonstrates strong industry-academia collaboration: Principal Investigator Capturing and representing sign language (British Council, 2025) VE Communication Programme (Erasmus+, 2020) Co-Investigator Rehabilitation Enhancement via Motion Capture (BU Fusion Fund, 2013) Cross-Channel Film Lab (Interreg, 2012) Digital Beijing Opera Project (2010) As a core member of Bournemouth's Computer Graphics and Visualisation Research Group and Centre for Digital Entertainment, he leads initiatives in motion capture technology through AccessMocap Studio. His international outreach includes invited lectures across China on computer animation education and visual effects techniques, strengthening global partnerships in creative technology development.
Luís Manuel Pessoa is a Senior Researcher and Coordinator of the Optical and Electronic Technologies area at the Centre of Telecommunications and Multimedia of INESC TEC. He holds a Licenciatura (2006) and PhD (2011) in Electrical and Computer Engineering from the Faculty of Engineering of the University of Porto. Previously, he taught RF/microwave engineering and optical communications as an invited assistant professor at the University of Porto. His research focuses on coherent optical systems, radio-over-fiber, RF/microwave devices/antennas, underwater wireless power/communications, and 6G technologies like THz communications and Reconfigurable Intelligent Surfaces (RIS). He has authored/co-authored >50 publications and 1 European patent, coordinating multiple R&D projects and EU initiatives such as SUPERIOT for sustainable IoT systems. His work emphasizes industrial applications and standardization efforts for future networks. **Education:** Licenciatura in Electrical & Computer Engineering (2006), University of Porto PhD in Electrical & Computer Engineering (2011), University of Porto **Research Interests:** Terahertz (THz) communications and 6G systems RIS hardware design and signal processing RF/microwave antennas and devices IoT sustainability and printed electronics Underwater wireless power transfer **Publications:** Recent work highlights industrial THz use cases, sustainable IoT systems, and RIS innovations for 6G. His articles address technical challenges like spectrum utilization, energy efficiency, and hardware scalability. **Projects & Grants:** Coordinates EU projects like SUPERIOT and TERRAMETA, managing R&D contracts with industry. Leads teams in developing prototypes for RIS, IoT nodes, and anechoic chambers. **Labs/Teams:** Active in INESC TEC’s Telecommunications and Multimedia Centre, fostering collaborations across academia and industry.
Prof. Stefania Cecchi is an Associate Professor at the Department of Information Engineering (DII) within the Faculty of Engineering at the University of Marche (UNIVPM). Her research focuses on audio signal processing, nonlinear systems, and biomedical engineering applications. She holds office hours on Mondays from 9:30–12:30 in DII or via Microsoft Teams. Her work spans innovative audio technologies such as binaural systems, real-time noise cancellation, and spatial audio rendering. She also explores interdisciplinary applications like bee hive monitoring through acoustic analysis and physiological response measurement to sound stimuli. Recent projects include developing low-cost medical devices for stress detection and optimizing automotive audio systems. Key research areas include: Head-Related Transfer Function (HRTF) interpolation for 3D audio Real-time audio processing for driver monitoring and health applications Nonlinear system identification in audio devices Acoustic-based fault detection in drones and vehicles Her publications highlight trends in immersive audio systems, bioacoustic analysis, and adaptive filtering techniques. She actively contributes to advancing audio engineering and its biomedical applications through interdisciplinary collaborations.
Lucia Migliorelli is a Research Fellow at the Polytechnic University of the Marche, Italy. She completed her Master of Science in Biomedical Engineering (cum laude) in 2018 and has been pursuing a PhD since 2018 under Prof. Emanuele Frontoni. Her research focuses on AI-driven monitoring systems for healthcare applications, leveraging multimedia data analysis without wearable sensors. Key areas include human action recognition, medical record analysis, and non-invasive patient monitoring for populations like preterm infants and elderly patients. Education highlights include a Master’s thesis on machine learning for diabetes management and ongoing PhD work on intelligent spaces for automated behavior analysis. Current projects involve federated learning for medical imaging (e.g., MRI-to-CT synthesis) and edge AI for real-time surveillance systems. She is based at the Polytechnic University of the Marche, contributing to interdisciplinary research at the intersection of biomedical engineering and artificial intelligence. Her publications span medical image analysis, AI in digital humanities, and ethical considerations in healthcare AI. She actively develops prototypes for neonatal intensive care units, emergency vehicle detection, and groundwater level prediction. No scientific awards are explicitly listed, though her work reflects strong contributions to AI-driven healthcare innovation. Current advising and grants are not detailed in the text, but her research aligns with lab efforts in environmental AI, rehabilitation robotics, and telemonitoring systems for neurological disorders. She collaborates with multidisciplinary teams to advance ethical, sustainable AI solutions for clinical and societal challenges.
Ifigeneia Mavridou is a Researcher at Bournemouth University's Faculty of Media and Communication , affiliated with the Centre of Digital Entertainment and collaborating with Emteq Ltd. Her work focuses on Virtual Reality and Affective Human-Computer Interaction (HCI) , leveraging physiological signals like EEG, EMG, and PPG for emotion detection. Education : MA in Art, Virtual Reality and Multi-user Systems (University Paris-8 & Athens School of Fine Arts, 2014); PhD in Affective State Recognition in Virtual Reality (Bournemouth University, 2021) Her research explores the intersection of New Media Art and VR to identify emotional features that enhance immersive experiences and VR content re-playability . Current projects involve developing OCOsense™ smart glasses for facial expression recognition and AVEL (Affective Virtual Environment Library) for emotion analysis. Recent publications highlight trends in VR-based emotion detection , wearable sensor integration (optomyography, EMG, PPG), and applications in mental health and clinical research . She is supported by an EPSRC grant for her work on affective VR systems. Collaborative efforts include supervision by Dr. Emili Balaguer-Ballester, Dr. Alain Renaud, Dr. Anna Troisi, Dr. Ellen Seiss, and Dr. Charles Nduka. She has participated in over 20 new media art exhibitions across Europe since 2005.
Maria De Marsico is an academic affiliated with Sapienza University of Rome, Italy, specializing in biometrics, computer vision, and pattern recognition. She actively contributes to conferences such as ICPRAM, serving as editor for multiple proceedings. Her research focuses on gait and facial recognition, wearable sensor applications, and multimodal interaction systems. She collaborates with institutions like SCITEPRESS and Springer, publishing extensively in journals like Pattern Recognition Letters and IEEE Transactions . Her work addresses challenges in biometric systems, emotion recognition, and accessibility through AI-driven solutions. Maria co-organizes workshops on Games-Human Interaction (GHItaly) and contributes to fields like e-learning accessibility for deaf students via SignWriting tools. Her projects include developing applications such as VQAsk for visually impaired users and exploring gait recognition via smartphone accelerometers. She also investigates robust machine learning models for facial expression analysis and dataset rebalancing to reduce bias in demographic classification. Her research spans from theoretical contributions, such as surveys on image integrity methods, to applied systems like the Biopen-Fusing authentication framework. She collaborates with global researchers on topics ranging from microplastics detection to UAV joystick interfaces, demonstrating interdisciplinary impact.
Dr. Naseer Al-Jawad is a Senior Lecturer in Computing at the School of Computing, part of the Faculty of Computing, Law and Psychology at the University of Buckingham. He holds a BSc from Basrah University (Iraq), an MSc from Baghdad University (Iraq), and a PhD in Computer Science from the University of Buckingham (UK). His teaching spans foundational computing courses, including Data Structures, Object-Oriented Programming, and Research Methods for Postgraduates, alongside project supervision at both undergraduate and postgraduate levels. His research interests focus on image and video processing, medical imaging (e.g., breast cancer and melanoma detection), biometric systems (face/gait recognition, Arabic handwriting analysis), and IoT-driven Smart Home systems. He has pioneered work in deep learning for video compression, fusion of audio-visual data for authentication, and steganography techniques. Recent projects include developing adaptive multi-user Smart Home frameworks and enhancing content-based video indexing with wavelet transforms. Dr. Al-Jawad’s publications span topics like facial expression recognition using 3D Kinect data, indoor localization via smartphone networks, and secure real-time video transmission. His work emphasizes practical applications in healthcare, cybersecurity, and disaster management networks. His academic contributions include overseeing student projects and collaborating on interdisciplinary initiatives such as UNILS (indoor localization) and sosMesh (disaster communication frameworks). He actively contributes to conferences like SPIE and MIUA, addressing challenges in multimedia processing and biomedical image analysis.
Dr. Athar Ali is a Senior Lecturer in Computing at the University of Buckingham's School of Computing, part of the Faculty of Computing, Law and Psychology. Prior to this role, he served as an Associate Professor at Aligarh Muslim University, India, and held a Research Associate position at Loughborough University, UK. He holds a PhD in Computer Science from Loughborough University, alongside Bachelor’s and Master’s degrees in Computer Engineering from Aligarh Muslim University. His research focuses on image and video coding , multimedia information security , systems engineering , system-of-systems , and machine learning . Notable contributions include advancements in medical imaging classification, vehicle recognition in video surveillance, and digital watermarking techniques for multimedia content protection. Dr. Ali’s work spans theoretical and applied domains, with publications in conferences like the International Conference on Automation and Computing (ICAC) and journals such as the International Journal of Computer Applications. His recent research emphasizes machine learning applications in healthcare and multimedia security, reflecting his interdisciplinary approach to computing challenges. No scientific awards are explicitly mentioned in his profile. He has advised students in his roles at Aligarh Muslim University, though specific names are not listed. His affiliation with the School of Computing at the University of Buckingham underscores his commitment to advancing computing education and research.
