Anne Roudaut is Professor of Human-Computer Interaction and Head of the Bristol Interaction Group within the Computer Science Department at the University of Bristol. Her research bridges HCI, Material Engineering, and Soft Robotics to pioneer shape-changing interfaces that overcome the limitations of static computing devices. Research Focus: Developing shape-changing interfaces that enable dynamic affordances for non-rectangular displays Exploring soft-morphing materials and programmable inks for scalable fabrication Democratizing fabrication through accessible personal manufacturing platforms Applying soft robotics to neurodegenerative disease care and sleep enhancement Her recent publications reveal strong emphasis on sustainable materials (SoftBioMorph), ethical design (Deceptive Pattern Analysis), and therapeutic applications (Equine-Assisted Interventions). Key trends include translational work from material science to practical healthcare solutions, particularly for dementia patients. Academic Leadership: Advises 16 PhD students (including 10 alumni) Leads the Bristol Interaction Group research team Coordinates cross-disciplinary collaborations between HCI and Material Science Technical Contributions: Pioneered frameworks like MorphBenches for mixed reality experimentation, Skin-on Interfaces for bio-driven artificial skin, and DisplayFab for free-form display fabrication. Current work focuses on sustainable biopolymers and ethical design practices in EdTech.
David McDonnell is an Associate Professor at Temple University's Boyer College of Music and Dance, specializing in Music Studies. A composer, computer music programmer, and saxophonist, he bridges spectral composition with rhythms from his 15-year career in Chicago's rock/jazz scene. DMA, University of Cincinnati-College Conservatory of Music MM, DePaul University BM, DePaul University McDonnell's research integrates algorithm-driven computer music with non-Western rhythmic traditions (e.g., African polyrhythms) and live percussion. His work explores ambient soundscapes, electro-acoustic techniques, and cultural contexts of electronic dance/world music. Conference presentations like Trapped by Polygons (2015 SEAMUS) and lecture recitals (e.g., Elements and Strategies in Computer Generated Music ) highlight his focus on blending computational methods with improvisation and multimedia.
Sebastian Thiede is a Full Professor specializing in Manufacturing Systems , with extensive research contributions to Smart Industry , Sustainable Manufacturing , and Human-Centered Production . His work bridges advanced technologies like Machine Learning , Simulation , and Artificial Intelligence with industrial applications, addressing critical challenges in Circular Economy , Energy Efficiency , and Factory Decarbonization . Research focuses on Battery Manufacturing , Edge Computing , and Real-Time Locating Systems (RTLS) . Key methodologies include Digital Twinning , Surrogate Modeling , and Data-Driven Process Optimization . Recent publications highlight trends in Autonomous Sensor Data Analysis (2025), Cyber-Physical Architectures for reconfigurable systems, and Circular Transition Methodologies for manufacturing. His work often integrates Human Factors with Smart Automation , emphasizing environmental and economic impacts.
Dr. Antonio Ribeiro serves as a Bioinformatician at the Centre for Genome-Enabled Biology and Medicine (CGEBM) within the School of Medicine, Medical Sciences and Nutrition at the University of Aberdeen. He joined the institution in August 2015, bringing extensive expertise in DNA sequence alignment, SNP calling, and next-generation sequencing data analysis. His technical proficiency spans Bash scripting, Java, and Perl for developing and optimizing bioinformatics pipelines. Dr. Ribeiro holds an MSc in Computational and Systems Biology from FIOCRUZ-RJ in Brazil, along with specializations in Computer Networks (PUC-Rio) and Information Systems Development (NCE-UFRJ). Prior to his academic career, he accumulated over 20 years of industry experience as a Customer Service Engineer with companies including Nokia-Siemens Networks and Xerox. His research focuses on advanced bioinformatics applications in microbial genomics, molecular epidemiology, and NGS data analysis. Dr. Ribeiro's work bridges computational methods with medical and biological applications, particularly in bacterial pathogen analysis, antimicrobial resistance studies, and genomic variation detection. His technical background enables sophisticated analysis of complex genomic datasets across diverse biological contexts. Analysis of his publication history reveals a strong trajectory in computational genomics with increasing focus on medical applications. His recent work (2023-2025) centers on molecular epidemiology of bacterial bloodstream infections, antimicrobial resistance mechanisms, and clinical applications of genomic analysis. Earlier publications (2012-2016) demonstrate foundational work in NGS data processing, SNP validation, and bioinformatics tool development. Dr. Ribeiro's technical expertise in processing and analyzing NGS data using both third-party open-source tools and custom scripts positions him at the intersection of computational science and biological research. His work supports critical medical research through advanced genomic analysis capabilities, contributing to understanding of bacterial pathogens, antimicrobial resistance, and genomic variation.
Michael Taroudakis is a Professor at the University of Crete, holding a Ph.D. from the National Technical University of Athens (1988). His research focuses on mathematical modeling of physical phenomena with emphasis in wave propagation, particularly as applied to acoustical oceanography and inverse problems. Professor Taroudakis's research interests span several interconnected fields including wave propagation, mathematical modeling, acoustical oceanography, inverse problems, seismic signal analysis, and geoacoustic inversion. His work demonstrates a consistent focus on applying advanced mathematical techniques to solve complex problems in underwater acoustics and oceanographic research. He has made significant contributions to statistical characterization of acoustic and seismic signals, development of inversion techniques, and applications of machine learning in acoustical oceanography. Analysis of his publication record reveals a strong emphasis on statistical methods for signal characterization, particularly using wavelet transforms and hidden Markov models. His research has evolved from fundamental mathematical approaches to more applied problems in ocean monitoring and environmental assessment, with recent work expanding into socioeconomic analysis related to labor markets in Crete. This interdisciplinary approach demonstrates his ability to apply mathematical expertise to diverse real-world problems. Professor Taroudakis has maintained an active research program with publications spanning over three decades, showing continued productivity through 2025. His work has contributed significantly to the field of acoustical oceanography, particularly in the Mediterranean region, and has helped advance methodologies for geoacoustic inversion and ocean acoustic tomography.
Brian Lim Youliang is an Associate Professor at the National University of Singapore (NUS) in the Department of Computer Science . He holds a BSc in Engineering Physics with a minor in Computer Science from Cornell University , an MSc in Human-Computer Interaction from Carnegie Mellon University , and a PhD in Human-Computer Interaction from Carnegie Mellon University . His research focuses on Explainable AI (XAI) , Human-Computer Interaction (HCI) , and Ubiquitous Computing , with applications in healthcare analytics , wellness, and urban sustainability . He leads the NUS Ubicomp Lab , which develops AI-driven technologies for user-centric and trustworthy systems. From analyzing his publications, the research trends show expertise in machine learning interpretability , cognitive load optimization , crowd ideation algorithms , and context-aware systems . His work bridges cognitive psychology with technical AI to create more human-relatable explanations for machine learning models. Scientific achievements include: CHI'22 Best Paper Award (Top 1%) IMWUT Distinguished Paper Award (Top 6/166 in 2019) MOE Outstanding Mentor Award (2016) CHI'09 Best Long Paper Nomination (Top 5%) He teaches courses in Machine Learning (CS3244) and Human-Computer Interaction Theories (CS4249) . The lab continues advancing human-AI collaboration through explainability frameworks and cognitive psychology integration.
Andrii Matviienko is an Assistant Professor (tenure track) in Computer Science specializing in Human-Computer Interaction at KTH Royal Institute of Technology, Sweden. He works at the Department of Media Technology and Interaction Design (MID) within the School of Electrical Engineering and Computer Science. His research focuses on Extended Reality (XR) and interaction with and within immersive spaces, where he leads the Immersive Technologies Lab. Dr. Matviienko received his Ph.D. in Computer Science from the University of Oldenburg while working at the Media Informatics and Multimedia Systems group with Susanne Boll. His academic journey includes: Postdoctoral researcher at the Telecooperation Lab, Technical University of Darmstadt, Germany Research visit at the Multimodal Interaction Group, University of Glasgow (UK), working with Stephen Brewster Work at the Exertion Games Lab led by Florian 'Floyd' Mueller at Monash University (Australia) His research focuses on Extended Reality (XR) and interaction with and within immersive spaces. He leads the Immersive Technologies Lab, where his team explores ways of improving users' XR experiences through novel input techniques, haptic feedback, locomotion methods, taste interfaces, simulations, and exertion games. His work particularly emphasizes cycling interfaces and safety systems for cyclists, as well as child-computer interaction and tangible interfaces. He has made significant contributions to understanding how people interact with technology in physical movement contexts, especially while cycling. Analysis of Dr. Matviienko's recent publications (2023-2025) reveals a strong focus on immersive technologies with several key trends emerging. His work spans virtual reality, augmented reality, and mixed reality applications across diverse domains including cycling safety, medical training, social interaction, and multisensory experiences. A significant portion of his research investigates novel input methods and sensory feedback in XR environments, with particular attention to how physical movement and embodiment affect user experience. His publications demonstrate growing interest in AI integration with immersive technologies, as seen in projects involving generative AI for museum experiences and human-AI interaction for dementia care. Dr. Matviienko actively mentors students and collaborates with researchers worldwide. He encourages students interested in thesis work to reach out to him and his team. His research is supported by various grants that enable: Development of VR bicycle simulators and cycling safety systems Exploration of haptic feedback and novel input techniques in XR Investigation of multisensory experiences, including taste modulation Creation of medical simulation tools for surgical training Dr. Matviienko leads the Immersive Technologies Lab at KTH, where his team explores ways of improving users' XR experiences via novel input techniques, haptic feedback, locomotion, taste, simulations, and exertion games. The lab collaborates with international partners including researchers from Monash University, Technical University of Darmstadt, and University of Glasgow. Current projects focus on cycling interfaces, social navigation in VR, medical simulations, and multisensory experiences that integrate audio, visual, and taste stimuli.
Chris Baber is Professor of Pervasive and Ubiquitous Computing in the Department of Computer Science at the University of Birmingham, where he also serves as Deputy Director of Research and Knowledge Transfer at the Centre for National Training and Research Excellence in Understanding Behaviour. His work bridges human-computer interaction, sensor technology, and AI systems. Research Interests: Baber's research explores human-centered technology design, with emphases on wearable computing, distributed cognition, and AI applications. Key domains include sensor-based interaction, ambient displays, serious games for training, mobile health innovations, and augmented reality systems. His lab focuses on enhancing human-AI collaboration through ergonomic and cognitive frameworks. Publication Trends: Recent articles (2024-2025) demonstrate a strong focus on human-AI teaming, sensor data uncertainty, and communication analysis. Methodologies combine machine learning (e.g., Bayesian networks, eye-tracking) with human factors principles to address real-world challenges in UAV operations, healthcare, and collaborative work environments. Grants & Projects: Actively leads multidisciplinary projects including: Human Machine Teaming (Alan Turing Institute, 2023-2026) Satisficing Trust in Human Robot Teams (EPSRC, 2023-2026) BuildAir: Holistic frameworks for safe built environments (UKRI, 2024-2025) He supervises PhD candidates in areas spanning sensor interactions, location-based systems, and AI-driven narrative design.
Dr. Archie Singh Khuman serves as Associate Head of Education and Senior Lecturer at De Montfort University's School of Computer Science and Informatics. An award-winning academic and proud DMU alumnus (BSc 2009, MSc 2011, PhD 2017), he specializes in Artificial Intelligence, Computational Intelligence, and Uncertainty Modelling with extensive leadership in curriculum development and Universal Design for Learning. His educational background demonstrates deep institutional commitment: PhD in Computer Science (2017, De Montfort University) MSc in Intelligent Systems (2011, De Montfort University) BSc (Hons) in Computer Science (2009, De Montfort University) Dr. Khuman's research pioneers hybrid uncertainty frameworks, notably the R-fuzzy grey analysis framework (RfGAf), bridging Grey Theory, Fuzzy Logic, and Rough Sets. His work focuses on quantifying perception-based uncertainty for applications in healthcare diagnostics, robotic swarm intelligence, and risk assessment systems, emphasizing real-world implementation through industry partnerships. His publication trajectory reveals increasing interdisciplinary convergence, particularly in healthcare AI applications since 2021. Recent outputs integrate Grey-Fuzzy methodologies for medical diagnostics (diabetes, heart disease), sentiment analysis of pandemic communications, and adaptive systems in automotive/industrial contexts, demonstrating strong translation from theoretical foundations to societal impact. Major recognitions include: Five consecutive Students' Choice Teaching Awards (2018-2024) De Montfort Teacher Fellowship (2022) Senior Fellow of the Higher Education Academy (2022) Leverhulme Trust-funded international Grey Systems network (£124,997) Best Paper Award at Grey Systems and Uncertainty Analysis (2016) As academic lead for the Data Analytics Project, he mentored students in developing machine learning solutions that generated £2.5m savings for retail partners. His grant portfolio includes Royal Society funding for IoT-based scheduling (£12,000) and leadership of the Race Equality Network. Current initiatives focus on transnational education partnerships and SI-PASS peer learning programs. Dr. Khuman co-leads DMU's Institute of Artificial Intelligence research group and serves as School Governor for Inglehurst Junior School, maintaining active collaborations with institutions in China, Canada, Romania, and Spain through the International Network on Grey Systems.
Giovanni Finocchio is an Associate Professor of Electrical Engineering at the Department of Mathematics and Computer Science, Physical Sciences, and Earth Sciences at the University of Messina. He maintains significant research affiliations as an associate researcher at the Istituto Nazionale di Geofisica e Vulcanologia (Roma2 section) since 2014 and serves as co-director of the Joint Laboratory UNIME-SINANO of PETAscale computing and SPINtronics (PETASPIN) since 2018. His international experience includes visiting scholar positions at Northwestern University's Department of Electrical and Computer Engineering in 2019 and 2022. Finocchio earned his PhD (XVIII cycle) in "Advanced Technologies for Optoelectronics, Photonics, and Electromagnetic Modeling." His academic journey has positioned him at the forefront of spintronics research, with expertise spanning nanomagnetism, unconventional computing, and micromagnetic modeling. His research interests focus on cutting-edge areas of spintronics, including antiferromagnetic spintronic devices, skyrmion-based memory systems, magnetic solitons, and unconventional computing approaches. His work bridges fundamental physics with practical applications in next-generation computing and memory technologies. He has made significant contributions to understanding magnetic vortices, skyrmion dynamics, and the development of energy-efficient spintronic devices for both conventional and emerging computing paradigms. Analysis of his recent publications reveals a strong focus on advancing spintronic memory technologies, particularly through the investigation of skyrmions and magnetic solitons for high-density storage applications. His research also explores novel computing architectures based on Ising machines and spintronic oscillators, demonstrating a strategic shift toward energy-efficient unconventional computing paradigms. The interdisciplinary nature of his work connects materials science, electrical engineering, and computational physics to address fundamental challenges in next-generation information technologies. American Physical Society (APS) Outstanding Referee (2020) Outstanding Reviewer Awards 2016 (New Journal of Physics) "Research Highlights" from Applied Physics Letters (American Institute of Physics) (2013) Finocchio serves as Principal Investigator for significant projects including the EU-funded "Low Power Spintronics Wireless Autonomous Node (SWAN)" (SWAN-on-chip code 101070287) and the MUR-funded national project "The Italian factory of micromagnetic modeling and spintronics" (PRIN 2020LWPKH7). He has co-authored over 180 publications in prestigious journals including IEEE, Nature group, APS, IOP, AIP, and Wiley publications, and holds 5 patents while having co-founded two spin-off companies. He directs the SpintronicFactory at the University of Messina and serves as president of the petaspin.com association. As one of the leading coordinators of Transverse Theme 2: Design and Modelling, he plays a pivotal role in shaping research directions in spintronics and magnetic computing at both institutional and national levels.
Dimitrios Tzionas is an Assistant Professor for 3D Computer Vision at the University of Amsterdam (UvA), leading research at the intersection of Computer Vision, Computer Graphics, and Machine Learning. His work focuses on reconstructing and modeling 3D human-body interactions with objects and scenes, supporting applications in Robotics, Mixed Reality, and the Metaverse. Academic Affiliation: University of Amsterdam (since 2022), Max Planck Institute for Intelligent Systems (2016-2022) Education: PhD in Computer Science (University of Bonn, 2017), MSc in Electrical & Computer Engineering (Aristotle University, Thessaloniki) His research emphasizes whole-body 3D interaction , including pose estimation, grasp synthesis, and contact-aware modeling. He develops methods like InteractVLM for semantic contact estimation and PICO for dense 3D contact datasets, leveraging vision-language models and optimization frameworks. Recent publications at CVPR, ICCV, and SIGGRAPH Asia highlight advancements in 3D reconstruction, avatar synthesis (PuzzleAvatar), and interactive dynamics (InterDyn). His work is supported by an ERC Starting Grant (2024-2030) and industry partnerships (Google, NVIDIA). Scientific Awards ERC Starting Grant (2024) ELLIS Scholar (2025) Outstanding Reviewer at CVPR, ICCV, ECCV He advises multiple PhD students, including Alvaro Budria and Lixin Xue , and collaborates with institutions like ETH Zurich and MPI-IS. His team's tools (e.g., ICON, GRAB) are widely adopted in academic and industrial research.
Plinio Morita is an Associate Professor at the University of Waterloo's School of Public Health Sciences, with cross-appointments in Systems Design Engineering (University of Waterloo) and the Institute of Health Policy, Management and Evaluation (University of Toronto). He holds positions as a Research Scientist at the Centre for Global eHealth Innovation and Techna Institute (University Health Network). His research focuses on leveraging IoT, mHealth, and AI-driven technologies to improve healthcare delivery through remote patient monitoring, telehealth, and aging-in-place solutions. Morita directs the Ubiquitous Health Technology Lab (UbiLab), which develops systems that integrate data from wearable and ambient sensors to enhance population health surveillance and precision medicine. He emphasizes ethical design and equitability in technology deployment for vulnerable populations. Education: BEng in Electrical Engineering, University of Campinas, Brazil MSc in Biomedical Engineering, University of Campinas, Brazil PhD in Systems Design Engineering, University of Waterloo Postdoctoral Fellowship in Healthcare Human Factors, Techna Institute Research Interests: Population-level health surveillance via IoT Non-invasive monitoring systems (e.g., radar-based gait analysis) AI applications in infodemic management and clinical inquiry Ethics of smart health technologies and data governance His work bridges engineering, public health, and human factors to create scalable, user-centered solutions for chronic disease management and aging care. Professional Contributions: Editor for Journal of Medical Internet Research (JMIH and JMIR mHealth/uHealth) Member of IEC/SCC SyC Active Assisted Living Standards Council Professional Engineer (PEO Ontario) Lab & Teams: The Ubiquitous Health Technology (UbiLab) collaborates across disciplines to advance smart health ecosystems, with recent projects focusing on radar-based in-home monitoring and misinformation detection systems.
Dr. Abdollah Malekjafarian is an Assistant Professor and Ad Astra Fellow in the School of Civil Engineering at University College Dublin (UCD). He leads the Structural Dynamics and Assessment Laboratory (SDA-Lab) and serves as Principal Investigator for the Di-Rail project (funded by Science Foundation Ireland) and Coordinator of the WindLEDeRR project (funded by Sustainable Energy Authority of Ireland). His research focuses on Structural Dynamics, Random Vibrations, Transport Infrastructure, and Offshore Wind Turbines. He holds a PhD in Civil Engineering from UCD (2016) and has held roles including Postdoctoral Research Fellow and SFI Research Fellow. Awards include the KB Broberg Medal (2020) and Royal Irish Academy Charlemont Award (2018). He is an editorial board member of the Journal of Shock and Vibration and Journal of Vibroengineering. Research interests emphasize indirect monitoring of infrastructure (e.g., bridges, railways) using mobile sensors and vibration-based techniques. His work integrates machine learning and data-driven methods for structural health monitoring. Grants include projects on offshore wind turbine dynamics, railway inspection, and smart infrastructure systems. Teaching includes modules like Creativity in Design, Mechanics of Solids, and Structural Research Projects. Professional activities include organizing conferences and reviewing for journals such as Engineering Structures and Journal of Sound and Vibration. Key achievements include developing frameworks for drive-by bridge monitoring, scour detection in offshore wind foundations, and vibration control systems. His lab, SDA-Lab, focuses on advancing dynamic assessment methodologies for civil infrastructure resilience. Current projects address floating modular energy islands and lifetime management of wind energy systems.
Andrew T. Duchowski is a Professor and Chair of the Visual Computing Division at Clemson University's School of Computing (part of the College of Engineering and Science). He holds a Ph.D. in Computer Science from Texas A&M University (1997) and a B.Sc. in Computer Science from Simon Fraser University (1990). His academic roles include service as Focus Area Chair at SIGGRAPH 2019 and Co-organizer of multiple workshops such as MobileHCI 2018 and ETRA 2018. His research focuses on eye tracking, visual perception, human-computer interaction, and computer graphics. Key areas include gaze analytics, virtual environments, and applications in healthcare, education, and cultural heritage. He has pioneered methodologies like the 'Gaze Analytics Pipeline' and authored Eye Tracking Methodology: Theory & Practice (Springer, 3rd ed., 2017). R&D interests span cognitive load measurement via pupil dynamics, ambient/focal attention modeling, and gaze-based interaction design. His work bridges computer science with fields like spatial cognition and neuroergonomics. Recent projects include studies on attention in VR, neurocognitive biomarkers, and sustainable cultural landscape management. Awarded multiple honors including the NSF CAREER Award (2000-2003) and Clemson's Faculty Excellence Award (2001), Duchowski has authored over 150 peer-reviewed papers. He led educational initiatives such as the 'Groovy Graphics Assignments' for SIGGRAPH and co-organized global conferences like ETRA and EuroGraphics. His service includes editorial roles in journals like Transactions on Applied Perception and reviewing for NIH, NSF, and international universities.
Dr. Jasmin Lehmann is a Researcher at the University of Siegen, affiliated with the Faculty of Information Systems and New Media and the Department of Socio-Informatics. Her work focuses on the intersection of human-computer interaction and healthcare technology, particularly for elderly populations and those with dementia. Lehmann's research interests center on gerontechnology and digital health solutions. She investigates how interactive technologies can support aging populations, with particular emphasis on dementia care, social robotics applications in nursing, and ethical considerations of ambient assisted living systems. Her work bridges technical innovation with practical healthcare applications, focusing on user-centered design for vulnerable populations. Analysis of her recent publications reveals a strong trend toward developing and evaluating technology interventions for elderly care. Her research spans digital twins for aging support, music-based exergames for dementia patients, social robotics applications, and ethical frameworks for monitoring technologies in care contexts. These works demonstrate her commitment to creating practical, human-centered technological solutions that address real-world healthcare challenges while considering ethical implications. Lehmann collaborates extensively with researchers across Europe and Japan, reflecting the international nature of her work in aging technology. Her publications appear in reputable journals covering human-computer interaction, sustainability, and healthcare technology domains.
