جست‌وجوی استاد و پژوهشگر

• Sequential Data Analysis
• Medical Data Analytics
• EEG Data Analysis
• +۳ مورد دیگر

Dr Lin Yue is a Lecturer at the University of Adelaide , affiliated with the Faculty of Sciences, Engineering and Technology and the School of Computer and Mathematical Sciences . She earned her PhD from Jilin University, with part of her doctoral studies completed as a joint PhD candidate at the University of Queensland. Past affiliations: Northeast Normal University, University of Queensland, University of Newcastle Her research focuses on Sequential Data Analysis and its applications in Medical Data Analytics, EEG Data Analysis, Brain-Computer Interfaces, Social Media Data Analytics, and Sentiment Analysis . She collaborates with academia, government, and professional organizations, supported by internal and external research grants. Dr Yue is eligible to supervise Masters and PhD students as a Co-Supervisor and contributes to advancing data mining and machine learning techniques in healthcare and time series analysis.

• Geriatric Medicine
• Nursing Home Medicine
• Palliative Care
• +۶ مورد دیگر

Bettina Sandgathe Husebø is a Professor and Head of the Center for Geriatric and Nursing Home Medicine at the Department of Global Health and Community Medicine, Faculty of Medicine, University of Bergen (UiB). She also serves as Innovation Manager at IGS, UiB since 2019. Her extensive career spans clinical practice, research, and leadership roles in geriatric and palliative care. Dr. Husebø completed her medical education at the University of Bonn, Germany in 1988, followed by specialization in Anaesthesiology and Intensive Care in 1995. Her Norwegian qualifications include Medical Specialization in Palliative Medicine (2012) and Nursing Home Medicine (2014) from UiB, along with a PhD from the Faculty of Medicine Dentistry at UiB in 2008. She further enhanced her expertise with a Postgraduate Safety, Quality, Informatics and Leadership (SQIL) Program from Harvard University in 2021. Her research focuses on critical geriatric issues including pain assessment and management in dementia patients, behavioral disturbances in dementia, palliative care in nursing homes, and digital phenotyping applications for elderly care. She has pioneered work on the relationship between pain, agitation, and neuropsychiatric symptoms in dementia patients, particularly through the COSMOS trial and LIVE@Home.Path study. Her recent publications (2023-2025) demonstrate a strong emphasis on digital health solutions for dementia care, with particular focus on activity monitoring, pain assessment through technology, and community-based interventions for aging populations. Her work bridges clinical geriatrics, technology innovation, and patient-centered care models. Among her notable recognitions are the National Dementia Award by His Majesty King Harald of Norway (2022) and multiple awards for research excellence in pain management and palliative care. Her work has significantly influenced Norwegian healthcare policy regarding dementia care and end-of-life practices. As an educator, she lectures in English, German, and Norwegian on dementia, pain in dementia, innovation technologies for older adults, symptom management at end-of-life, systematic medication review, and advance care planning. She has received teaching awards including 'Teacher of the Year' from the Faculty of Medicine and Dentistry at UiB. Dr. Husebø leads the Center for Geriatric and Nursing Home Medicine (SEFAS) and has been instrumental in establishing Norway's first palliative care ward in a nursing home. Her research group focuses on translating evidence into practice to improve quality of life for elderly patients, particularly those with dementia.

Ian Quinnمشاهده پروفایل

استاد

• Music Theory
• Harmonic Analysis
• Music Cognition
• +۴ مورد دیگر

Ian Quinn is the Allen Forte Professor of Music Theory and Director of Graduate Studies (DGS) at Yale University, within the Department of Music in the College of Arts and Science. He holds a B.A. from Columbia University (1993), M.A. and Ph.D. from the Eastman School of Music (1998, 2004). Prior to Yale, he taught at the University of Chicago and University of Oregon, and was a CASBS Residential Fellow at Stanford (2008-09). His research focuses on tonal harmony, corpus studies, music cognition, and the intersections of mathematics and computation in music theory. He edited the Journal of Music Theory (2004–2011) and co-organized the 2009 Society for Mathematics and Computation in Music conference. He leads the Yale-New Haven Regular Singing (YNHRS) shape-note singing group and serves on editorial boards for Journal of Mathematics and Music and the Northeast Music Cognition Group (NEMCOG). Quinn’s work has earned awards from the Society for Music Theory, including the Emerging Scholar Award (2004) and Outstanding Publication Award (2006/2007). His research spans analytical frameworks for harmonic function, corpus-based studies of tonal systems, and empirical investigations of music perception. He has advised over 15 doctoral students, many now in academic and industry roles. Key collaborations include co-authoring the Oxford Handbook of Corpus Studies in Music (2023) and developing the Yale-Classical Archives Corpus. His work bridges theoretical, computational, and historical approaches to music analysis, with contributions to Science , Music Theory Spectrum , and Perspectives of New Music .

Courtney N. Reedمشاهده پروفایل

مدرس

• Human-Computer Interaction
• Music Technology
• Digital Instrument Design
• +۵ مورد دیگر

Courtney N. Reed is a Lecturer in Digital Technologies at Loughborough University London, where she joined in November 2023. She maintains a dual role as a visiting research fellow at the Max Planck Institute for Informatics. Her academic journey includes a BMus in Electronic Production and Design from Berklee College of Music (2016), followed by an MSc (2018) and PhD (2023) in Computer Science from Queen Mary University of London. Prior to her current position, she completed postdoctoral research at both the Max Planck Institute for Informatics and King's College London. Bachelor of Music: Electronic Production and Design, Berklee College of Music (2016) Master of Science: Computer Science, Queen Mary University of London (2018) Doctor of Philosophy: Computer Science, Queen Mary University of London (2023) Dr. Reed's research explores the entangled relationships between humans, bodies, instruments, and technology in music interaction, with particular focus on vocal electromyography (VoxEMG) and the vocalist-voice relationship. Her work incorporates feminist and post-human theories to examine sociopolitical contexts within arts technology, aiming to design for creativity while acknowledging individual, messy bodies in artistic practice. She has developed an open-source platform for vocal electromyography to investigate how biosignal feedback changes understanding and perception of the body in vocal performance. Her interdisciplinary approach bridges music technology, human-computer interaction, and embodied interaction studies. Analysis of Dr. Reed's recent publications (2023-2025) reveals a strong thematic focus on embodied interaction in music technology, with particular emphasis on vocal performance, biosignal feedback, and the philosophical underpinnings of digital instrument design. Her work consistently integrates theoretical frameworks like Karen Barad's agential realism with practical applications in digital musical instruments. Key trends include the exploration of ambiguity in data representation, the sociocultural dimensions of timbre in instrument design, and the development of novel methodologies for understanding embodied musical experiences through micro-phenomenology and ethnographic approaches. ACM SIGCHI Outstanding Dissertation Award (2024) for her thesis 'Imagining & Sensing: Understanding and Extending the Vocalist-Voice Relationship Through Biosignal Feedback' Best Newcomer Award at Loughborough University London's Community Awards Celebration (2024) Dr. Reed actively contributes to the academic community through conference organization and leadership roles. She serves as Member-at-Large on the NIME Board, previously chaired papers for NIME 2024, and co-organized the IBM SkillsBuild Sprint at Loughborough London. She has also chaired sessions at the ACM TEI Conference and co-chaired the Student Design Competition. Her collaborative work spans multiple institutions and includes significant contributions to interdisciplinary projects that bridge music, technology, and human experience. She has been instrumental in developing the senSInt research group and the RaveNET wearable network project. Dr. Reed leads the senSInt research group which focuses on sensorimotor interaction in music and performance contexts. The group develops innovative technologies including the VoxEMG platform for vocal electromyography, the Bones anti-corset for vocal performance, and the RaveNET network of wearable biosensing nodes. These projects explore the intersection of biosignals, embodied interaction, and musical expression, creating novel frameworks for understanding how technology mediates human creativity and performance. The group frequently collaborates with musicians, technologists, and theorists to develop and test these systems in real-world performance contexts.

Long Nguyenمشاهده پروفایل

استاد

• Nonparametric Bayesian Statistics
• Optimal Transport
• Machine Learning
• +۴ مورد دیگر

Long Nguyen is a Professor of Statistics at the University of Michigan, Ann Arbor, with a courtesy appointment in Electrical Engineering and Computer Science. He is affiliated with the Michigan Institute for Data Science (MIDAS) and the Vietnam Institute for Advanced Study in Mathematics (VIASM). His research focuses on Bayesian nonparametrics, optimal transport, machine learning, and spatiotemporal data analysis. Nguyen holds a PhD in Computer Science from UC Berkeley and has held postdoctoral positions at Duke University and the Statistical and Applied Mathematical Institute. Education: B.Sc. in Computer Science from Pohang University of Science and Technology; M.Sc. in Mathematics from Arizona State University; Ph.D. in Computer Science from UC Berkeley (2007). Research Interests: Bayesian nonparametric methods, optimal transport theory, statistical inference for complex models, and applications in spatiotemporal data, functional data analysis, and hierarchical modeling. He emphasizes developing scalable algorithms and geometric approaches for statistical learning. Editorial Roles : Annals of Statistics Journal of Machine Learning Research SIAM Journal on Mathematics of Data Science Bayesian Analysis Awards : IMS Fellow, ASA Fellow NSF CAREER Award IEEE Signal Processing Young Author Award L. J. Savage Dissertation Award (via student Aritra Guha) Advising & Collaborations : Guided over 20 PhD students and postdocs, many now in academia and industry. Collaborates on projects in AI ethics, music theory, and environmental data science. Active in organizing summer schools in Vietnam on Bayesian statistics and machine learning. Labs & Teams : Co-leads the Statistical Machine Learning reading group at U-M and collaborates with the VIASM on advanced mathematical research in Hanoi.

Ben Swiftمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Machine Learning
• Computational Art & Music
• +۴ مورد دیگر

Dr. Ben Swift is a Senior Lecturer at the School of Cybernetics, ANU, specializing in AI, computational art, and cybernetics. He leads the Cybernetic Studio, an interdisciplinary collective exploring cybernetic systems through hardware/software/people collaborations. As a livecoding artist, he performs globally and co-founded the ANU Laptop Ensemble. His research spans generative AI, open-source tools like Extempore, and UX design. Education: PhD in Computer Science (ANU) Projects: Australia's Digital Economy (2022), The Augmented Web (2019) Research focuses on AI creativity, biofeedback interfaces, and computational music. His work bridges technical innovation with artistic expression, evident in projects like TSPNet and adversarial camera systems. Key contributions include Extempore’s development and studies in live coding disruption. Awards unspecified but recognized internationally for interdisciplinary impact.

P.J. Whiteمشاهده پروفایل

مدرس ارشد

• Ageing
• Product Design
• Service Design
• +۶ مورد دیگر

Dr. P.J. White is a Senior Lecturer and Research Director at the Department of Humanities and designCORE at South East Technological University (SETU). He also holds adjunct roles at McMaster University (Adjunct Full Professor) and the McMaster Institute for Research on Aging, Canada. His expertise lies in Product and Service Design, with a focus on Aging, Co-Design, and Interdisciplinary Research. Education: PhD in Product Design (Maynooth University, 2012), National Diploma in Industrial Design (2002), and a Bachelor’s in Industrial Design (2002). Research interests include aging-related design, participatory design methodologies, and addressing the digital divide in aging populations. Key projects involve health services co-design (HSE), dementia standards development (NSAI), and global collaborations in Uganda and Mozambique. Notable awards include the Gerontological Society of America David A. Peterson Award (2020) and design accolades from the Institute of Designers Ireland (2002, 2015). He supervises PhD students and has led funded projects like the SFI-supported labor market digital tools initiative. As SETU’s representative in Carlow’s Age Friendly Alliance, he bridges academia and policy. His work spans 53 publications, including high-impact journal articles and conference contributions.

Steven Livingstoneمشاهده پروفایل

دانشیار

• Data Science
• Affective Computing
• Human Computer Interaction
• +۴ مورد دیگر

Steven Livingstone is an Associate Professor in the Department of Computer Science at Ontario Tech University, Faculty of Science. His research focuses on affective data science, applying machine learning and statistical modeling to understand emotion and its disorders. He leads the Affective Data Science Lab (ADSL), specializing in emotion recognition technologies using physiological data like EEG and motion capture. Livingstone holds a PhD from The University of Queensland (2008) and has published over 70 peer-reviewed papers, with over 2,400 citations. His RAVDESS dataset is widely used in speech emotion recognition research. His research interests span data science, affective computing, and music's role in emotion. Recent work emphasizes data provisioning for deep learning applications. Livingstone teaches courses including Scientific Data Analysis and Information Visualization. He actively mentors undergraduate and graduate students in his lab, focusing on research assistantships in emotion technology development. Key contributions include studies on musical tempo’s physiological effects, Parkinson’s disease facial mimicry deficits, and ensemble performance dynamics. His work has been featured in The Atlantic, NBC Today, and on the cover of Informatik Spektrum. The ADSL lab collaborates on projects combining data analytics with human-computer interaction to advance emotion-aware systems.

R. Luke DuBoisمشاهده پروفایل

دانشیار

• Digital Media
• Human Computer Interaction
• Cyber Physical Systems
• +۳ مورد دیگر

R. Luke DuBois is an Associate Professor and Co-Chair of the Technology, Culture and Society Department at the NYU Tandon School of Engineering, where he also directs the Integrated Design & Media program and the Brooklyn Experimental Media Center. He holds a DMA in music composition from Columbia University and is a renowned artist, composer, and performer whose work explores intersections between technology, sound, and visual media. His research focuses on digital media, human-computer interaction, and emerging technologies applied to artistic expression and accessibility. Key roles include directing the SONYC initiative (addressing urban noise pollution via AI) and leading the NYU Ability Project (advancing disability studies through technology). He has collaborated with institutions like the Smithsonian and artists such as Maya Lin, and his work has been exhibited globally, including at the Sundance Film Festival and the Aspen Institute. DuBois co-developed the Jitter software suite for real-time data manipulation and performs in avant-garde groups like Bioluminescence and Fair Use. His artistic practice combines time-lapse phonography, interactive installations, and interdisciplinary projects that critique cultural ephemera while advancing accessibility in STEM and the arts. Recent contributions include browser-based tools for accessible music notation (SoundCells) and sonification techniques for calculus education. He serves on the Board of the ISSUE Project Room and has been featured in major publications like the New York Times and TED Conference talks.

Simon Dixonمشاهده پروفایل

استاد

• Music Information Retrieval
• Artificial Intelligence
• Music Signal Processing
• +۳ مورد دیگر

Simon Dixon is a Professor of Computer Science and Director of the UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM CDT) at Queen Mary University of London. He also serves as Deputy Director of the Centre for Digital Music (C4DM). His work focuses on music informatics, AI, and computational musicology, with emphasis on music signal analysis, performance modeling, and MIR applications. He leads projects funded by UKRI, Innovate UK, and industry partners like Yamaha and Spotify. Dixon has supervised over 20 PhD students and contributed to major initiatives like the Jazz Digital Archives Project and the Dig that Lick study on jazz melodic patterns. His research has been recognized with awards including the Peter Claricoats Award and Turing Fellowship. Education: PhD in Computer Science, BSc(Hons) in related field, with music qualifications (AMusA LMusA) Roles: AIM CDT Director, C4DM Deputy Director, EU H2020 MIP-Frontiers PI Research: Music transcription, expressive performance analysis, MIR, and AI applications in music education Key projects include industry collaborations (e.g., Yamaha for jazz piano modeling), semantic audio analysis, and large-scale music corpus studies. His team has pioneered methods in chord detection, source separation, and alignment algorithms, with top rankings in MIREX evaluations. Publications span journals like TISMIR and ICASSP, with a focus on foundational MIR techniques and AI-driven music systems. His work bridges technical innovation with cultural heritage through projects like JazzDAP and the Dig that Lick analysis of jazz solos.

Benjamin Ricaudمشاهده پروفایل

دانشیار

• Machine Learning
• Graph Signal Processing
• Explainable AI
• +۳ مورد دیگر

Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.

• Neural Networks
• Genetic Algorithms
• System Identification and Control
• +۷ مورد دیگر

FURUZUKI, Takayuki is a Professor at Waseda University's Faculty of Science and Engineering, Graduate School of Information, Production, and Systems. He holds a PhD from Kyushu Institute of Technology and maintains the NCLab research group (http://nclab.w.waseda.jp/nclab/). His professional memberships include the Institute of Electronics, Information and Communication Engineers, Institute of Electrical Engineers of Japan, Society of Instrument and Control Engineers, and IEEE. His educational background includes a PhD in Information Science from Kyushu Institute of Technology (1994-1997), a Master's degree in Electronics Engineering from Sun Yat-Sen University (1983-1986), and a Bachelor's degree in Electrical & Electronic Systems Engineering from Sun Yat-Sen University (1979-1983). Professor FURUZUKI's research spans neural networks, genetic algorithms, system identification and control, complex systems, bioinformatics, and combinatorial optimization. His work integrates soft computing techniques with medical informatics and control systems engineering, developing innovative approaches for problems ranging from brain tumor segmentation to music information retrieval. His research methodology often combines theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His recent publication record shows a strong focus on sequential recommendation systems, graph neural networks for medical imaging, music information retrieval, and power systems analysis. The consistent thread across his work is the application of advanced machine learning techniques to solve complex, real-world problems across diverse domains, with particular emphasis on handling missing data, improving model efficiency, and enhancing prediction accuracy. His scientific achievements have been recognized with several prestigious awards: ISCIIA2008 Excellent Paper Award (2008) Institute of Electrical Engineers of Japan Excellent Paper Award (2001) Guangdong Province Higher Education Science and Technology Progress Second Prize (1991) Guangdong Province Higher Education Science and Technology Progress Second Prize (1989) Chinese Ministry of Education Scientific and Technological Achievements Second Prize (1986) Professor FURUZUKI has maintained continuous research activity since joining Waseda University in 2003, with significant contributions to multiple research projects spanning neural computation, soft computing, and system engineering. His work demonstrates strong international collaboration, with publications involving researchers from various institutions worldwide. His research output includes over 410 papers with 3,623 citations and an h-index of 23 according to Scopus data. The NCLab research group, which he leads, focuses on neural computation and related areas, providing a platform for interdisciplinary research that bridges theoretical foundations with practical applications in healthcare, energy systems, and intelligent transportation. His laboratory environment fosters innovation in machine learning algorithms and their application to real-world problems, with particular emphasis on developing efficient, robust solutions for complex data analysis tasks.

Dian Tjondronegoroمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Professor Dian Tjondronegoro is a leading academic at Griffith University's Department of Management within the Griffith Business School. He holds roles such as Acting Head of Department and Deputy Head (Research), and is affiliated with the Centre for Work, Organisation and Wellbeing, Griffith Asia Institute, and the Griffith Inclusive Future Beacon. His research focuses on AI ethics, eHealth systems, and digital governance, with over $9M in grants from bodies like the ARC and NHMRC. He has published 145+ peer-reviewed papers and leads initiatives like the 'Governing in the Digital Age' program. A Fellow of the Australian Computer Society and Senior Member of IEEE/ACM, he has won the Gold Disrupters Award (2019) and multiple teaching accolades. Education: PhD in Information Systems (Deakin University, 2005), BIS (QUT, 2001). Research Interests: AI, machine learning, healthcare innovation, responsible AI, workplace design, and digital economy strategies. His articles emphasize AI applications in healthcare monitoring, workplace productivity, and ethical surveillance. Recent work explores post-COVID workplace trends and AI-driven public health solutions. He advises on government policy and innovation through roles like Gold Coast Health's Digital Innovation Advisory Committee. His teaching includes courses on digital strategy and innovation management.

• Sociotechnical Systems
• Health Informatics
• Animal-Computer Interaction
• +۴ مورد دیگر

Patrick C. Shih is an Associate Professor of Informatics at Indiana University Bloomington's Luddy School of Informatics, Computing, and Engineering, where he directs the Societal Computing Lab (SoCo Lab). He holds leadership roles as Director of Graduate Studies for Data Science and Co-Director of the Animal Informatics MS/PhD track. His affiliations include core faculty positions in Health Informatics and Animal-Computer Interaction programs. Education: Ph.D. in Information and Computer Science (UC Irvine), M.S. in Information Networking (Carnegie Mellon University), B.S. in Computer Science and Engineering (UCLA). Previously held research positions at Microsoft Research and IBM Research. Research Focus: Develops sociotechnical systems to support health/wellbeing in underserved populations, including older adults, autistic individuals, and communities affected by health disparities. Designs technologies for animal-assisted interventions, animal welfare enhancement, and empathy cultivation. Major projects include mobile health interventions for stress management, Alzheimer's disease tools for African American communities, and physical activity promotion for autistic adults. Publication Trends: Recent work (2023-2025) emphasizes AI-driven health tools for vulnerable groups, including generative AI for Alzheimer's education, mobile apps for chronic disease management, and culturally tailored interventions. Research spans HCI, health informatics, and community-based participatory design, with strong focus on real-world applicability and health equity. Awards & Honors: ACM Senior Member (2020) NSF CAREER Award (2022) Multiple Indiana University teaching/mentoring awards (2016-2019) Euroinvent Silver Medal Award Best Paper Awards at ACM CSCW, ISCRAM Grants & Projects: $3.7M NIH grant for studying environmental stressors and cognitive decline in older adults (2024) NSF-funded mobile stress measurement app for midlife adults (2023) Mobile 'just-in-time' stress intervention for breast cancer survivors (2022) Labs & Teams: Leads the Societal Computing Lab (SoCo Lab) focusing on community-centered technology design. Collaborates with healthcare institutions, zoos, and community organizations. Develops interdisciplinary research teams for projects in health informatics, animal-computer interaction, and crisis response technologies.

• Human-Computer Interaction
• Digital Wellbeing
• Health and Wellbeing Technologies
• +۱۲ مورد دیگر

Dr Bernd Ploderer is an Associate Professor in the School of Computer Science at Queensland University of Technology (QUT), where he conducts research in Human-Computer Interaction (HCI), focusing on health, wellbeing, and human-centred computing. He leads the Digital Wellbeing Lab and is actively involved in multiple Australian Competitive Grant-funded projects, including those on generative AI for self-care, tangible intergenerational interaction, and children's active play. Education: PhD in Human-Computer Interaction (University of Melbourne, 2011), MA in Communications (University of Salzburg, 2007), BSc (Hons) in Information Systems (FH Joanneum Graz, 2003) Previous Positions: Research Fellow and Lecturer at the University of Melbourne (2011–2015) Professional Recognition: Fellow of the Higher Education Academy, Graduate Certificate in Academic Practice (QUT, 2017) Dr Ploderer’s research centers on designing interactive technologies that support personal and social wellbeing, with a strong emphasis on user-centered and co-design methods. His work spans domains such as digital self-care , mobile health , generative AI , tangible and embodied interaction , and intergenerational and child play . He has developed innovative educational methods in HCI, notably the ‘working field theories for design’ approach, which received an Honorable Mention at CHI 2021. His recent publications (2023–2025) reflect a growing interest in AI-mediated self-care, nature-connected technologies, and inclusive design for diverse populations including teens, older adults, and individuals with disabilities. These works appear in top-tier venues such as CHI , DIS , and Personal and Ubiquitous Computing . Dr Ploderer has received several awards, including: QUT Vice Chancellor's Performance Award (2016) PCHF Innovation Grant (2018) Metro North Hospital and Health Service Link Grant (2017) Honorable Mention at CHI 2021 Associate Chair for Health Subcommittee at CHI 2022 He is actively involved in research supervision, currently mentoring PhD and Honours students on topics such as conversational agents for music education, mental wellbeing for international students, and relational technologies for dementia care. He has supervised numerous completed PhD and Masters theses, often in collaboration with Professor Margot Brereton and other QUT researchers. Dr Ploderer also contributes to research ethics and integrity within his school and serves on committees for CHI and CHISIG. His lab, the Digital Wellbeing Lab, fosters interdisciplinary collaboration focused on creating technology that enhances social, mental, and physical wellbeing.