Dirk Pflüger is a Professor at the University of Stuttgart's Institute of Parallel and Distributed Systems, within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on high-performance computing (HPC), parallel and distributed systems, and sparse grids. He has led projects in astrophysical simulations, machine learning applications, and uncertainty quantification. Notable contributions include developing scalable algorithms for exascale computing using HPX, Kokkos, and SYCL frameworks. His expertise spans distributed computing architectures, task-based parallel programming, and interdisciplinary applications in astrophysics and medical AI. Recent work includes optimizing hyperparameter tuning, simulating stellar mergers, and enhancing blood glucose prediction models using deep reinforcement learning. Pflüger's research emphasizes performance portability, fault tolerance, and cross-platform collaboration. He has contributed to open-source tools like PLSSVM and hws, which address hardware monitoring and GPU acceleration challenges. His work bridges theoretical advancements with practical implementations for real-world computational problems.
Gabriel Vigliensoni is an Assistant Professor in Creative Artificial Intelligence within the Department of Design and Computation Arts at Concordia University. He holds a PhD in Music Technology from McGill University and maintains active research and creative practice at the intersection of artificial intelligence, music, and human-computer interaction. His work spans academic research, artistic performance, and music production. PhD in Music Technology, McGill University Assistant Professor, Department of Design and Computation Arts, Concordia University Vigliensoni's research focuses on sound and music making through machine learning, human-computer interaction, artificial intelligence, embodied musical interaction, music information retrieval, and new interfaces for musical expression. His approach merges formal musical training with extensive experience in sound recording, music production, and computational techniques. His creative work challenges traditional notions of liveness and immediacy in digital music production through procedural composition and embodied interaction. His recent publications demonstrate a strong focus on interactive machine learning for creative applications, particularly in audio and music domains. The research trends show increasing emphasis on explainable AI for the arts, ethical considerations in AI music applications, and the development of interfaces that facilitate sustained artistic practice with machine learning systems. His work spans from theoretical foundations to practical implementations in musical interfaces. 2025-2027: Co-Applicant, New Frontiers in Research Fund—Exploration 2024–2025: PI, Petro-Canada Young Innovator Award (PCYIA) 2024–2025: Collaborator, Responsible AI UK international partnerships UKRI 2023–2025: PI, Explore and Create | Research Creation (Canada Council for the Arts) 2023–2025: PI, Faculty Research Development Program (Concordia University) 2022–2023: PI, Knowledge Mobilization Grant (FRQSC) 2020–2022: PI, Postdoctoral Research Creation (FRQSC) Vigliensoni supervises graduate students in Design (MDes), Individualized Programs (MA, MSc), and Individualized Programs (PhD). His research is supported by significant grants from Canadian funding agencies as well as international collaborations. His work bridges academic research with artistic practice, creating feedback loops between theoretical exploration and creative output. His studio practice and research involve developing interactive systems for musical expression, with notable projects including Clastic Music, Telematic Awakening, and Re•col•lec•tions. These projects often combine real-time audio processing, machine learning, and gestural interfaces to create novel musical experiences that explore the relationship between human performers and AI systems.
Professor Georgios Kouroupetroglou is a leading academic at the National and Kapodistrian University of Athens (NKUA), serving as Professor in the Department of Informatics and Telecommunications. He holds significant roles including President of the Association for the Advancement of Assistive Technology in Europe (AAATE) and membership in the European Academy of Sciences and Arts. His research focuses on digital accessibility, voice processing, assistive technologies for disabilities, and music computing. He has pioneered initiatives such as the 'Accessibility Unit' and the Speech and Accessibility Research Laboratory at NKUA. Education: B.Sc. in Physics and Ph.D. in Communications and Signal Processing. He has held administrative roles including Chairman of the Department of Informatics and Telecommunications, Director of Postgraduate Studies, and Director of the M.Sc. Programme in 'Language Technology'. Research interests span: Computer Accessibility & Voice User Interfaces Accessibility of Documents and Mathematics Music Computing and Ancient Instruments Gesture-Based UIs and Haptics Inclusive Education Technologies Publications highlight innovations in: Assistive tech for mobility and motor disabilities Audio-tactile interfaces for blind users Accessibility in higher education Acoustic modeling of historical instruments He has authored over 180 papers and books, reviewed numerous EU projects, and teaches courses on accessibility, signal processing, and assistive tech. His work bridges clinical science and technology, emphasizing sustainability and global inclusivity.
Yannis Ioannidis is a Professor at the Department of Informatics and Telecommunications of the National and Kapodistrian University of Athens. His research spans interdisciplinary areas including database systems, virtual reality applications, and cultural heritage technology. He leads projects integrating technology with education, art, and social sciences, such as VR environments for urban planning (ARSINOE VR) and AI-driven scientific discovery frameworks. His work emphasizes collaborative tools for digital storytelling and participatory cultural experiences. Ioannidis has contributed to advancements in database query optimization, text exploration through SQL extensions (DETEXA), and indexing techniques for data lakes. His recent focus includes exploring the intersection of tabletop role-playing games with historical education and ethical AI-human coevolution. He actively participates in initiatives like the EBRAINS infrastructure for brain research and the MagicARTS social VR project. Notable research trends in his publications include leveraging VR for immersive learning, designing hybrid museum kits, and applying AI to support UN Sustainable Development Goals. His work bridges technical innovation with societal impact, addressing challenges in education, art preservation, and ethical technology use.
Dr. Maya Ackerman serves as Associate Professor of Computer Engineering in the School of Engineering at Santa Clara University while concurrently leading WaveAI as CEO. This dual role positions her at the critical intersection of academic research and commercial AI innovation, specializing in computational creativity and human-AI collaboration systems. Her work focuses on transforming theoretical AI concepts into practical creative tools that empower users globally. Her research spans three interconnected domains: foundational clustering algorithms (2007-2014), computational creativity frameworks (2015-2020), and ethical human-AI collaboration (2021-present). Early work established theoretical properties of clustering paradigms published in NIPS and JMLR, while recent research develops musical AI systems like ALYSIA that enable accessible song creation. Her scholarship consistently bridges theoretical rigor with real-world impact, particularly in creative domains where AI augments rather than replaces human expression. Runner-up Best Paper Award (Association for Computational Creativity, 2017) Women in Tech Awards Data Scientist Finalist (2019) NSERC Alexander Bell Canadian Graduate Scholarship (2009-2012) First-Year Assistant Professor Award (Florida State University, 2015) Office of Naval Research grant ($400,000 for clustering research) As mentor, Ackerman has guided numerous graduate students through interdisciplinary projects combining computer science with creative arts, evidenced by asterisked co-authors on publications spanning musical AI, poetry generation, and therapeutic applications. Her WaveAI startup, serving millions globally with tools like LyricStudio, demonstrates successful research commercialization. Current work explores AI's role in bereavement support and gender bias mitigation, reflecting her commitment to socially impactful technology.
Wang Zhaoxia is an Associate Professor of Computer Science (Practice) at the School of Computing and Information Systems, Singapore Management University. Her expertise lies in Artificial Intelligence, Data Science, and Machine Learning with a focus on Natural Language Processing (NLP), Sentiment Analysis, and Knowledge Graphs. She holds a PhD from Nankai University (2004). Research Interests: Her work spans AI-driven solutions for sentiment analysis, maritime risk management, audio-to-music conversion, and social media analytics. Recent projects include leveraging Large Language Models (LLMs) for tasks like logical reasoning, translation optimization, and risk assessment. Publications: Recent work highlights include developing metrics for LLM performance (RAP), emotion analysis in images, and automated music transcription. Her research demonstrates cross-disciplinary applications of AI in fields like finance (ICO success prediction) and health (disease surveillance). Awards: No awards explicitly listed Advising: Supervises 3 PhD/Master students including HUANG Donghao and Nicole Anne Hui Ying TEO Contact: zxwang@smu.edu.sg | +65 68261347
Samuele Vinanzi is a Senior Lecturer in Robotics and Artificial Intelligence at Sheffield Hallam University's School of Computing and Digital Technologies, part of the College of Business, Technology and Engineering. He specializes in Cognitive Robotics, focusing on human-robot interaction, social robotics, and symbolic AI. He holds a PhD from the University of Manchester, where his research explored computational models for intention reading in humanoid robots, supported by Honda Research Institute and AFOSR funding. Previously, he worked as a Postdoctoral Research Associate and Research Assistant at universities in the UK and Italy. His teaching includes leading the MSc AI module on Cognitive Systems and Robotics. Vinanzi has contributed to over 20 peer-reviewed publications since 2017, addressing topics like trust modelling, theory of mind in robots, and cybersecurity in assistive robotics. He serves as an Associate Editor for the International Journal of Advanced Robotic Systems. Education: Bachelor's in Computer Engineering, University of Palermo, 2013 Master's in Computer Engineering, University of Palermo, 2016 PhD in Robotics/AI, University of Manchester, 2021 Research Interests: Cognitive robotics, human-robot collaboration, artificial cognitive architectures, symbolic AI, and machine learning. His work emphasizes robots' ability to perceive human intent, build trust, and engage in socially intelligent interactions. Publications Overview: Over 15 recent works (2021–2025) focus on theory of mind in robotics, trust metrics, computational architectures for social perception, and cybersecurity challenges in healthcare robotics. Key themes include improving transparency in human-robot collaboration and enabling robots to infer complex mental states like false beliefs.
Corey Ford is a Lecturer in Computer and Data Science at the Creative Computing Institute, University of the Arts London. His research focuses on Human-Computer Interaction (HCI), generative AI in music composition, and reflective practices in creative technologies. He holds a BSc in Creative Music Technology and an MRes in Data Science from the University of the West of England. Current role: Lecturer (since July 2024) Previous role: Postdoctoral Research Fellow (May 2024 - July 2024) Education: BSc (2016-2019), MRes (2019-2020) Research Interests : Ford explores creative technologies like AI-driven music composition, reflection in creative processes, and user-centered design for creative tools. His work bridges music technology, HCI, and explainable AI. Recent articles focus on co-creative AI frameworks, generative music systems, and reflection in creative experiences. He has contributed to conferences like CHI and Creativity & Cognition (C&C). Awards : Associate Fellow of the Higher Education Academy (AFHEA) Teaching : Leads modules on computer science fundamentals and HCI in BSc programs Active in open-source projects including the Step-Sequencer and wAIve , demonstrating practical applications of his research.
Navrati Saxena is an Associate Professor in the Department of Computer Science at San José State University (SJSU), California, USA. She previously served as an Assistant/Associate Professor and Director of the Mobile Ubiquitous System Information Center (MUSIC) at Sungkyunkwan University (SKKU), South Korea. Her research focuses on next-generation wireless networks (5G/6G), IoT, vehicular communications, satellite networks, and machine learning applications in telecommunications. She holds a Ph.D. in Informatics and Communications Engineering from the University of Trento, Italy. Education: Ph.D. in Informatics and Communications Engineering, University of Trento, Italy (2005). Research Interests: 5G/6G wireless systems, IoT, social networking, smart grids, D2D communications, vehicular networks, satellite communications, and machine learning-driven network optimization. Her work spans energy efficiency, network architecture design, and disaster-resilient connectivity. Grants & Awards: Secured over $465,000 in research grants from institutions like the Korea Research Foundation and Samsung. Notable awards include the Young Scientist Best Paper Award (Indian Science Congress Association, 2006-2007). Professional Contributions: Supervised 12 Ph.D. students and 22 MS researchers at SKKU. Authored/co-authored one book, two book chapters, and over 90 international journal/conference papers. Served as a guest editor for journals and TPC member for IEEE conferences. Active in diversity initiatives at SJSU, including leadership roles in DEI and anti-racism committees. Labs & Teams: Leads the Mobile Ubiquitous Systems Information Center (MUSIC) at SJSU, focusing on edge-cutting research in wireless networks and education.
Laurel Trainor is a Professor of Psychology, Neuroscience and Behaviour at McMaster University, where she holds multiple prestigious distinctions including Fellow of the Royal Society of Canada (FRSC), Fellow of the Canadian Institute for Advanced Research (CIFAR), and Fellow of the Association for Psychological Science. She is also a McMaster Distinguished University Professor and a Research Scientist at the Rotman Research Institute. Trainor earned her Ph.D. in Experimental Psychology from the University of Toronto in 1991, following an M.A. in the same field (1987) and a Bachelor of Music Performance (1981). Her pioneering research focuses on the neuroscience of auditory development and music perception, particularly how infants acquire musical systems similarly to language acquisition. Trainor has demonstrated that listening to a beat activates motor networks in the brain even without movement, reveals how predictive processes in rhythms shape communication between people, and shows that synchronous movement to musical beats increases prosocial behavior even in infants. Her work spans multiple disciplines including auditory neuroscience, cognitive development, and music therapy. Analysis of Trainor's recent publications (2023-2025) reveals a strong focus on infant and child development, particularly regarding rhythm processing, neural mechanisms of musical perception, and applications for developmental disorders. Her research increasingly incorporates advanced methodologies like machine learning for EEG analysis, online motion tracking, and live performance studies in the unique LIVELab environment. Key themes include the relationship between rhythm perception and motor coordination, cross-modal interactions in music processing, and the therapeutic applications of music. Fellow of the Royal Society of Canada (FRSC) Fellow of the Canadian Institute for Advanced Research (CIFAR) Fellow of the Association for Psychological Science McMaster Distinguished University Professor Lifetime Achievement Award from the Society for Music Perception and Cognition (2022) YWCA Woman of Distinction Award for Arts Culture and Design Co-holder of patent for the Neuro-compensator hearing aid Trainor has secured major funding from prestigious organizations including the Canadian Foundation for Innovation, Canadian Institutes of Health Research, Natural Science and Engineering Research Council of Canada, Social Science Research Council of Canada, and the Grammy Foundation. She is the founding and current director of the McMaster Institute for Music and the Mind (MIMM), which houses the LIVELab—a unique research-concert hall with high acoustic control equipped with multi-person motion capture and EEG systems. This facility enables groundbreaking studies on performer-audience interactions and music's role in health and well-being. Trainor is also an accomplished musician, serving as principal flute of the Burlington Symphony.
Kevin Webster is a Senior Teaching Fellow in the Department of Mathematics at Imperial College London, within the Faculty of Natural Sciences. His research focuses on applied mathematics, numerical and computational mathematics, and machine learning, with particular expertise in dynamical systems, ordinary differential equations, and bifurcation theory. He contributes to both teaching and research, bridging theoretical mathematics with practical applications in fields like machine learning and network analysis. His research interests span areas such as symmetry transformations in neural networks, Lyapunov function approximation, and stability analysis in dynamical systems. Notably, he has developed methods for music data generation and structure induction in melodies using generative models. He also investigates the interplay between discrete geometry and graph neural networks, advancing techniques for network rewiring and optimization. Webster’s work often addresses complex systems, including bifurcation phenomena in differential-algebraic equations and stability analysis in cosmological models such as Bianchi systems. His publications reflect a deep engagement with interdisciplinary topics, combining mathematical rigor with computational innovation.
Holger H. Hoos is a Professor at the University of British Columbia's Computer Science Department, affiliated with the Institute for Computational Intelligence and Cognitive Systems (ICICS) and the Peter-Wall Institute for Advanced Studies. As of 2017, his primary affiliation shifted to Leiden University (Netherlands), though he remains connected to UBC. His research focuses on algorithmics, bioinformatics, computational intelligence, and computer music, with significant contributions to stochastic local search, empirical algorithmics, and SAT solving. Education: Ph.D. (Dr.rer.nat) in Computer Science from TU Darmstadt (1998), postdoctoral fellow at UBC. He has led the BETA Lab and SALIERI Project, exploring biomolecular structure prediction and music algorithms. Research interests span stochastic algorithms, RNA structure prediction, automated algorithm configuration, and interdisciplinary applications like flow cytometry analysis. His work bridges theoretical foundations with empirical methods, emphasizing practical problem-solving. Awards include the AAAI Fellowship, multiple best paper awards (GECCO 2020, IJCAI-JAIR 2010), and leadership roles in conferences like AAAI and LION. He has advised over 30 students, contributing to impactful projects in bioinformatics and AI. Labs/Teams: Founder of the BETA Lab (Bioinformatics, Theoretical and Empirical Algorithms), member of the Laboratory for Computational Intelligence (LCI), and collaborator on SALIERI (Computer Music).
Professor Tapio Lokki is a renowned academic in acoustics and audio engineering at Aalto University's School of Electrical Engineering, Department of Signal Processing and Acoustics. He transitioned from the School of Science's Department of Computer Science to streamline teaching and research in acoustics. His work focuses on concert hall acoustics, spatial sound reproduction, binaural technology, and augmented reality audio. Key projects include developing measurement techniques for concert halls and sensory evaluation methods. He holds the title of Honorary Member of the Acoustical Society of Finland and is a Fellow of the Audio Engineering Society. Research interests include room acoustics modeling, perceptual differences in concert halls, and hearing protection devices. His contributions span psychoacoustic studies, material science for sound absorption, and virtual acoustic environments. He leads the Aalto Acoustics Lab, advancing interdisciplinary applications in audio engineering and environmental acoustics.
Dr. Andrew Gilbert is an Associate Professor in Machine Learning at the University of Surrey, co-leading the Centre for Creative Arts and Technologies (C-CATS). His work bridges computer vision, generative AI, and creative industries, focusing on interpretable and human-centered systems. He leads a multidisciplinary team of 17 PhD students and has published over 100 papers in top venues like CVPR, ECCV, and BMVC. Education & Research: Gilbert earned his PhD in Computer Vision from the University of Surrey (2008) and has pioneered advancements in video understanding, style modeling, and AI-driven media production. His research integrates technical innovation with creative applications, such as AI-generated narratives and blockchain-based content rights. Research Trends: Recent work emphasizes long-term video analysis (DANTE-AD), motion-focused self-supervision (MOFO), and human-AI benchmarking (Human vs. Machine Minds). His projects often involve industry partnerships to address real-world challenges in media production and digital arts. Achievements: Best Paper at CVMP 2023, active BMVA committee member, and contributor to standards in explainable AI. His lab’s annual Gilbertine Weekend Away fosters collaborative research culture. Advising: Supervised 14 PhD completions, with ongoing projects in video diffusion models, decentralized content rights, and multimodal scene understanding. Seeks exceptional candidates for funded UK-based PhD opportunities. Key Labs: C-CATS lab explores AI’s creative applications, while his group develops tools for style transfer (NeAT), action recognition, and 3D performance capture.
Pascal Wallisch is a Clinical Professor of Data Science, Psychology, and Neural Science at New York University, affiliated with the College of Arts & Science. His research focuses on understanding how subjective reality is constructed through perception and cognitive processes. He holds a PhD in Cognitive Neuroscience from the University of Chicago and has held progressively senior roles at NYU since 2007, including Post-Doctoral Fellowships and Clinical Professorships across departments. Education: PhD in Psychology (Cognitive Neuroscience), University of Chicago, 2007 MA in Psychology, University of Chicago, 2004 B.A. (Vordiplom) in Psychology, Free University of Berlin, 2000 Research Interests: Wallisch explores perceptual ambiguity, subjective reality construction, and interdisciplinary applications of data science in neuroscience. His work bridges cognitive psychology, computational modeling, and behavioral analysis, with notable studies on phenomena like 'The Dress' illusion and motion perception. He emphasizes educational innovation, including computational neuroscience training through platforms like Neuromatch Academy. Key Research Trends: Recent publications highlight themes in AI-assisted learning, perceptual disagreement mechanisms, and the neural basis of social behaviors. His work often integrates data-driven approaches to address questions in perception, psychopharmacology, and social psychology. Awards & Honors: 2021/22 CAS Teaching Innovation Award 2020 Teach/Tech Award 2016 Golden Dozen College Teaching Award Multiple postdoctoral and graduate fellowships from NIH, University of Chicago, and German academic institutions Grants & Advising: While specific grants are not listed, his roles reflect sustained institutional support. He has advised numerous students in interdisciplinary neuroscience and data science programs, fostering collaborative research environments like the FoxLab (https://www.foxlabnyu.com/). Labs & Teams: Associated with the FoxLab at NYU, focusing on computational neuroscience, perception, and educational technology. Collaborates across NYU’s Center for Data Science, Psychology, and Neural Science departments.
