​Penelope Bekiari is an artist-researcher, sound artist and Creative Technologist specialising in human-centred AI for music, embodied interaction and adaptive creative systems. She is currently undertaking a PhD exploring the relationship between sound, technology and the human body, and holds an MMus in Electroacoustic Composition from the University of Manchester, alongside degrees from the National and Kapodistrian University of Athens (NKUA). Her work has been presented internationally at festivals, conferences and institutions including Ultima – Oslo Contemporary Music Festival, EASTN-DC (Cardiff), MANTIS (Manchester), CIME/ICEM (Montpellier), Spatial Audio Gathering (Leicester), Sound and Music Computing (Porto and Zagreb), International Computer Music Conference (Hamburg), MA/IN Festival (Rome), BEAST Festival (UK), SOUND/IMAGE (UK), the Institute for Data Science at the University of Manchester, the Greek National Opera and the Athens Concert Hall. Recent projects and distinctions include the Art of Neuroscience 2025 Prize, awarded by the Netherlands Institute for Neuroscience for Rustle, an immersive electroacoustic performance integrating real-time EEG and heart-rate data with Python-based machine learning to dynamically shape audiovisual processes. Her recent work also includes Permeable Signal, presented at Sonorities Festival and PROCESSES Festival (2025–2026), and Rhoē No. 1 for piano and live bio-electronics, presented at BEAST and the Athens Contemporary Music Festival (2024–2025). She was also Artist in Residence at the Greek National Opera (2024–2025) in a programme funded by the European Commission. Her artistic and research practice examines how physiological data, adaptive artificial intelligence and sound can function as compositional and performative agents within contemporary artistic practice. Her work brings together neuroaesthetics, EEG-based musical interaction, physiological computing, embodied cognition, interactive media, electroacoustic composition and human–machine interaction, with a particular interest in how perception, cognition and bodily states can shape responsive musical environments. Through the integration of biosignals including EEG, heart rate, galvanic skin response (GSR), breath, movement and gesture, she develops adaptive sonic and interactive systems that explore distributed musical agency, performability, accessibility and the relationship between cognition, embodiment and sound. Her current research increasingly focuses on human-centred AI, machine learning and deep-learning approaches for music and interactive media, including the development of adaptive and neuro-responsive systems capable of analysing physiological and behavioural data in real time. Alongside this, she investigates neuroaesthetics, accessibility, music and wellbeing, and inclusive creative technologies, with an emphasis on designing interactive systems that support agency, engagement and meaningful human–machine interaction across diverse users.