Dr Rebecca Fiebrink<\/em><\/strong> makes new accessible and creative technologies. As a Reader at the Creative Computing Institute at University of the Arts London, her teaching and research focus largely on how machine learning and\u00a0artificial intelligence can change human creative practices. Fiebrink is the developer of the Wekinator creative machine learning software, which is used around the world by musicians, artists, game designers, and educators. She\u00a0is the creator of the world\u2019s first online class about machine learning for music and art. Much of her work is driven by a belief in the importance of inclusion, participation, and accessibility: she works frequently with human-centred\u00a0and participatory design processes, and she is currently working on projects related to creating new accessible technologies with people with disabilities, and designing inclusive machine learning curricula and tools. Dr. Fiebrink\u00a0previously taught at Goldsmiths University of London and Princeton University, and she has worked with companies including Microsoft, Smule, and Imagine Research. She holds a PhD in Computer Science from Princeton\u00a0University.<\/p>\n\n\n\n Chris Kiefer is a computer-musician, musical instrument designer and Lecturer in Music Technology at the University of Sussex. He performs with custom-made instruments; including an augmented self-resonating cello as half of improv-duo Feedback Cell, and with feedback-drone-quartet \u2018Brain Dead Ensemble\u2019. His research specialises in musician-computer interaction, physical computing, machine learning and complex systems. He is the principal investigator for the AHRC Feedback Musicianship Network, and co-Investigator on AHRC Musically Intelligent Machines Interacting Creatively.<\/p>\n","protected":false},"excerpt":{"rendered":" Rebecca Fiebrink Dr Rebecca Fiebrink makes new accessible and creative technologies. As a Reader at the Creative Computing Institute at University of the Arts London, her teaching and research focus largely on how machine learning …<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[23,14,24],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/digital.eca.ed.ac.uk\/dialogues\/wp-json\/wp\/v2\/posts\/571"}],"collection":[{"href":"https:\/\/digital.eca.ed.ac.uk\/dialogues\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/digital.eca.ed.ac.uk\/dialogues\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/digital.eca.ed.ac.uk\/dialogues\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/digital.eca.ed.ac.uk\/dialogues\/wp-json\/wp\/v2\/comments?post=571"}],"version-history":[{"count":1,"href":"https:\/\/digital.eca.ed.ac.uk\/dialogues\/wp-json\/wp\/v2\/posts\/571\/revisions"}],"predecessor-version":[{"id":572,"href":"https:\/\/digital.eca.ed.ac.uk\/dialogues\/wp-json\/wp\/v2\/posts\/571\/revisions\/572"}],"wp:attachment":[{"href":"https:\/\/digital.eca.ed.ac.uk\/dialogues\/wp-json\/wp\/v2\/media?parent=571"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/digital.eca.ed.ac.uk\/dialogues\/wp-json\/wp\/v2\/categories?post=571"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/digital.eca.ed.ac.uk\/dialogues\/wp-json\/wp\/v2\/tags?post=571"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Chris Kiefer<\/h4>\n\n\n\n