MuSe Data Chair

Alice Baird

University of Augsburg, DE,

An audio researcher from the UK, with interdisplinary experience across computer science and the arts. Alice has an MFA in Sound Art from Columbia University’s Computer Music Center and worked as a Research Assistant for the Chair of Complex and Intelligent Systems at Passau University, Germany. Currently a Ph.D Fellow of the ZD.B, supervised by Professor Prof. Björn Schuller at the Chair of Embedded Intelligence for Healthcare and Wellbeing, University of Augsburg, Germany. Her research is in the field of intelligent audio, and focuses on the domain of speech and general audio. Alice has experience with machine learning, and data analysis methods, as well as audio and speech processing. Core research interests include but not limited to: computer audition, health informatics, affective computing, computational paralinguistic, speech pathologies, audio synthesis, and the perception of sound.

Georgios Rizos

Imperial College London, UK

Georgios Rizos is a doctoral candidate at Imperial College London, UK, working on deep uncertainty quantification of multimodal data as a member of the Group on Language, Audio, and Music (GLAM). His PhD work is supervised by Dr. Björn Schuller and funded by the prestigious President's Scholarship of Imperial College London (EPSRC Grant No. 2021037). Between 2013 and 2017, he was a research assistant at the Information Technologies Institute of the Centre for Research and Technology Hellas (CERTH-ITI), working on machine learning for online social network data as part of the Multimedia Knowledge and Social Media Analytics Lab (MKLab), and supervised by Dr. Symeon Papadopoulos and Dr. Ioannis Kompatsiaris. Georgios holds a diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki, Greece (2012), and an MSc with distinction in Biomedical Engineering from Imperial College London (2013). Research interests include analysis of multimodal data (vision, audio, text, graphs), quantification of different factors of predictive uncertainty, and application thereof on active and reinforcement learning.