Gus Xia
Music X Lab, Abu Dhabi

Gus is an Assistant Professor in Machine Learning at MBZUAI, and he also holds affiliations at NYU Shanghai and NYU.
Gus has been leading the Music X Lab in developing intelligent systems that help people better compose, perform, and learn music.
Gus received his Ph.D. in the Machine Learning Department at Carnegie Mellon University in 2016, and he was a Neukom Fellow at Dartmouth from 2016 to 2017. Gus is also a professional Di and Xiao (Chinese flute and vertical flute) player. He plays as a soloist in the NYU Shanghai Jazz Ensemble, Pitt Carpathian Ensemble, and Chinese Music Institute of Peking University, where he also served as the president and assistant conductor.
He held a Di&Xiao solo concert at Peking University in 2010, and his team held a Music AI concert in Dubai in 2022.

Matteo Farnè
University of Bologna, Department of Statistical Sciences, Italy

Matteo Farnè (1988) began his piano studies with Olaf John Laneri and received his diploma in piano (2010) and second-level academic diploma with composition-interpretation address (2012) at the "G. Verdi" Institute in Ravenna. Committed to music popularization, he is a fortepiano graduate from the Imola Piano Academy under the guidance of Stefano Fiuzzi. Admitted to the Collegio Superiore of the University of Bologna, he graduated in 2012 with a master degree in Statistics, Economics and Business with highest honors, and then received his doctorate in Statistics in 2016 from the University of Bologna, where he is currently a tenure-track researcher in statistics.
During his carrier, he has held study, teaching and research positions in England (University College London), Germany (European Central Bank), and the United States (UC Davis). His research interests mainly lie in the field of high-dimensional statistics, and include signal processing methods for musical data analysis.
Recently, he attended the master in Music Analysis and Theory sponsored by GATM (Gruppo di Analisi e Teoria Musicale), where he graduated cum laude at the end of 2021 under the guidance of Mario Baroni, in collaboration with the Bologna Liszt Foundation. His thesis work, a data driven analysis of Liszt’s Trascendental Étude no.1, was presented at the IFCS 2022 Conference “Classification and Data Science in the Digital Age”, where Matteo was delivered the “Chikio Hayashi Award” for the best statistician aged 30-35.

Zhengshan (Kitty) Shi
Stanford University, USA

Dr. Kitty Shi is an accordionist, pianist, bagpipes player, and a music technologist. She received her Ph.D in Computer-Based Music Theory and Acoustics at Stanford's Center for Computer Research in Music and Acoustics (CCRMA). Her research interests lie in the intersection of music information retrieval and human-computer interaction. She is interested in user-curated computer-assisted expressive musical performances.
She is currently a researcher at Stanford University.

Luca Turchet
University of Trento, Italy

Luca Turchet is an associate professor at the Department of Information Engineering and Computer Science of the University of Trento, where he leads the Creative, Intelligent and Multisensory Interactions Laboratory. He holds master degrees in computer science from the University of Verona, in classical guitar and composition from the Music Conservatory of Verona, and in electronic music from the Royal College of Music of Stockholm. He received a PhD in media technology from Aalborg University Copenhagen. He is co-founder and strategic advisor of Elk. He is the founding president of the Internet of Sounds Research Network, and is the Chair of the IEEE Emerging Technology Initiative on the Internet of Sounds. His scientific, artistic, and entrepreneurial research has been funded by several research grants including a Marie-Curie individual fellowship, a grant of the Danish Council for Independent research, as well as grants from the European Space Agency, European Institute of Innovation and Technology and the Italian Ministry for University and Research. His research interests include new interfaces for musical expression, musical metaverse, networked music performance systems, extended reality, haptic technology e multisensory interaction.