Cheng-i Wang

Cheng-i Wang has a diverse work experience starting from 2005 as a Freelance Recording Engineer and Bassist. In 2011, they worked as a Digital Signal Theory tutor, Music Information Retrieval tutor, and Max/MSP tutor at New York University. From 2012 to 2013, they served as a Projection Designer at Kinematic Dance Theater. In 2015, they interned at Adobe, where they improved music structural segmentation algorithms and devised an iterative segmentation boundary adjustment algorithm. Cheng-i also worked as a Creative Technologies Lab Intern at Adobe in 2016, conducting research on automatic music segmentation problems and designing crowdsourcing tasks. From 2017 to 2017, Cheng-i Wang worked as an Audio Research Intern at Smule, Inc., focusing on singing style quantification using deep learning approaches. From 2017 to 2018, they worked as a Research Assistant at Calit2, where they focused on music segmentation and automatic music generation. At Smule, Inc., from 2018 to 2022, Cheng-i Wang worked as an Audio Software Engineer, building data-driven solutions for their karaoke app and implementing an in-house content deduplication system. Currently, in 2023, Cheng-i Wang is working as a Deep Learning Engineer at AudioShake.

Cheng-i Wang completed their education in a chronological order. Cheng-i first pursued a Bachelor of Business Administration (BBA) degree in Finance, General at National Taiwan University from 2001 to 2007. Following this, they attended New York University from 2010 to 2013, where they achieved a Master of Music degree in Music Technology. Later, from 2013 to 2018, Cheng-i Wang pursued a Doctor of Philosophy (Ph.D.) degree in Computer Music at UC San Diego. In addition to their formal education, Cheng-i Wang obtained a certification in Data Engineering, Big Data, and Machine Learning on GCP Specialization from Coursera in March 2022.