معرفی
Dr. Cheng-Zhi Anna Huang is currently a faculty member at the Massachusetts Institute of Technology (MIT) with a joint appointment between the Department of Electrical Engineering and Computer Science (EECS) and the Department of Music and Theater Arts (MTA), spanning both the College of Computing and School of Humanities, Arts, and Social Sciences. She simultaneously holds an adjunct associate professor position at the Université de Montréal's Department of Computer Science and Operations Research. Her academic background includes a PhD from Harvard University, a master's from the MIT Media Lab, and dual bachelor's degrees in music composition and computer science from the University of Southern California.
Dr. Huang's research focuses on human-AI co-creation in music, with particular emphasis on:
- Generative AI models for music composition and performance
- Neural network interpretability for musical applications
- Interactive systems for real-time human-AI collaboration
- Reinforcement learning frameworks for creative expression
- Cross-cultural music modeling and computational musicology
Her publication portfolio shows strong emphasis on generative models for music, particularly transformer architectures, with consistent output in top AI/ML venues since 2014. Recent work focuses on controllable music synthesis, human-AI co-creation frameworks, and performance modeling. The 14 most recent publications demonstrate progression from fundamental music representation research toward sophisticated interactive systems and evaluation frameworks.
Awards and Honors:
- Canada CIFAR AI Chair (Mila)
- Outstanding Paper Award at NeurIPS Workshop CtrlGen (2021)
- First Prize in San Francisco Choral Artists New Voices Project (composition)
Dr. Huang actively supervises graduate students, with recent master's advisees including Nithya Shikarpur (2024) working on human-AI co-creation for Hindustani music, and Yusong Wu (2023) researching controllable performance synthesis. She is currently recruiting postdoctoral researchers and PhD students for her MIT Music Technology laboratory, focusing on multi-agent reinforcement learning and human-AI interaction in musical contexts.
