Zhenqiao Song
Carnegie Mellon University
zhenqias@andrew.cmu.edu
Bio
Zhenqiao Song is a fourth-year Ph.D. student in the Language Technologies Institute at Carnegie Mellon University, advised by Prof. Lei Li. Her research focuses on developing generative models for scientific domains, with applications in biology and chemistry. Before her doctoral studies, she was a researcher at ByteDance AI Lab, where she worked on natural language processing. She has published in leading journals and conferences, including Nature Communications, ICML, ICLR, NeurIPS, ACL and so on. In addition, she has served as an organizer for major academic events, such as the NeurIPS 2023 GenBio Workshop and the ICML 2025 GenBio Workshop.
Areas of Research
- AI for Healthcare and Life Sciences
Generative AI for Functional Protein Design
The rapid advancement of artificial intelligence is transforming medicine and the life sciences. Traditional approaches to designing biomolecules, such as proteins and nucleic acids, are constrained by immense design spaces and slow experimental cycles. My research aims to overcome these challenges by developing generative AI and foundation models tailored to biomolecular design. Specifically, I focus on building models that integrate sequence, structure, and functional data to accelerate the rational design of functional biomolecules. This approach enables more efficient exploration of biomolecular space and opens new opportunities in drug discovery, synthetic biology, and fundamental biological research. My work has led to advances in protein design and the development of robust foundation models for nucleic acids, demonstrating the potential of AI-assisted generative models to revolutionize biomolecular engineering.