Lingdong Huang

Lingdong Huang was born in Shanghai in 1997. He is an artist, programmer, and creative technologist whose work centers on creative coding and software for the arts. Huang studied Computer Science and Art at Carnegie Mellon University, where he completed a Bachelor of Computer Science and Arts degree in 2019. He later joined the Future Sketches Group at the MIT Media Lab, where his graduate research focused on programming languages and frameworks for creative coding. 

Huang uses code as a creative medium. He builds systems that generate images, environments, tools, and machine based processes. His work examines how computational rules can produce painterly effects, calligraphic movement, landscape forms, and human gestures. He has also created new programming languages and creative coding tools for visual experimentation. In his robotic calligraphy projects, Huang modifies 3D printers and writes custom algorithms that replicate the landing, lifting, and changing direction of a traditional brush. Other works include machines for automated seal carving and generative drawing, as well as 3D printed objects such as rotating puzzle balls based on historical carvings. Huang typically designs the software, machines, and fabrication systems himself.

Exhibitions and presentations of Huang’s work have taken place across the United States and internationally. In 2018, his project {Shan, Shui}, an automated system that produces endless Chinese landscape paintings through procedural generation, was exhibited at the Carnegie Mellon University School of Art. In 2019 he presented Legend of Wrong Mountain: AI Generated Opera at the ISEA Symposium. His work and research have also been featured through presentations at the MIT Media Lab, New York University Tisch School of the Arts, and the Hong Kong University of Science and Technology. In 2021, Huang contributed to Tulsa Massacre, an interactive software project created by The New York Times that reconstructed the Greenwood neighborhood in Tulsa, Oklahoma, through 3D modeling, historical mapping, archival photographs, census analysis, and machine learning. The project later entered the Architecture and Design collection of the Museum of Modern Art.