RESEARCH
Haoling’s current research centers on the modeling of biological sequences, with particular emphasis on defining and characterizing functional boundaries at both the coding sequence level and the genomic scale. He is also deeply engaged in developing noise-resilient machine learning models tailored to high-noise biological environments, enabling robust knowledge inference in complex living systems. In parallel, he works on synthetic biology and bioinformatics, with a focus on synonymous codon optimization, missense variant effect and DNA-based data storage. Beyond technical contributions, he is committed to advancing scientific governance, including the development of evidence specifications for research software and disclosure protocols for hazardous biological sequences.

BIOGRAPHY
Prior to joining KAUST, Haoling earned his bachelor’s degree in Software Engineering from Chongqing University of Technology in 2018. He subsequently conducted specialized research in DNA-based data storage and bioinformatics at BGI Research (5 years), working within a joint laboratory with Professor George Church at Harvard Medical School. His work has led to publications in high-impact academic journals such as Nature Computational Science, Nature Communications, Briefings in Bioinformatics, and Bioinformatics. Haoling was also awarded the Warren L. DeLano Memorial PyMOL Open-Source Fellowship by Schrödinger, Inc., becoming the first recipient from Asia. In addition, he serves as an early-career-researcher reviewer for Springer Nature.