Desheng Wang, PhD, is a mathematician and philosopher whose work bridges computational science, artificial intelligence, education innovation, and civilizational theory. He is best known for proposing SIO Ontology (Subject-Interaction-Object Ontology), a co-generative framework that replaces subject-object dualism with integral SIO wholes that emerge, stabilize, and transform through interaction. This orientation also grounds Wang's recent "destiny studies" program, developed in The Science of Destiny, where "destiny" is treated not as a mystical verdict but as an editable system: a feature-entanglement aggregate that can be diagnosed, measured, and rewritten through reproducible methods. Wang's scientific formation is rooted in advanced mathematical training and long-term research in computational mathematics and complex systems. He studied mathematics at Xiangtan University (BSc, 1990-1994; MSc, 1994-1997), pursued doctoral studies at the Chinese Academy of Sciences (1997-2001), completed postdoctoral research in computational mathematics (2001-2003), held research appointments in the United Kingdom (2003-2005), and later worked for over a decade at Nanyang Technological University in Singapore as a senior research scientist and doctoral supervisor, publishing widely on numerical methods, mesh generation, and geometry processing. A defining feature of Wang's trajectory is the integration of scientific formalism with philosophical generativity. In his view, modern science can achieve impeccable form while drifting toward "meaning anemia," and modern philosophy can preserve depth of experience while lacking operational structure. To bridge this split, he developed a Three-Law model of generativity-Creation Law, Freedom Law, and Happiness Law-describing how new structures emerge, how possibilities become viable paths, and how tension is transformed into sustainable driving force through release and stabilization.

In The Science of Destiny, these ideas converge into a practical framework for "rewriting destiny": shifting from explanation to generative engineering across reality, idea, and self domains, and extending SIO-based methods into education, psychology, organizational design, economics, governance, and human-AI collaboration.