Ching Jin
Also Known as, Qing Jin
Assistant Professor
Centre for Interdisciplinary Methodologies
University of Warwick
About me
I am a computational social scientist, who specializes in developing novel computational methodologies by leveraging tools from network science, statistical physics, and artificial intelligence. My work involves applying these methods to large-scale datasets across various domains, including technology, science, and commerce, with the overarching goal of describing, modeling, and predicting social patterns.
My research has resulted in five academic journal publications, including first-authored papers in Nature Human Behaviour (featured on the cover) and Nature Communications. Additionally, my work has been covered in media outlets such as Harvard Business Review and Science Daily.
I am currently an Assistant Professor in Data Science at CIM (Centre for Interdisciplinary Methodologies) at the University of Warwick. I earned my PhD in Physics from Northeastern University, under the supervision of Prof. Dashun Wang and Professor Albert-László Barabási. Following my doctoral studies, I served as a postdoctoral researcher at the Kellogg School of Management and held the role of Associate Director at the Northwestern Institute on Complex Systems (NICO), both at Northwestern University, under the guidance of Professor Brian Uzzi.
Research and Projects
My Research and Projects :
My main research interest lies in understanding complex diffusions in social systems. I bring methodologies from statistical physics, complexity science, network science, and artificial intelligence to provide new directions in innovation studies. Specifically, I develop novel, rigorous mathematical frameworks, network models, and causal inference techniques to explore large-scale datasets in different domains, from technology and science to commerce and medicine, offering new quantitative insights into technology forecasting, sustainable growth, knowledge diffusion, and scientific policy making. These methodologies and approaches also enable me to explore several related questions, such as substitution chains, information diffusion and diversity, equity, and inclusion in social systems, and to predict, sustain, and navigate growth in social systems. Here are a few research projects:
1.Universal Substitution Patterns
Diffusion processes are central to human interactions. One common prediction of the current modeling frameworks is that initial spreading dynamics follow exponential growth. Here we find that, for subjects ranging from mobile handsets to automobiles and from smartphone apps to scientific fields, early growth patterns follow a power law with non-integer exponents. We test the hypothesis that mechanisms specific to substitution dynamics may play a role, by analyzing unique data tracing 3.6 million individuals substituting different mobile handsets. We uncover three generic ingredients governing substitutions, allowing us to develop a minimal substitution model, which not only explains the power-law growth but also collapses diverse growth trajectories of individual constituents into a single curve. These results offer a mechanistic understanding of power-law early growth patterns emerging from various domains and demonstrate that substitution dynamics are governed by robust self-organizing principles that go beyond the particulars of individual systems.
Media
2. Abandonment of Innovation and Emergence of Fragility in Robust Ecosystems
We examine the social networks underneath the two systems through co-authorships and mobile communication records, finding that a preferential abandonment mechanism at a network level is responsible for generating the observed effect. Most importantly, we show analytically that the presence of preferential abandonment induces a structural collapse in the topology of the system, where networked systems that were thought to be robust undergo a novel phase transition. We test the theoretical predictions systematically in our datasets, obtaining broadly consistent empirical support. Together these results demonstrate that the collapse of real systems follows reproducible but fundamentally different dynamics than what traditional theoretical frameworks predicted.
Our findings suggest that preferential abandonment and the structural collapse it induces may be a generic property that prevails in the declining phase of the innovation lifecycle.
3. Scientific Prizes and Scientific Growth
Rather, growth is positively related to the degree to which the prize is discipline-specific, conferred for recent research, or has prize money. These findings reveal new dynamics behind scientific innovation and investment.
Media
4. Scientific Prizes and Knowledge Diffusion
This study thus uncovers the pivotal role of scientific accolades in shaping the trajectory of ideas and their interconnectivity, charting the course of scientific evolution and the burgeoning spread of knowledge across the global scientific community.