Computational Social Science, Science of Science and Innovation, Complex System Modeling

My research examines how individual, social, and environmental processes independently and jointly promote (or inhibit) scientific progress and innovation achievements. In my dissertation, I have applied and developed a wide range of computational tools to help us better understand (1) how successful innovations emerge from repeated failures, (2) how scientific careers unfold with social and environmental changes, and (3) how successful ideas in science interact with broader socio-economic institutions. This research agenda is only possible because of the increasing availability of large-scale datasets and the increasingly sophisticated computational tools and capabilities. Together, they offer an unprecedented opportunity to understand the fundamental dynamics and uncertainties underlying the inner-workings of science, innovation, and other socio-economic institutions. By embracing approaches from artificial intelligence and complexity sciences to sociology and economics, results from my research program not only improve our ability to understand and facilitate scientific progress and breakthroughs, but also have implications for entrepreneurship and sustainable technological and business innovation.

As a computational social scientist, I have also used science and innovation as a powerful lens to examine broader processes and outcomes in human society. My research has yielded a range of generalizable insights that are relevant to a variety of complex social processes, from artistic and cultural productions to public policy, from media attention to market competition to human conflict.

Prospective students / postdocs

Thank you for your interest in working with me! Our group currently has a number of fundamental projects at the boundary of network science and computational social science, including science of science and innovation, social networks, human dynamics, and mathematical and/or computational methods in complex social systems.
  • PhD applicants
    We are seeking for motivated PhD student(s) who have strong interest in pursuing research careers in the areas of Network Science, Data Science, and Computational Social Science. If you are interested, apply to our PhD program in Cornell Information Science. Please feel free to email me (yy994 [at] your CV with subject heading [(your name) -- PhD] when you apply, so that I can make sure to review your application materials.

  • Cornell student interested in working with us?
    If you are an undergraduate/graduate student at Cornell and interested in working with us, please feel free to email me (yy994 [at] your CV with subject heading [(your name) -- Cornell undergraduate/graduate RA], and mention your training and experience in data science and network analysis. Given the complexity of our project, we expect you to commit to at least 6 months and 10 hours per week.

  • Postdoc opening
    We currently have one open position for Postdoctoral Fellow. We are currently focused on problems in science and innovation, but are also broadly interested in a variety of complex social systems, such as social networks, human dynamics, and cultural productions. Successful applicants are expected to have a strong background in one or more of the aforementioned areas. Excellent organizational and interpersonal skills, along with a stated interest in social and computational sciences, are essential. Hands-on experience with large-scale datasets and network modeling is a strong plus.

    The successful candidate must hold a PhD or expected PhD in Computational Social Science, Applied Mathematics, Physics, Computer Science, Economics, or a closely related field by the start of the position. Interested candidates must submit to Yian Yin (yy994 [at] with subject heading [(your name) -- Postdoc], containing: 1) cover letter, describing your interest in and qualifications for this position; 2) CV (including publications list); 3) the name and contact information of three references.

    Interested candidates are also strongly recommended to apply to the Research Assistant Professor at Cornell Center for Data Science for Enterprise & Society and mention me as your faculty advisor.

  • Visiting scholar Opportunities
    Positions for visiting scholars are available at a limited basis. If you are interested in visiting us, please email me (yy994 [at] 1) your CV; and 2) a brief introduction about your goal and research interest with subject heading [(your name) -- Visiting Scholar].

Research topics

  • The tipping point between failure and success
    Current research on science and innovation has mainly focused on data of published papers, granted patents, or released products, implying that existing insights are limited to ideas, individuals, or teams that have succeeded in the first place. Yet, most innovations fail, sometimes in a speculative manner. But our knowledge of why, how, and when failures lead to eventual victory (or defeat) remains limited. In a recent paper published in Nature, I develop a new empirical and theoretical basis to advance our quantitative understanding of failures. By analyzing three large-scale datasets of repeated attempts by NIH investigators, serial entrepreneurs, and terrorist organizations, this paper probes a simple yet fundamental question: how do we go from failure to success?

  • Production and recognition of successful ideas in individual careers
    The rapid growth of science and its increasing complexity have created unprecedented opportunities and challenges for understanding and managing the scientific enterprise. Understanding the key mechanisms underlying career lifecycles carries growing importance for identifying and nurturing talents across a diverse range of domains. My research in this direction has focused on understanding both general patterns governing individual careers and important change processes frequently occurring within a career.

  • Beyond science: A broader definition of impact
    The bulk of studies on science have focused within the scientific ecosystem. Yet we know relatively little about their impact beyond the ivory tower. Indeed, science has long been described as a social institution that interacts with many other aspects of human society, which affects our ability to confront some of today’s biggest challenges – from the pandemic to climate change, from fake news to privacy and security. Hence there is an urgent need to understand the role and impact of science outside science – in the halls of government, public perceptions, marketplace applications, and more.