- About
- Courses
- Research
- Computational Social Science
- Critical Data Studies
- Data Science
- Economics and Information
- Education Technology
- Ethics, Law and Policy
- Human-Computer Interaction
- Human-Robot Interaction
- Incentives and Computation
- Infrastructure Studies
- Interface Design and Ubiquitous Computing
- Natural Language Processing
- Network Science
- Social Computing and Computer-supported Cooperative Work
- Technology and Equity
- People
- Career
- Undergraduate
- Info Sci Majors
- BA - Information Science (College of Arts & Sciences)
- BS - Information Science (CALS)
- BS - Information Science, Systems, and Technology
- MPS Early Credit Option
- Independent Research
- CPT Procedures
- Student Associations
- Undergraduate Minor in Info Sci
- Our Students and Alumni
- Graduation Info
- Contact Us
- Info Sci Majors
- Masters
- PHD
- Prospective PhD Students
- Admissions
- Degree Requirements and Curriculum
- Grad Student Orgs
- For Current PhDs
- Diversity and Inclusion
- Our Students and Alumni
- Graduation Info
- Program Contacts and Student Advising
Yian Yin is a PhD candidate of Industrial Engineering & Management Sciences at Northwestern University, with research affiliations at the Northwestern Institute on Complex Systems and Center for Science of Science and Innovation. His research embraces a combination of interdisciplinary tools to understand how successful innovations emerge from failures, how creative careers unfold with social and environmental changes, and how successful ideas in science interact with other socioeconomic institutions. Yian’s research has been published in multidisciplinary journals including Nature, Science, Nature Human Behaviour, and Nature Reviews Physics and featured in a wide range of media outlets including Forbes, Scientific American, and MIT Technology Review. He was also named as Spotlight Rising Star in Data Science by UChicago Data Science Institute.
Talk: Quantifying Success and Failure Dynamics in Science and Innovation
Watch this talk via Zoom // passcode: 357582
Abstract: The recent data explosion in science and technology has offered an unprecedented opportunity to capture the entirety of the scientific enterprise at a new level of scale and detail. In this talk, I will focus on a few related but distinct studies to help us better understand the complex dynamics of success and failure across a wide variety of settings. In each study, I will begin by drawing together a large variety of empirical datasets across different domains of science and technology. I then combine these data with modeling frameworks to quantify how successful innovations emerge from past repeated failures, how a community of innovators collectively contribute to punctuated record-breaking dynamics, and how scientific research interacts with other socioeconomic institutions. Through these examples, I hope to illustrate how a combination of canonical social science theories, large-scale empirical datasets, and complex systems tools can help us better understand the fundamental dynamics, predictability, and uncertainty of human achievements.