Biography

Zihang Lin is an incoming PhD student in the Department of Industrial Engineering & Management Sciences of the McCormick School of Engineering at Northwestern University. He is affiliated with Kellogg Center for Science of Science & Innovation and Northwestern Institute on Complex Systems, under the supervision of Prof. Dashun Wang. He received his bachelor’s degree in computer science from Fudan University in June 2023. During 2021 and 2022, he was a Visiting Predoctoral Fellow at the Kellogg School of Management, Northwestern University.

His research interests include Computational Social Science, Science of Science, and Innovation. Currently, he is engaged in large-scale scientific data analyses with advanced machine learning techniques, trying to attain a deeper understanding of science and innovation to better devote to this society.

Download my Curriculum Vitae.

Interests
  • Computational Social Science
  • Science of Science
  • Innovation
Education
  • PhD student, 2023-now

    Department of Industial Engineering and Management Sciences, Northwestern University

  • BSc in Computer Science, 2023

    School of Computer Science, Fudan University

  • Visiting Predoctoral Fellow, 2021-2022

    Kellogg School of Management, Northwestern University

Experience

 
 
 
 
 
PhD student at Northwestern IEMS, Researcher in residence
Aug 2023 – Present Evanston, IL, USA | working with Prof. Dashun Wang
 
 
 
 
 
Intern
Mar 2023 – Jun 2023 Shanghai, China | working as a Machine Learning Engineer Intern
 
 
 
 
 
Visiting Predoctoral Fellow
Aug 2021 – Jul 2022 Evanston, IL, USA | working at Center for Science of Science & Innovation (CSSI) with Prof. Yian Yin and Prof. Dashun Wang
 
 
 
 
 
Research Assistant
Jun 2019 – Jun 2023 Shanghai, China | working at Mobile Systems and Networking (MSN) Group with Prof. Qingyuan Gong and Prof. Yang Chen

Research

Dual frontier: How prior scientific knowledge predicts tomorrow’s technological breakthroughs
As the time lag between granted patents and cited scientific papers has increased over the past few decades, we are currently trying to predict tomorrow’s patented inventions based on prior scientific knowledge, depicting the knowledge flow from previous scientific literature to technological innovation world.
This work is advised by Prof. Yian Yin and Prof. Dashun Wang.
Dual frontier: How prior scientific knowledge predicts tomorrow's technological breakthroughs

Publications & Working Papers

(2022). Structural Hole Theory in Social Network Analysis: A Review. IEEE Transactions on Computational Social Systems 9 (3), 724-739.

Preprint PDF Cite

Selected Scholarships & Awards