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Yuan Yuan

Yuan Yuan

Assistant Professor
Management Information Systems


Ph.D., Social & Engineering Systems & Statistics, MIT, 2021
B.Eng., Computer Science, Tsinghua University, 2016
B.A., Economics, Tsinghua University, 2016


Yuan Yuan is a computational social scientist and an assistant professor at the Krannert School of Management (MIS area) at Purdue University. His research focuses on (1) leveraging big data and advanced computational techniques (e.g., machine learning and causal inference) to study online social interactions and social networks; and (2) developing computational techniques that combine machine learning and causal inference, with applications to online field experiments (a.k.a A/B testing). Before coming to Purdue, he did my Ph.D. in the Institute for Data, Systems, and Society (IDSS) at Massachusetts Institute of Technology, and received his Bachelor's degrees in Computer Science and Economics from Tsinghua University.

Journal Articles

  • Yuan Yuan, Tracy Xiao Liu, Chenhao Tan, Qian Chen, Alex `Sandy' Pentland, and Jie Tang (2023). Gift contagion in online groups: Evidence from virtual red packets. accepted at Management Science,
  • Ding Lyu, Yuan Yuan, Lin Wang, Xiaofan Wang, Alex Pentland (2022). Investigating and modeling the dynamics of long ties. Communications Physics (Nature), | Related Website |
  • Yuan Yuan, Ahmad Alabdulkareem & Alex ‘Sandy’ Pentland (2018). An interpretable approach for social network formation among heterogeneous agents. Nature Communications, | Related Website | Download |

Conference Proceedings

  • Yongkang Guo, Yuan Yuan, Jinshan Zhang, Yuqing Kong, Zhihua Zhu, Zheng Cai (2023). Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms. Web Conference (WWW),
  • Yuan, Yuan, Kristen Altenburger, & Farshad Kooti (2021). Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests.. Web Conference (WWW), | Related Website | Download |

Office: KRAN 709

Quick links

Personal website

Area(s) of Expertise

social networks, causal inference, experimental design, machine learning