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Yan Leng

Assistant Professor

Department:     Information, Risk & Operations Management

Yan Leng

Yan Leng is an assistant professor of information, risk, and operations management in the McCombs School of Business at The University of Texas at Austin. She has taught Introduction to Problem Solving and Programming, for which she earned a 2021 McCombs Teaching Excellence Award.

Leng is also an award-winning computational social scientist and network scientist who uses large-scale data sets, network theory, and machine learning techniques to understand human behavior over social networks. Her research primarily focuses on the intricate network structures underlying human decisions or organizational performance across networks of different natures—such as social networks, data networks, and business market structures. On the one hand, she develops network-based machine learning, deep learning, and active learning techniques, informed by economic theory, social science theory, and game theory, to help companies make data-driven decisions. On the other hand, she studies the intricate spillover effects among humans or organizations using fine-grained human behavioral data, such as phone communications, online reviews, and mobility traces. For much of this work, she collaborates with mobile carriers, cross-channel retailers, and marketing research companies.

A member of the UT Machine Learning Laboratory, Leng works with the UT AI and Misinformation Initiative and UT Good Systems. She is also affiliated with the Massachusetts Institute of Technology Media Lab. Her research on learning network structures was awarded the NSF CISE CRII Award and was nominated as the finalist for Meta’s People’s Expectations & Experiences with Digital Privacy in 2022. Her work on data networks has also been awarded the INFORMS ISS Cluster Best Paper in 2022 and was a runner up for INFORMS RMP Data-Driven Research Challenge in 2021. In addition to NSF, her research is funded by Marketing Science Institute, Russel Sage Foundation, UT Good Systems, and McCombs Research Excellence Grant.

Leng joined the IROM faculty in 2020 from MIT, where she earned a Ph.D. working at the Human Dynamics Group at the Media Lab. She holds two master’s degrees from MIT, in computer science and transportation engineering, and dual bachelor's degrees in transportation engineering and computational mathematics from Beijing Jiaotong University in China.



Texas Global Faculty Research Seed Grant recipient


National Science Foundation CRII Award


Marketing Science Institute Grant


RMP Data-Driven Research Challenge, Second place

Yan Leng, and Drew Dimmery.
Calibration of Heterogeneous Treatment Effects in Randomized Experiments.
Information Systems Research. 

Chenbo Fu, Yinan Xia, Xinchen Yue, Shanqing Yu, Yong Min, Qingpeng Zhang, and Yan Leng. A Novel Spatiotemporal Behavior-Enabled Random Walk Strategy on Online Social Platforms. IEEE Transactions on Computational Social Systems, forthcoming.


Yan Leng, Sharon Strover, and Ying Ding. Misinformation during the COVID-19 Outbreak in China: Cultural, Social, and Political Implications. IEEE Transactions on Big Data, forthcoming.


Yan Leng, Tara Sowrirajan, Yujia Zhai, and Alex Pentland. Interpretable Stochastic Block Influence Model: Measuring Social Influence Among Homophilous Communities. IEEE Transactions on Knowledge and Data Engineering. Forthcoming.


Yan Leng, Xiaowen Dong, Esteban Moro, Alex Pentland. Long-range Social Influence in Phone Communication Network on Costly Offline Adoption BehaviorInformation Systems Research.  Forthcoming.

Learning to Infer Structure of Network Games. By: Emanuele Rossi, Federico Monti, Yan Leng, Michael Bronstein, Xiaowen Dong. International Conference on Machine Learning.  Forthcoming.

Liu, Meijun; Bu, Yi; Chen, Chongyan; Xu, Jian; Li, Daifeng; Leng, Yan; et al. 2022. Pandemics are Catalysts of Scientific Novelty: Evidence from COVID‐19. Journal of the Association for Information Science & Technology, 73(8): 1065-1078.

Yan Leng, Jinhua Zao , and Haris N. Koutsopoulos. Leveraging Individual and Collective Regularity to Profile and Segment User Locations from Mobile Phone Data. ACM Transactions on Management Information Systems.