Junming Yin

I work at the intersection of statistics, machine learning, and business, focusing on the design of novel statistical models and computational algorithms for solving cutting-edge business problems. I received a Ph.D. in Computer Science and an M.A. in Statistics from UC Berkeley (advisors: Michael I. Jordan and Yun S. Song).

Research Interests:

Methodologies: statistical learning, probabilistic modeling and variational inference, nonparametric and high-dimensional statistics, deep generative models
Applications: business decision making, dynamic consumer behavior, matching markets, crowdsourcing, healthcare

Student Mentees:

Jerry Luo (Ph.D. in Math; now at Google), Xi Wang (CS; now Ph.D. student at UMass)

Email: junming.yin.work[at]gmail[dot]com

Selected Recent Papers

J. Yin, Y. Feng, and Y. Liu.
Modeling behavioral dynamics in digital content consumption: An attention-based neural point process approach with applications in video games.
Marketing Science, forthcoming.

Z. Wang, L. Guo, J. Yin, and S. Li.
Bandit Learning in Many-to-One Matching Markets.
ACM International Conference on Information and Knowledge Management (CIKM), 2022.

F. Kong, J. Yin, and S. Li.
Thompson sampling for bandit learning in matching markets.
International Joint Conference on Artificial Intelligence (IJCAI), 2022.

J. Yin, J. Luo, and S. A. Brown
Learning from crowdsourced multi-labeling: A variational Bayesian approach.
Information Systems Research, 32(3), 752–773, 2021.

X. Wang and J. Yin.
Relaxed multivariate Bernoulli distribution and its applications to deep generative models.
Uncertainty in Artificial Intelligence (UAI), 2020.