Junming Yin

Assistant Professor of Management Information Systems

McClelland Hall 430BB
1130 E. Helen St.
P.O. Box 210108
Tucson, Arizona 85721-0108

Areas of Expertise

  • Statistical machine learning
  • Probabilistic modeling and inference
  • Nonparametric and high-dimensional statistical inference


PhD in EECS, University of California, Berkeley

MA in Statistics, University of California, Berkeley

Additional Links

Before joining the Eller College of Management in 2015, Junming Yin was a Lane Fellow at Carnegie Mellon University. His areas of expertise include statistical machine learning, probabilistic modeling and inference and nonparametric and high-dimensional statistical inference. He earned his PhD in Computer Science from the University of California-Berkeley.


  • MIS 301 Data Structures and Algorithms
  • MIS 601 Statistical Foundations of Machine Learning

Refereed Journal Publications

  • W. Li, J. Yin, and H. Chen. Supervised topic modeling using hierarchical Dirichlet process-based inverse regression: Experiments on e-commerce applications. IEEE Transactions on Knowledge and Data Engineering, Vol. 30, Iss. 6, 1192-1205, 2018.
  • Q. Ho*, J. Yin* and E. P. Xing (*joint first authors). Latent space inference of Internet-scale networks. Journal of Machine Learning Research, 17(78):1−41, 2016.
  • M. Marchetti-Bowick, J. Yin, J. A. Howrylak, and E. P. Xing. A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits. Bioinformatics, Vol. 32, Iss. 19, 2903−2910, 2016.
  • L. Zhu, D. Guo, J. Yin, G. Ver Steeg, and A. Galstyan. Scalable temporal latent space inference for link prediction in dynamic social networks. IEEE Transactions on Knowledge and Data Engineering, Vol. 28, Iss. 10, 2765−2777, 2016.
  • J. Yin. Hypothesis testing of meiotic recombination rates from population genetic data. BMC Genetics, 15:122, 2014.
  • E. P. Xing, R. Curtis, G. Schoenherr, S. Lee, J. Yin, K. Puniyani, W. Wu, and P. Kinnaird. GWAS in a Box: statistical and visual analytics of structured associations via GenAMap. PLOS ONE, 2014.
  • J. Yin, M. I. Jordan, and Y. S. Song. Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data. Bioinformatics, Vol. 25, Iss. 12, i231-i239, 2009.
  • J. Yin, N. Beerenwinkel, J. Rahnenfuhrer, and T. Lengauer. Model selection for mixtures of mutagenetic trees. Statistical Applications in Genetics and Molecular Biology, Vol. 5, Iss. 1, Article 17, 2006.

Refereed Conference Proceedings

  • J. Yin and Y. Yu. Convex-constrained sparse additive modeling and its extensions. Uncertainty in Artificial Intelligence (UAI), 2017.
  • J. Yin, Q. Ho and E. P. Xing. A scalable approach to probabilistic latent space inference of large-scale networks. Advances in Neural Information Processing (NIPS), 2013.
  • Q. Ho, J. Yin and E. P. Xing. On triangular versus edge representations --- Towards scalable modeling of networks. Advances in Neural Information Processing (NIPS), 2012.
  • J. Yin, X. Chen and E. P. Xing. Group sparse additive models. International Conference on Machine Learning (ICML), 2012.
  • R. Curtis, J. Yin, P. Kinnaird and E. P. Xing. Finding genome-transcriptome-phenome association with structured association mapping and visualization in GenAMap. Pacific Symposium on Biocomputing (PSB), 2012.
  • J. Yin, M. I. Jordan, and Y. S. Song. Joint estimation of gene conversion rates and mean conver- sion tract lengths from population SNP data. Intelligent Systems for Molecular Biology (ISMB), 2009.

Awards and Honors

  • Amazon AWS Machine Learning Research Award (MLRA)
  • Best Paper Award, The 28th Annual Workshop on Information Technologies and Systems (WITS’18).
  • Adobe Digital Experience Research Award
  • Best Paper Award Runner-up, The 2017 INFORMS Workshop on Data Science.
  • Research, Discovery and Innovation (RDI) Faculty Seed Grant
  • Eller College Dean’s Research Award
  • Center for Management Innovations in Healthcare (CMIH) Research Award
  • Ray and Stephanie Lane Fellowship
  • Honors Degree of the International Max Planck Research School for Computer Science (IMPRS-CS)
  • Max Planck Society Fellowship