Junming Yin

Degrees

Ph.D. in EECS, University of California, Berkeley

M.A. in Statistics, University of California, Berkeley

Areas of Expertise

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

Courses

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 conversion tract lengths from population SNP data. Intelligent Systems for Molecular Biology (ISMB), 2009.

Awards and Honors

• Adobe Digital Experience Research Award
• Best Paper Award Runner-up at the INFORMS Workshop on Data Science
• Research, Discovery & 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