MIS Assistant Professor
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
- Business intelligence
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 Faculty 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