Yixin Fang

Contact Info

Title: Associate Professor
Email: yixin.fang@njit.edu
Office: 210 cullimore
Hours: Tuesday 2-4pm (Fall 2016)
Phone: 973-596-3281
Dept: math

Academic Interests: high-dimensional data analysis, data science, clinical trials


About Me

After I received Ph.D. in Statistics from Columbia University in 2006, I worked as Postdoctoral Research Scientist for one year in the Division of Behavioral Medicine at Columbia University. Before joining NJIT, I worked as Assistant Professor of Statistics in the Department of Mathematics and Statistics at Georgia State University from 2007-2011 and then as Assistant Professor of Biostatistics in the Department of Population Health at New York University School of Medicine from 2011-2016.


  • PhD, Statistics, Columbia University, 2006
  • MS, Statistics, University of Science and Technology of China, 2003
  • BS, Statistics, University of Science and Technology of China, 2000 

Research Interests

Yixin Fang's research focuses on the development of statistical methods for high-dimensional data analysis and data science, and the application of statistical methods to clinical trials, population health research, and industrial data.


Selected Publications

  • Fang Y, Loh JM (2016). Single-index model for inhomogeneous spatial point processes. Statistica Sinica, Accepted.
  • Denson JL, Jensen A, Saag HS, Wang B, Fang Y, Horwitz, LI, Evans L, Sherman SE (2016) Association Between End-of-Rotation Resident Transition in Care and Mortality Among Hospitalized Patients. Journal of the American Medical Association, 316: 2204-2213.
  • Braithwaite RS, Fang Y, Tate J, Mentor SM, Bryant KJ, Fiellin DA, Justice AC (2016). Do Alcohol Misuse, Smoking, and Depression Vary Concordantly or Sequentially? A Longitudinal Study of HIV-Infected and Matched Uninfected Veterans in Care. AIDS and Behavior, 20: 566-572.
  • Fang Y, Wang B, Feng Y (2015) Tuning parameter selection in regularized estimations of large covariance matrices. Journal of Statistical Computation and Simulation, 86: 494-509.
  • Fang Y, Lian H, Liang H, Ruppert D (2015). Variance function additive partial linear models. Electronic Journal of Statistics, 9: 2793-2827.
  • Fang Y, Feng Y, Yuan M (2014). Regularized Principal Components of Heritability. Computational Statistics 29: 455-565.
  • Persky M, Fang Y, Myssiorek D (2014). Relationship of the recurrent laryngeal nerve to the superior parathyroid gland during thyroidectomy. Journal of laryngology and otology 128: 368-371.
  • Xu T, Wang J, Fang Y (2014). A model-free estimation for the covariate-adjusted Youden index and its associated cut-point. Statistics in Medicine 33: 4963-4974.
  • Sun W, Wang J, Fang Y (2013). Consistent selection of tuning parameters via variable selection stability. Journal of Machine Learning Research 14: 3419-3440.
  • Lafer M, Achlatis S, Lazarus C, Fang Y, Branski R, Amin MR (2013). Temporal measurements of deglutination in dynamic magnetic resonance imaging versus videouoroscopy. Annals of Otology, Rhinology, and Laryngology 122: 748-753.
  • L'akoa R, Noubiap J, Fang Y, Ntone F, Kuaban C (2013). Prevalence and Correlates of Depressive Symptoms in HIV-Positive Patients: a Cross-Sectional Study among Newly Diagnosed Patients in Yaounde, Cameroon. BMC Psychiatry 13: 328-337.
  • Sun W, Wang J, Fang Y (2012). Regularized k-means clustering of high-dimensional data and its asymptotic consistency. Electronic Journal of Statistics 6: 148-167.
  • Wang Y, Huang C, Fang Y, Yang Q, Li R (2012). Flexible semiparametric analysis of longitudinal genetic studies by reduced rank smoothing. Journal of the Royal Statistical Society, Series C (Applied Statistics) 61: 1-24.
  • Fang Y, Wang J (2012). Selection of the number of clusters via the bootstrap. Computational Statistics and Data Analysis 56: 468-477.
  • Huang X, Qin G, Fang Y (2011). Optimal combinations of diagnostic tests based on AUC. Biometrics 67: 568-576.
  • Fang Y (2011). Asymptotic equivalence between cross-validations and Akaike information criteria in mixed-effects models. Journal of Data Science 9: 15-21.