Bo Ning

About Me

Bo Ning is a fifth year Ph.D. candidate in Deparment of Statistics at North Carolina State University. He is advised under Dr. Peter Bloomfield and Dr. Subhashis Ghoshal. He has his research insterests in Bayesian methodoglogies and theoretical studies on dynamic time series models, high dimensional model and nonparametric models. He also interested in applications on causal inference and Astrostatistics.

Besides his advisors, he also collabates with other researchers including a Statistican: Dr. Sujit Ghosh; an Astronomer: Dr. Angie Wolfgang; the Product analysis team in Maxpoint Interactive Inc.

He is currently looking for a PostDoc position. CV

Research

Papers submitted/ in preparation

  1. Bo Ning and Subhashis Ghosal, 2017, "Bayesian multivariate linear regression with correlated errors under group sparsity." In preparation.
  2. Bo Ning, Angie Wolfgang and Sujit Ghosh, 2017, "Nonparametric prediction and the exoplanet mass-radius relationship." In preparation. [Results preview]
  3. Bo Ning, Subhashis Ghosal and Jewell Thomas, 2017, "Bayesian method for causal inference in spatially-correlated multivariate time series." Under revision. [R code]
  4. Bo Ning and Peter Bloomfield, 2017, “Bayesian inference for generalized extreme value distribution with Gaussian copula dependence.” Working paper, Arxiv:1703:00968.

Here I list of some future projects have not fully planned out yet:

  1. Predicting exoplanets' Mass-Radius relation using all the mass and radius Kepler measurements.
  2. The Kepler mission has detected tons of exoplanets with only few of them have both of the mass and radius measurements. Modeling their mass-radius relationships is important for understanding their components. My previous study only selected the exoplanets have both mass and radius measurements to explore their mass-radius relationships. In the next project, I am trying to build a Bayesian model to include those exoplantes only have one of the mass and radius measurements to model the relationships.

  3. Bayesian deconvolution on multivariate nonparameteric models.
  4. Although deconvolution problems have been intensively studied in many Frequentist literatures, the results in the Bayesian side are limited. Due to astronomy data often contain measurement errors and Bayesian models are widely used in the Astronomy society, it is important to understand the Frequentist properties of those models.

Academic activities

    Invited talks

  1. 3rd workshop on extreme precise radius velocities (EPRV), Penn State University, August 14-17, 2017
  2. "ASTRO Transition Workshop", SAMSI, NC, May 8-10, 2017.
  3. Maxpoint research day, Maxpoint Interactive Inc., Morrisville, NC, March, 2016
  4. Presentations and poster

  5. O'Bayes Meeting, Austin, Dec 10-13, 2017
  6. Joint Statistical Meeting, Baltimore, August, 2017
  7. International conference on advances in interdisciplinary statistics and combinatorics, Greensboro, NC, Sep 30-Oct 2, 2016
  8. Joint Statistical Meeting, Chicago, August, 2016
  9. ICSA Applied Statistics Symposium, Jun 12-15, Atlanta, 2016
  10. Graduate Student Research Symposium, the Graduate School, NC State University, March, 2016
  11. Workshops

  12. "ASTRO: Astrophysical Population Emulation and Uncertainty Quantification: April 3-7, 2017", SAMSI, NC, April 3-7, 2017
  13. "ASTRO: Hierarchical Bayesian modeling of exoplanet populations" by invitation only, SAMSI, NC, Oct 17-28, 2016
  14. "Statistical, Mathematical and Computational Methods for Astronomy (ASTRO)", SAMSI, NC, Aug 22-26, 2016

Teaching

Courses

Teaching portfolio

Trainings and awards

  • O'Bayes meeting travel award, O'Bayes 2017 Meeting, Austin, Dec 10-13, 2017.
  • Certificate of accomplishment in teaching (CoAT), the Graduate School, NCSU, 2016
  • Outstanding teaching assistant award for excellence in mentorship, University Graduate Student Association, NCSU, 2015
  • Outstanding teaching assistant award, Department of Statistics, NCSU, 2015
  • Graduate student summer teaching institute, NCSU, June 9-11, 2014

Contact

209 BOM
Department of Statistics
NC State University
Raleigh, NC 27695
bning@ncsu.edu