Selected Publications

A novel complex-valued spatial model via kernel convolution and corresponding MCMC algorithm are developed for detecting fMRI brain activation at the voxel level.

We present a complex-valued EM variable selection algorithm that leads to fast detection at the voxel level in CV-fMRI slices and also consider full posterior inference via MCMC.The proposed approach leads to activation in the expected motor-related brain regions and produces fewer spurious results than other methods for CV-fMRI.
In JASA A&CS, 2018

Recent Publications

(2018). Bayesian Spatial Modeling via Kernel Convolutions on Complex-Valued fMRI Signals.

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(2018). A Bayesian Variable Selection Approach Yields Improved Brain Activation From Complex-Valued fMRI. In JASA A&CS.

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Recent & Upcoming Talks

Example Talk
Jun 1, 2030 1:00 PM
Bayesian Modeling of Complex-valued fMRI Signals for Brain Activation
Aug 1, 2017 2:00 PM

Recent Posts

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I have been taking many mathematics and statistics courses at IUB and UCSC. At IUB, I took several undergraduate level and advanced mathematics as well as (mainly frequentist) statistics courses. At UCSC, most of the coursework focus on Bayesian statistics. I am also a MOOC fan

Selected Coursework at IUB

  • Advanced Mathematics

    • M413/414 Introduction to Analysis (C. Livingston)
    • M441/442 Partial Differential Equations (P. Sternberg/N. Levenberg)
    • M471 Numerical Analysis (S. Wang)
    • M563/564 Measure Theoretic Probability Theory (R. Bradley)
  • Advanced Statistics

    • S710 Statistical Computing (M. Trosset)
    • S675 Statistical Learning (M. Trosset)
    • S730 Theory of Linear Models (C. Huang)

Selected Coursework at UCSC

  • Probability, Statistics and Computing Fundamentals

    • AMS205B Classical Inference (D. Draper)
    • AMS209 Scientific Computing (D. Lee)
    • AMS256 Linear Models (A. Rodriguez)
    • AMS263 Stochastic Processes (A. Kottas)
  • Bayesian Statistics

    • AMS206B Bayesian Inference (R. Prado)
    • AMS207 Bayesian Modeling (B. Sanso)
    • AMS221 Bayesian Decision Theory (B. Sanso)
    • AMS241 Bayesian Nonparametrics (A. Rodriguez)
    • AMS268 Advanced Bayesian Computation (R. Guhaniyogi)
  • Specific Topics

    • AMS216 Stochastic Differential Equations (T. Xifara)
    • AMS225 Multivariate Statistical Methods (J. Lee)
    • AMS223 Time Series Analysis (R. Prado)
    • AMS245 Spatial Statistics (B. Sanso)
    • AMS274 Generalized Linear Models (A. Kottas)

The webpage includes some of my coursework at UCSC. You are welcome to see my work but please do not copy or plagiarize them in any way of any form. If any course is currently offered, the links for that course will be removed. Feel free to contact me about my coursework!


  • CBMS: Regional Conference on Spatial Statistics, University of California, Santa Cruz, August 2017
  • 3rd Annual Summer Institute in Statistics for Big Data, Department of Biostatistics, University of Washington, July 2017
  • Workshop on Big Data in Brain Science, Data Science Initiative, University of California, Irvine, February 2017

Selected Coursework from MOOC

  • Coursera
    • Data Science Specializations offered by Johns Hopkins University (completed all courses except capstone project)
    • Mastering Software Development in R Specializations offered by Johns Hopkins University (completed all courses except capstone project)
    • Python Programming:
      • The Fundamentals (Python), University of Toronto
      • An Introduction to Interactive Programming in Python (Part 1), Rice University
      • Using Python to Access Web Data, University of Michigan

I also visit Udemy, Udacity, Edx and other online learning sites.


Teaching at IUB

Grading and holding office hours

  • S420/620 Introduction to Statistical Theory (Spring 2013, B. Luen)
  • S432/632 Applied Linear Models II (Spring 2013, C. Huang)
  • S431/631 Applied Linear Models I (Fall 2012, C. Huang)
  • S426/626 Bayesian Theory and Data Analysis (Fall 2012, G. Huerta, now at Univeristy of New Mexico).

Lab sections

  • S501 Statistical methods (Fall 2011, W. Wyatt)
  • R workshop (Fall 2012, T. Jackson)

Teaching at UCSC

Grading, office hours and discussion sections

  • AMS7 Statistical Methods for the Biological, Environmental, and Health Sciences (Spring 2016, T. Xifara; Summer 2015, B. Mendes; Winter 2015, R. Prado)
  • AMS131 Introduction to Probability Theory (Summer 2016, D. Draper; Spring 2014, R. Morris)
  • AMS132 Classical and Bayesian Inference (Winter 2018, C.A. Wehrhahn Cortes)
  • AMS203 Introduction to Probability Theory (Fall 2015, R. Prado, Fall 2017, J. Lee)
  • AMS207 Intermediate Bayesian Modeling (Spring 2017, R. Prado)

Lab instructor

  • AMS 7L Statistical Methods for the Biological, Environmental, and Health Sciences Laboratory (Fall 2016, Summer 2017)


  • Room 2088, George R. Brown School of Engineering, Rice University, 6100 Main St., Houston, TX 77005 USA
  • Email for appointment