About the Author

Summary
  • A motivated professional with many years of statistical analysis and effectively interpreting findings and generating valuable insights.
  • Strong experience and knowledge in data science projects including Machine Learning with kills in Python, R, SAS, MS SQL Server, MySQL, NoSQL (MongoDB), AzureML, Power BI, Tableau, HTML5, CSS, and Big Data analytics using MS R Client, Azure Spark, HDInsight.
  • Other expertise includes RNA-seq analysis, molecular genetics, genomics and bioinformatics.

Education
Social/meetup groups
  • SQLSaturday: Indianapolis, IN
  • IndyPy: Indianapolis, IN
  • IndyUseR Group: Indianapolis, IN
  • Indy Azure User Group: Indianapolis, IN
  • Power BI User Group: Indianapolis, IN
  • Indy Big Data: Carmel, IN

Recent data science projects
  • MPP machine learning projects: (1) predicting using regression model on customer budget spending, (2) classifying item purchases with an accuracy of 85% - 100% with different algorithms.
  • Microsoft Professional Capstone - Data Science: successfully accomplished an MPP Capstone Challenge for the machine learning project hosted by DrivenData in predicting the gross rents, nationalwide. Algorithms applied include (1) Linear Regression, (2) Random Forest, (3) AdaBoost, (4) Decision Tree. The R-squares of predictions from different data sets range from 0.8326 ~ 0.9697 and the RMSE from 0.0978 ~ 0.0421, respectively.
  • MPP Capstone Challenge II: successfully predicted the mortgage rate spreads across 50 states from a data set with 21 independent variables using the following algorithms: (1) Linear Regression, (2) Random Forest and (3) XGBoost.
  • Kaggle machine learning project: classifying the forest cover types using Python with an accuracy of 88.04% - 100% using the algorithms of (1) Logistic Regression, (2) Support Vector Machine (SVM), (3) Random Forest, (4) XGBClassifier.
  • Clinic data analysis: accomplished sucessfully a statistical analysis using R for a set of clinic datasets collected from several thousand participants from a private research organization and generated valuable conclusions and insights.
  • Relational database management project: executed successfully a RDBMS project from a private company using MS SQL server.

The early experience in programming for his Master’s degree

While pursuing his graduate studies for a Master’s Degree in Statistical and Quantitative Genetics at Nanjing Agricultural University, the author had the opportunity to take the programming courses, i.e. Fortran (formerly FORTRAN from “FORmula TRANslation”) and BASIC (“Beginner's All-purpose Symbolic Instruction Code”). Soon, he created genetic algorithm using the programming languages to develop quantitative genetic models for data analysis and prediction of various genetic parameters for the quantitatively inherited traits of soybean for his M.S. thesis project.

By the way, Fortran is a general-purpose programming language which is well suited to numeric computation & scientific computing. It was developed in the early 1950s by the team led by John W. Backus at IBM and later released in 1957. However, it's a compiled language compared with other interpreted languages such as Python and JavaScript. BASIC is a general-purpose and high-level programming language which was designed by John G. Kemeny and Thomas E. Kurtz at Dartmouth College in New Hampshire, USA in 1964.


Computer simulation for his PhD project

One of his earlier experience was the computer simulation of the distributions of the phenotypic data which were influenced by both genetic and environmental factors as part of the research proposal for his PhD project at the University of Saskatchewan. The phenotypic data, due to the genetic models and the modes of gene actions, depended predominantly on the number of genes as well as the environmental factors varied by the locations and years for the resistance to the common bunt disease of wheat using SAS and MINITAB packages. Referring to the genetic approach of Robert C. Elston, the initial intention was to explore the possibility of grouping or clustering the -derived progenies or families from the breeding populations to determine the number of genes controlling the disease resistance through the analysis of Mendelian inheritance, using simulated datasets based on the binomial distribution:


where p is the probability of common bunt disease for a wheat plant while q is the probability of being healthy for the plant (), m is the number of diseased plants while n is the total number of wheat plants evaluated for the specific genotype. The expected mean of the probability distribution is with a variance .

Looking back the simulation with the concept of machine learning (unsupervised learning) in mind, the objective of the simulation was basically to draw inferences for classifying genotypic groups from the datasets simulated with the genetic models under different assumptions through the cluster analyses such as k-means, mixture models and hierarchical clustering.

With his background and interest, he is inspired by the following quote from Dr. Robert C. Elston, a distinguished statistical geneticist and professor at Case Western Reserve University since 1995, who was formerly a professor and head (1979-1995) in the Department of Biometry and Genetics at Louisiana State University Medical Center and a professor (prior to 1979) in the Department of Biostatistics at University of North Carolina:

"Statistical genetics may go out of fashion, but there will always be a need for statisticians who can compute." -- Robert C. Elston (2015)
(Zheng et al. 2015. A Conversation with Robert C. Elston. Statistical Science 30(2) : 258–267. DOI: 10.1214/14-STS497.)


His previous roles with data sets

Dr. He has worked as the Alfalfa Toolbox(ABT)/Data curator at Noble Research Institute. Some of his responsibilities in the ABT project relate to the genomic, genetic and phenotypic datasets for their integration into the toolbox web portal to provide user-friendly access to the research community, for instance, to find the homologous/orthologous genes through sequence blasting against the reference genomes of the Cultivated Alfalfa at the Diploid Level (CADL) (Medicago sativa) and M. truncatula, the model legume species. The ABT aims to provide resources for molecular breeding with efficiency to increase genetic gain not only for the forage crop but for the beef industry, ultimately.

In addition, Dr. He has also worked for the CGIAR Generation Challenge Programme (GCP), currently the Integrated Breeding Platform (IBP), c/o CIMMYT, which was mainly funded by the Bill & Melinda Gates Foundation (BMGF). Then, he managed numerous genotyping projects to assist many research programs in generating genotypic data for molecular marker assisted breeding of crop species to increase genetic gain in the developing countries across different continents. For instance, Dr. He, together with his colleagues, coordinated the development of many sets of SNP markers and advocated their applications to target important genes in the 11 key crop species, such as rice (Oryza spp.) and common bean (Phaseolus vulgaris). All sets of the KASP SNP markers were carefully selected including those from the important gene sequences of interest and developed across the genome of the relevant crop species. Many of them are associated with the economically important traits such as yield, quality, disease resistance and stress tolerance etc. He worked closely with the plant scientists to generate genomic, genetic and phenotypic datasets and conduct molecular breeding, some of his efforts are demonstrated through his introduction in the program interview (including the Chinese version). Also, as he has continued introducing the KASP assay through his publication for generating genotypic data, many research programs have adopted the cost-effective KASP assay into their own research laboratories.


About the Website

This website aims to host information on data management, processing, analysis, visualization and machine learning (ML) in order to provide possible solutions to real world problems using statistics and computer programming.


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The contents on this site are for preliminary information purposes only based on data-related technologies such as data management, processing, analysis, visualization as well as machine learning.