Hey, I'm

Xiantian Zhou


About me

I received my master degree in Computer Science at 2019 from the University of Houston, then, I continue my research as a Ph.D student at the Department of Computer Science, University of Houston. My master's thesis focused on analyze graphs on DBMS. My Ph.D research interests are Big Data, Graph Analysis. My Research Supervisor is Dr. Carlos Ordonez



Xiantian Zhou
Computer Science, University of Houston

Data Science Systems Research Group

Teaching Assistant

Fall 2020

COSC 3380: Design of File and Database Systems

Instructor: Dr. Carlos Ordonez

Spring 2020

COSC 4315/6345: Programming Languages

Instructor: Dr. Carlos Ordonez

Fall 2019

COSC 4315/6345: Programming Languages

Instructor: Dr. Carlos Ordonez



Paper 2021

Zhou, X. and Ordonez, C., 2021. Efficient Graph Analytics in Python for Large-scale Data Science. The 23rd International Conference on Big Data Analytics and Knowledge Discovery.

Paper 2019

Zhou, X. and Ordonez, C., 2019, December. Computing Complex Graph Properties with SQL Queries. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 4808-4816). IEEE.

Paper 2020

Li, H., Wang, H., Wang, L. and Zhou, X., 2020. A modified Boltzmann annealing differential evolution algorithm for inversion of directional resistivity logging-while-drilling measurements. Journal of Petroleum Science and Engineering, 188, p.106916.

Paper 2020

Ordonez, C., Al Amin, S., and Zhou, X., 2020. A Simple Low Cost Parallel Architecture for Big Data Analytics. IEEE International Conference on Big Data (Big Data),2020

Paper 2020

Zhou, X. and Ordonez, C., Matrix Multiplication with SQL Queries for Graph Analytics(poster). IEEE International Conference on Big Data (Big Data), 2020


Big graph analysis

08/2019 – 06/2022

  • Developing and optimizing parallel algorithms for analyzing big graph properties and big graph metrics (C++, Python)

  • Big data analysis

    06/2018 – 06/2019

  • Developing and Optimizing algorithms for Betweenness centrality, Group Betweenness centrality for large graphs using parallel DBMS
  • Optimizations algorithms of All Pairs Shortest Paths on parallel DBMS
  • Compare different algorithms in different platforms(in Spark, Vertica) of centrality detection to find the better solution

  • Developed a database management system (DBMS) in C++ (C++, SQL)

  • Designed a DBMS using binary file for evaluating SPJA (Select, Group by, Where, Join) queries on large table sets (cannot fit in main memory)
  • • Implemented external in-file merge sort for large binary files in order to efficiently manage larger amounts of data

  • Optimization of Linear Recursive Queries in SQL and maintaining Transitive Closure of graphs (Spark, Vertica, java, Scala, SQL)

  • Calculated the transitive closure of graphs using depth-first-search and optimize it to reduce time cost
  • Calculated and optimized the transitive closure in Spark and compared the two data analytics platforms to find the better one (Vertica and Spark)
  • Optimized the maintenance of transitive closure when inserting edges and deleting edges

  • Developed a user study in immersive virtual environment using Vizard (Python, java)

  • Created the process and environment of user study in virtual environment so that the results of user study would reflect the fact
  • Built models in Maya to simulate human’s dynamic body-movement and then exported avatar into Vizard to make the movement more realistic, implemented Microsoft Speech Platform in Vizard
  • Education

    University of Houston

    08/2019 - 05/2022 (Expected)

    Ph.D. in Computer Scienct

    Advanced Machine Learning, Operating System etc.

    University of Houston

    08/2017 - 05/2019

    M.S. in Computer Scienct

    Big Data Analysis, Machine Learning, Data Structures and Algorithms, Databases, Parallel Computing, Software Design, The theory of computation etc.



    • C++
    • SQL
    • Temsor Flow
    • Spark
    • OpenMP
    • Scala
    • Maya
    • Java
    • Python
    • MPI
    • Vertica
    • Vizard
    • CUDA
    • C


    Let's connect! I prefer LinkedIn message and Email.