Rui Wu

(252) 328-9682 · wur18 at

I am now an assistant professor at the East Carolina University. My research interests are data mining, data visualization, and interdisciplinary research (such as fire simulation and environmental model improvements).

I received my Ph.D. and Master degree from University of Nevada, Reno and my major is Computer Science and Engineering. My advisors are Dr. Sergiu Dascalu and Dr. Frederick C Harris, Jr. I received my bachelor degree from Jilin University in 2013, majoring in Computer Science.

RA positions are available. If you are interested in research and want to be my research assistant, Please email me your CV, transcripts, TOEFL, and GRE scores, and everything else that you believe will help your application.


National Science Foundation, IUSE/PFE:RED: PPSE – From Programmers to Professional Software Engineers: Revolutionary Curricular Role: Senior Personnel

The project aims to achieve professional formation of software engineers through a non-course-centric innovative curriculum, inclusive pedagogy, reusable and personalizable teaching and learning content.

07/01/2017 – 06/30/2022

National Science Foundation, IUSE: EHR: Assessing Virtual Reality Field Experiences for Enhanced Learning in the Geosciences Role: Senior Personnel

The project has two core goals. First, we will evaluate the learning gains and implementation challenges afforded by different VR modalities. Second, we will develop and pilot a model for disseminating VR content and best practices in geoscience education.

07/01/2018 – 06/30/2021

North Carolina Sea Grant, Mitigating the effects of storm water flooding in coastal regions using machine learning techniques Role: Co-PI

The goal of the proposed project is to make coastal communities more resilient by providing coastal water managers with the tools for addressing coastal storm water flooding. The research questions that will be addressed in this project are: “when would water managers need to turn on groundwater pumps to start lowering the water table based on current groundwater levels, forecasted rainfall amounts, and other variables?” and “how effective is this approach in mitigating storm water flooding in coastal regions?”

02/01/2020 to 01/31/2022


Built a framework to improve the accuracy of hydrological models; using machine learning techniques to improve model accuracy based on the correlation relation between model inputs and model errors in Apache Spark. Techniques & Tools: Python, R, Flask and Apache Spark/PySpark.

Created a GPU fire simulation service using the server-client architecture. Set up a flask server on top of a GPU fire simulation library. The user (client side) can run different scenarios and visualize the outputs. Techniques & Tools: C++, CUDA, Flask, Bootstrap, and JavaScript/JQuery.


Research Assistant at the Cyber Infrastructure Lab (CIL). Engaged in research on big data and software engineering. Working on the NSF-funded research project The Solar Energy-Water-Environment Nexus in Nevada. Techniques & Tools: Python, R, MongoDB, JavaScript/JQuery.


Participated in NSF-funded Collaborative Research: The Western Consortium for Watershed Analysis, Visualization, and Experiments (WC-WAVE). Programmed web-based visualization and data processing section for the WC-WAVE website using Docker, Flask, and JQuery. Designed a new workflow that enables users to interact and visualize big data. Website is available Here. Techniques & Tools: Python, Flask and Docker/Rancher.


Created a system with colleagues to assist users in focusing on their computers. The system issues warnings if it detects that users do not look at the screen or fall asleep. Haar Cascades classifier was used for checking if the user’s eyes are closed or not (drowsiness). Created own algorithm to detect if users look outside of the screen. Techniques & Tools: C++ and Python.

Made improvement to the GFC, which is a floating-point data lossless compression algorithm, with multiple GPUs. The compression speed is more than 1,000 gigabits/s, which is much faster than the original GFC algorithm (75 gigabits/s). Techniques & Tools: C++, CUDA, Flask, Bootstrap, and JavaScript/JQuery.


Member of the research team led by Dr. Meng Zhang, a professor at Jilin University. The research program was funded by China National Science Foundation for anti-intrusion on multiple-core processors. Under Dr. Zhang’s guidance, my research focus was multiple string matching. Based on my research findings, I co-authored and published a peer-reviewed paper on a research journal (please see details in the Publication section). Techniques & Tools: C++ and CUDA.



East Carolina University

2018 Courses
CSCI 2405 - Discrete Structures II

East Carolina University

2019 Courses
CSCI 4710/6710 - Web Application

Research Interests

Data Processing
Data Visualization
Interdisciplinary Research



Conferences (Full Paper Refereed)