About Me

I’m a second year PhD student of Computer Science & Engineering at Wright State University, Dayton OH. My research interests are broadly in data science, with a focus on deep learning, data semantics, and web systems modeling. I possess industry experience in neural and kernel learning methods for image processing and sequence prediction. My academic research studies ways to bias interior DNN mechanisms toward inherently explainable representations, given semantic information about relationships among input features. I have also applied Recurrent Neural Networks to reveal latent behavioral patterns in classes of automated traffic faced by web systems. I’m now actively seeking for a 2017 summer internship, and interested in both research related and programming related positions.

Contact Details

Ning Xie
Wright State University
3640 Colonel Glenn Hwy
Dayton, Ohio 45435


Wright State University

Ph.D. in Computer Science and Engineering GPA 4.0 Jan. 2016 - May 2020(expected)

Courses Taken: Algorithm Design and Analysis, Network Science, Advanced Programming Languages, Machine Learning, Distributed Computing, Computer Organization, OS Internals and Design

Hebei University of Technology

B.S. in Mathematics and Applied Mathematics GPA 3.55 Sep. 2010 - Jun. 2014

Selected Awards:

  • National Second Prize of CUMCM (Contemporary Undergraduate Mathematics Contest Modeling) --- Oct. 2013
  • Learning Excellence Award in College of Science --- 2012-2013
  • First-class Scholarship of the Hebei University of Technology --- Nov. 2011
  • Learning model Award in College of Science --- Apr. 2011
  • Experience

    WaCS lab, Kno.e.sis Center, Wright State University

    Graduate Research Assistant Jan. 2016 - Present

  • Implementing deep learning systems (LSTM-RNN) to evaluate patterns in request sequence of web robot and IoT device sessions for request prediction and traffic generation. One paper on this project has been accepted by ISNN 2017. --- Jan. 2017
  • Awarded two scholarships to attend events for women in computing: Ohio Celebration of Women in Computing at Lake Huron in Ohio, February 2017, and the CRA-W Grad Cohort Workshop in Washington, DC, April 2017
  • Poster presented at 2016 Women in STEMM Leadership Institute Symposium, Dayton, OH. --- Oct. 2016
  • Awarded an NSF Student Fellowship to attend 12th Reasoning Web Summer School, Aberdeen, Scotland. --- Sep. 2016
  • Prepared and delivered a Deep Neural Network presentation for a Wright State Seminar. --- Jun. 2016
  • Tianjin Tiandy Digital Technology Co., Ltd

    Algorithm Engineer Jun. 2014 - Dec. 2015

  • Intelligent Court System --- Sep. 2015
    Used Deformable Parts Model (DPM) to detect people making phone calls in image data. Reduced false recognition rates by developing location and skin-color filters.
  • Floating Garbage Detection Project --- May 2015
    Modified Visual Background Extractor (VIBE) to significantly improve the performance on extracting the Regions of Interest (ROI) effectively in complex environment of rivers and streams (light changing, wave interfering, etc.) to identify debris in water. Implemented an Artificial Neural Network for garbage classification.
  • Intelligent Transportation System --- Oct. 2014
    Researched Pixel-Based Adaptive segmentation (PBAS) for image foreground segmentation, implementing this method for an intelligent transportation system project.
  • Publication

  • Ning Xie, Kyle Brown, Nathan Rude, and Derek Doran. A Soft Computing Prefetcher to Mitigate Cache Degradation by Web Robots. 14th International Symposium on Neural Networks (accepted)
  • Skills

    Programming Language

    • C/C++
    • Python
    • Matlab
    • R
    • Java

    Framework & Library

    • Keras
    • Tensorflow
    • Caffe
    • OpenCV

    Software & Tool

    • Microsoft Visual Studio
    • Eclipse
    • Matlab
    • RStudio

    Operating System

    • Linux
    • Windows
    • Mac OS X

    Check Out Some of My Recent Projects.

    A Soft Computing Prefetcher to Mitigate Cache Degradation by Web Robots

    The majority of requests seen by web servers and clouds are caused by web robots and IoT(Internet of things) agents. Prefetching web resources for caching and content management systems is a common technique to anticipate and pre-load the resources likely to be requested next for fast, low latency access. This project introduces a novel soft computing resource prefetcher, which marries a deep recurrent neural network(RNN) with a Bayesian network, to predict future requests made by web robots. Experiments with traffic logs from web servers across two universities demonstrate an improvement in precision and recall over a traditional dependency graph-based approach.

    Did you make the Wright Choice for your College?

    It is well known that U.S. university students often graduate with copious amounts of financial debt. The tuition rates for some colleges are often far above a reasonable threshold of being considered practical, which often limits many aspiring students' enrollment choices to educational institutions that seem more affordable. Even worse, students who do graduate often find themselves facing repayment obligations that far outweigh their potential income earnings. These notations collectively spark a number of questions.

  • Can we discover and explain groupings of colleges which exhibit similar characteristics?
  • Do higher tuitions colleges always provide students with higher average payment?
  • Can examining the relationship between features such as tuition costs and earnings after graduation help future students make more informed college decisions?
  • This is a final project for Machine Learning course, for more details please check the report here.

    Artistic Deep Nets

    This is just a demo project that I runned for my Deep Learning presentation in Wright State to show an application example of Deep Neural Network. The code is written by Justin Johnson and could be found online.