About Me

I’m a CS PhD candidate (supervised by Dr. Derek Doran) at Wright State University, Dayton OH. My research interests are broadly in data science, with a focus on deep learning, computer vision, and web systems modeling. My academic research studies deep neural network mechanisms toward interpretability, that aid in interpreting how they process input data in a human-understandable way. This work includes implementation, training, visualization, and analysis of convolutional neural networks using Tensorflow. I have also applied Recurrent Neural Networks via Keras to reveal latent behavioral patterns in classes of automated traffic faced by web systems. Besides research, I'm also an amateur acoustic guitar player. Check my YouTube channel for some songs I played :)

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 - Dec. 2020(expected)

Courses Taken: Computer Vision, Algorithm Design and Analysis, Network Science, Advanced Programming Languages, Machine Learning, Smart Cities:Devices & Methods, Distributed Computing, Computer Organization, OS Internals and Design

Selected course projects:

  • Smart Cities, Devices & Methods: Augmented Convolutional Neural Network (AUG-CNN) for Autonomous Driving.
  • Network Science: Dynamic Social Network Analysis on Untrustworthy News Networks.
  • Machine Learning: Did you make the Wright Choice for your College? Using SVM to analyze college scorecard dataset public by the U.S. Department of Education.
  • Hebei University of Technology

    B.S. in Pure and Applied Mathematics 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

    Machine Learning Department, NEC Laboratories America, Inc.

    Summer Research Intern May. 2018 - Aug. 2018

  • Towards building a more transparent/explainable model on Visual Question Answering (VQA) problems. Work in progress.
  • WaCS lab, Kno.e.sis Center, Wright State University

    Graduate Research Assistant Jan. 2016 - Present

  • Paper reviewing for IJCAI 2018.
  • Currently working on Explainable Deep Learning. Most recent result is accepted at NIPS 2017 Workshop on Explainable Machine Learning. [pdf]
  • 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. [pdf]
  • Awarded two full 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 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

  • Xie, N., Sarker, M. K., Doran, D., Hitzler, P., & Raymer, M. (2017, Dec.). Relating Input Concepts to Convolutional Neural Network Decisions. NIPS 2017 Workshop: Interpreting, Explaining and Visualizing Deep Learning, NIPS IEVDL 2017.
  • Xie, N., Brown, K., Rude, N., & Doran, D. (2017, Jun.). A Soft Computing Prefetcher to Mitigate Cache Degradation by Web Robots. In International Symposium on Neural Networks (pp. 536-546). Springer, Cham.
  • Sarker, M. K., Xie, N., Doran, D., Raymer, M., & Hitzler, P. Explaining Trained Neural Networks with Semantic Web Technologies: First Steps. Twelfth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy (Vol. 17).
  • Skills

    Programming Language

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

    Framework & Library

    • PyTorch
    • Tensorflow
    • Keras
    • Caffe
    • OpenCV

    Software & Tool

    • PyCharm
    • Microsoft Visual Studio
    • Eclipse
    • Matlab
    • RStudio

    Operating System

    • Mac OS X
    • Linux
    • Windows

    Check Out Some of My Recent Projects.

    Explainable Deep Learning for Computer Vision

    When a person's future, health, finances, and safety is on the line, human decision makers relying on an AI system need to rationalize the recommendations the system offers. Yet present day AI is hampered by our ability to interpret or ''explain'' the rationale AI uses to arrive at a conclusion. This project investigates new approaches for reaching ''explanations'' of an AI's decision making process, specifically applied to deep learning, for computer vision tasks. A paper on this project has been accpeted by NIPS 2017 Workshop: Interpreting, Explaining and Visualizing Deep Learning, NIPS IEVDL 2017
    *Work supported by the Ohio Federal Research Network.

    Understanding the Impact of Web Robot and IoT Traffic on Web Systems

    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.
    *Work supported by the National Science Foundation under grant #1464104.

    Augmented Convolutional Neural Network for Autonomous Driving

    Autonomous driving is an active research topic these years in both academia and industry. In this work, I designed a novel end-to-end augmented convolutional neural network (AUG-CNN) to address this problem. Based on raw image pixels from the front-facing cameras equipped on the car and the car’s current status on speed and throttle, AUG-CNN autonomous driving system is able to directly make decisions to control the steering wheel and the throttle. The training dataset and testing simulator are released by Udacity Self-Driving Car Nanodegree Program. Although the AUG-CNN system is trained only on the daytime-plain-driving dataset, the performance is surprisingly good in the simulator of a nightfall-mountain-driving scenario that the model has never seen. This work is the final project for Smart Cities:Devices & Methods.

    Dynamic Social Network Analysis on Untrustworthy News Networks

    The spread of misinformation in media has become an increasingly relevant problem in society. However, automatically identifying whether a news story is an incorrect claim or factual from the content of a story is an extremely difficult machine learning problem that has yet to be solved by the research community. Our approach is to examine dynamic social networks on the discussion of news for patterns which can distinguish between disreputable claims and factual stories. Using a dynamic stochastic block model (DSBM), we demonstrate the ability to infer structural behavior such as the emergence of new connected components in social networks from temporal patterns. We also explore the potential and difficulties of using network based features to train a classifier over the truth or falseness of news. This work is the final project for Network Science

    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.