I’m a second year PhD student (supervised by Dr. Derek Doran) of Computer Science & Engineering 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. I possess industry experience in neural and kernel learning methods for image processing and sequence prediction. My academic research studies deep neural network mechanisms toward interpretability, via convolutional neural network(CNN) visualization and other techniques. 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 2018 summer internship, and interested in both research related and programming related positions.
Wright State University
3640 Colonel Glenn Hwy
Dayton, Ohio 45435
Ph.D. in Computer Science and Engineering • GPA 4.0 • Jan. 2016 - Dec. 2020(expected)
Courses Taken: Algorithm Design and Analysis, Network Science, Advanced Programming Languages, Machine Learning, Smart Cities:Devices & Methods, Distributed Computing, Computer Organization, OS Internals and Design
B.S. in Mathematics and Applied Mathematics • GPA 3.55 • Sep. 2010 - Jun. 2014
Graduate Research Assistant • Jan. 2016 - Present
Algorithm Engineer • Jun. 2014 - Dec. 2015
Framework & Library
Software & Tool
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. The work takes a semantics-first rather than visual- or text- first approach for developing explanations. This work is supported by the Ohio Federal Research Network.
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. This work is supported by the National Science Foundation under grant #1464104.
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.
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
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.
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.