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.
Wright State University
3640 Colonel Glenn Hwy
Dayton, Ohio 45435
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
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
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.
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.