Lobo B., Lin R., Brown D., Kim T., Panzer M. (2015) Predicting Pedestrian Injury Metrics Based on Vehicle Front-End Design. In: Hsu CH., Xia F., Liu X., Wang S. (eds) Internet of Vehicles - Safe and Intelligent Mobility. Lecture Notes in Computer Science, vol 9502. Springer, Cham

Google Schlar link: https://books.google.com./books?id=re4LCwAAQBAJ

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Users can interact with the bot by either manually requesting cat photos, or turning on the push notification so that the bot would send a random cat photo every day.

Check out the bot here!

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A simple website that helps users to pick a restaurant in the Bryan/College Station/Houston area: BCSH Foodie Guide

Other than the random picker, registered users can also favorite or add a note to a restaurant. They can also view how others are exploring new restaurant around the area.

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This is a group project for CSCE 606 Software Enginnering class (Fall 2017). Working in a team of 5, I was responsible for project management. I have also implemented the product feature, as well as handling escalated technical issues.

Yaphub is a fully functional web-service exchange platform that is specially designed to serve students and residents around the Bryan/College Station area. Visitors can browse available products or services for sale. Once registered, users may post product/service needs, place orders, favorite items, bidding, or sending private messages. More details could be found from the technical report here.

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This is a group project for CSCE 608 Database Systems (Fall 2017). Working with another teammate, I took care of the parser front-end through ANTLR. I also implemented the actual CREATE, INSERT and DELETE functionalities and throughout tested them to make sure the simulator worked as expected.

TinySQL is a light version of SQL. It mimics the actual DBMS (Database Management System) and is implemented in Java. More details could be found from the technical report here.

Source code on Github Enterprise

This is a group project for CSCE 670 Information Retrieval and Storage (Spring 2018). I gathered data through different sources, wrote crawler scripts to acquire latest data from Twitter API, performed feature engineering for the account model, and further fine tuned and picked the best model for account features. I also worked closely with the web designer as well as the deep learning master to ensure that the final product worked as expected.

TESLA (Twitter Spammer Learning) is able to tell if a user is considered spam or not based on account and text features. We were able to do online prediction with offline trained models. For more technical details, please refer to the about page as well as our GitHub Repository.

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This is a group project for CSCE 638 Natural Language Processing Foundation and Techniques (Fall 2018). First advocate of this project, I found the DeepMind Q&A Dataset and implemented a modified version of the NetSum algorithm. Modified NetSum is found to perform the best among all three algorithms. I was also responsible for project management.

ASTON (Automatic SummarizaTion fOr News) includes three algorithms: LexRank, LSA, and NetSum. LexRank and LSA are unsupervised models, whereas NetSum is a neural-network based model. Given a sample news article and a selected algorithm, an auto summary consisting of three sentences will be prooduced. All algorithms are essentially extractive methods. We plan to release a Python package so that people may use our algorithms for more exploration.

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