Ge Gao

Hello! This is Ge and I am a first-year PhD student in computer science at Boston University, advised by Dr. Margrit Betke and Dr. Derry Wijaya. My research is in the intersection of emerging media and artificial intelligence, with a goal to use machine learning tools to enhance the ways we access and process information in the newsworld. Alongside my research, I am a self-motivated software engineer with substantial industry experience in full-stack and mobile development, proficient in developing end-to-end solutions with Java, C++, JavaScript, and Python.

Outside of work, I enjoy skiing and cooking!

• Our paper "Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation" has been accepted at the LREC-COLING 2024 conference.

Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation
Ge Gao, Jongin Kim, Sejin Paik, Ekaterina Novozhilova, Yi Liu, Sarah Bonna, Margrit Betke, Derry Wijaya (2024)


The Affective Nature of AI-Generated News Images: Impact on Visual Journalism
Sejin Paik, Sarah Bonna, Ekaterina Novozhilova, Ge Gao, Jongin Kim, Derry Wijaya, Margrit Betke (2023)


Prediction of People’s Emotional Response towards Multi-modal News
Ge Gao, Sejin Paik, Carley Reardon, Yanling Zhao, Lei Guo, Prakash Ishwar, Margrit Betke, Derry Wijaya (2022)


NEmo: an Affective Dataset of Gun Violence News
Carley Reardon, Sejin Paik, Ge Gao, Meet Parekh, Yanling Zhao, Lei Guo, Margrit Betke, Derry Wijaya (2022)


Proposing an Open-Sourced Tool for Computational Framing Analysis of Multilingual Data
Lei Guo, Chao Su, Sejin Paik, Vibhu Bhatia, Akavoor, Vidya Prasad Akavoor, Ge Gao, Margrit Betke & Derry Wijaya (2022)

As a backend software engineer, I maintained and improved the backend sales platform with Spring Boot framework.

• Designed and implemented an internal tracking ID system that has gone alive with AWS lambda functions
• Optimized architure of microservice interactions and speed up the insurance quoting flow by 70-75%.

As a full stack software engineer, I built and optimized the TripAdvisor flights, cruises, and cars pages with Java microservices and React.

• Optimized page performance of TripAdvisor cars pages with up to 250% increase in lighthouse score.
• Designed and implemented a python tool that verifies user interactions and tracking events on the page.
• Implemented a geo image next to the search form for every flight search with graphql and React.

• Designed and implemented a caching mechanism to parse and store interaction rules used for a system of deepwater wells into compiled code blocks, significantly speeding up the loading process of the rules.
• Compressed the output file size by 75% by redesigning an auditing system in the code base.
• Built RESTful API endpoints for processing wells data with Loopback and MySQL and implemented the unit tests for each endpoint with SuperTest.

• Developed and maintained a mobile platform for a traveling app, released on both Google Play and App Store with over 1000 downloads.
• Implemented a smart tour checklist, summarizing the required items based on the data of the visiting countries requested from MongoDB; the feature has gone alive on Google Play.
• Implemented a syncing mechanism for local Android database and MongoDB, saving offline user activities that can be synchronized to MongoDB when connected to the Internet.

Programming Languages

• C/C++
• Python (Tensorflow, Pytorch, Keras, Numpy)
• Java (Spring)
• JavaScript (NodeJS, React, Bootstrap)
• C#
• Scala
• Erlang

Databases

• MySQL
• PostgreSQL
• MongoDB

I wholeheartedly take delight in building softwares and I am excited about the sheer joy of creating something fun!

Facial Features Recognition

• Detects and highlights key facial features given an image.
• Designed and trained a CNN model with AlexNet as the basic structure on YouTube Faces Dataset using Pytorch.

Some results →

Image Captioning

• Describe (auto caption) a given image.
• Trained an end-to-end CNN-RNN (LSTM) model using Pytorch with the Microsoft COCO dataset as described in the paper Neural Image Caption Generator.

Some results →

Boston University

Tufts University