My Chinese name is 高嘉蕊, which means beautiful blooming flowers.
I am a first year master student majoring in software engineering at Carnegie Mellon University, Silicon Valley. I received my bachelor's degree in computer science from Fudan University, Shanghai. I was advised by Prof. Yanwei Fu and Prof. Yu-Gang Jiang to work on machine learning and multimedia then. I'm passionate about full-stack software development.Here is my CV.
The profile picture is taken by my sweet boyfriend Xingyi Zhou.
Google LLC, Sunnyvale, CA
Software Engineer Intern in NBU team at Google Ads
May 2019 - Aug 2019
Developed a manual review tool for Engagement Rewards By Google to visualize per user abuse information.
Established fully tested back-end RPC services to retrieve and process data from Spanner database.
Integrated with two frameworks to securely access user data and utilize Google-wide abuse protections.
Morgan Stanley, Shanghai
Application Development Intern in the department of Corporate & Funding Technology
Jul 2017 - Sept 2017
Developed a Java web application for Outside Business Interest system as an effective PoC.
Proposed a novel graph algorithm in AngularJS2.0 on front-end to render decision processes with a config- urable set of tree-like rules.
Designed a new database schema using DB2 temporary tables on back-end to make history data traceable.
Jiarui Gao, Yanwei Fu, Yu-Gang Jiang, and Xiangyang Xue
ACM International Conference on Multimedia Retrieval(ICMR), 2017
In this paper, we propose a new architecture–Frame-Transformer Emotion Classi cation Network (FT-EC-net) to solve three highly correlated emotion analysis tasks: emotion recognition, emotion attribution and emotion-oriented summarization. We also contribute a new dataset for emotion attribution task by annotating the ground-truth labels of attribution segments.
Guoyun Tu, Yanwei Fu, Jiarui Gao, Boyang Li, Yu-Gang Jiang and Xiangyang Xue
IEEE Transactions on Multimedia
We propose a new neural approach Frame-Bi-stream Emotion Attribution-Classification Network(BEAC-Net), an end-to-end trainable neural architecture that tackles emotion attribution and classification simultaneously with significant performance improvements. Also we propose an efficient dynamic programming method for video summarization based on the output of A-Net. To establish a good benchmark for emotion attribution, we re-annotate the Ekman-6 dataset with the most emotion-oriented segments which can be used as the ground-truth for the emotion attribution task.
Last update: 08/11/2019 By Jiarui Gao