Yuxiang Bao, Developer in Medford, MA, United States
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Yuxiang Bao

Verified Expert  in Engineering

Bio

Having studied advanced machine learning (ML) theory for the past three years, it’s safe to say Yuxiang knows ML quite well and he's delivered multiple projects using cutting-edge ML algorithms and tools. While at school, he also spent two years researching NLP. With a solid knowledge base in ML and NLP, hands-on experience, and exemplary communication skills—both written and verbal—Yuxiang will add value to your project.

Availability

Part-time

Preferred Environment

Jupyter Notebook, GitHub, Git, Windows, Linux

The most amazing...

...project I've worked on was a sentiment analysis system which featured extraction on both semantic and syntax information and aspect relationships.

Work Experience

Machine Learning Engineer

2018 - 2018
Fujitsu
  • Built a crawler that collected the labels of a given phone number from a search engine's semi-open database. It processed millions of call detail records from tens of thousands of applicants for a subprime loan using Pandas.
  • Manually selected features and classifiers with different imbalance dataset handle tricks to build a risk classification system and achieved 72% precision.
  • Proposed an info-flow model based on a call detail record.
  • Applied a model on a random-walk-based graphic neural network implemented by TensorFlow and Keras which is faster than a DNN approach based on the Tucker decomposition and achieved similar metrics, including a 76% F1-score and with 78% precision.
Technologies: Scikit-learn, Keras, Selenium, Spark, TensorFlow, Python

Research Assistant

2015 - 2017
Tsinghua University
  • Collected reviews using Selenium from Amazon and applied a feature-extraction algorithm to find sentiment words and corresponding aspect words.
  • Transformed raw reviews into VSM and built a binary sentiment analysis system and achieved 88% accuracy.
  • Collected FAQs from an eCommerce website using Python.
  • Extracted the subject, predicate, and object from the questions and answers.
  • Built an automatic question-answering system based on similarity and applied it to an iBen robot.
Technologies: Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT)

Sentiment Analysis System

I used BERT and Kindle reviews to build a classifier to distinguish incoming text into categories: positive, mixed, and negative. I also modified the BERT model by changing the loss function to focal loss to fit the imbalance of the dataset and achieved a 75% macro F1-score.
2017 - 2019

Master's Degree in Computer Science

Worcester Polytechnic Institute - Worcester, MA, USA

2013 - 2017

Bachelor's Degree in Computer Science

Beijing Institute of Technology - Beijing, China

Libraries/APIs

TensorFlow, Keras, Scikit-learn, React, PyTorch, Node.js, Stanford NLP

Tools

Amazon Elastic Block Store (EBS), Git, GitHub

Languages

Python 3, Java, JavaScript, Python, C++, Scala, SQL

Frameworks

Express.js, Hadoop, Spark, Selenium, Angular, Django, Flask, Spring Boot

Platforms

Linux, Android, Jupyter Notebook, Windows

Storage

MySQL, MongoDB, Apache Hive

Other

Natural Language Processing (NLP), Machine Learning, Generative Pre-trained Transformers (GPT)

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