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RUI ZHAO

Quant Researcher

Harveston Asset Management

About Me

I am a Quantitative Researcher in Singapore. I completed my Ph.D studies at Nanyang Technological University supervised by Prof. Mao Kezhi. My Ph.D Dissertation title is Representation Learning for Sentences and Documents. Before that I obtained my undergraduate degree from Southeast University. In SEU, my FYP thesis named Stochastic Resonance for Machine Fault Diagnosis was guided by Prof. Yan Ruqiang. I have several years experience in developing machine learning projects (from POC to production) across various domains. My main research interests are machine learning, natural language processing and predictive modelling. Currently, I am exploring the application of machine learning on algorithmic trading.

Interests

  • Machine Learning
  • Natural Language Processing
  • Quantitative Trading
  • Smart Manufacturing

Education

  • Ph.D in Machine Learning, 2017

    Nanyang Technology University

  • BSc in Electrical Engineering, 2012

    Southeast University

Experience

 
 
 
 
 

Quant Researcher

Harveston

Jul 2018 – Present SG
Develop and test automated quant trading strategies.
 
 
 
 
 

Adjunct Faculty

National University of Singapore

Jun 2018 – Present SG
Design and teach data science modules for MSBA students.
 
 
 
 
 

Data Scientist

Shopee

Jul 2017 – Jul 2018 SG
Developed and implemented large-scale machine learning systems such as recommendation engine, category categorization and etc.

Recent Notes

20 For Twenty

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Last updated on Jan 12, 2020 4 min read

Advances in Financial Machine Learning

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Last updated on Dec 29, 2019 4 min read

Following the Trend

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Last updated on Jul 8, 2019 3 min read

Recent Publications

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Probabilistic Transfer Factor Analysis for Machinery Autonomous Diagnosis Cross Various Operating Conditions

The variability of machinery fault signatures causes the data samples to follow different distributions under various operating …
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Deep learning and its applications to machine health monitoring

Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide …
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Multi-level information fusion for induction motor fault diagnosis

Condition monitoring and fault diagnosis are of significance to improve the safety and reliability of motors, given their widespread …
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Building occupancy estimation with environmental sensors via CDBLSTM

Buildings consume quite a lot of energy; hence, the issue of building energy efficiency has attracted a great deal of attention in …
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Convolutional discriminative feature learning for induction motor fault diagnosis

A convolutional discriminative feature learning method is presented for induction motor fault diagnosis. The approach firstly utilizes …
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Contact

  • [myfirstname]91seu [at] gmail.com
  • Singapore, Central, Singapore 
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