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# Zhiyuan Yao
- URL: https://fsc.stevens.edu/zhiyuan-yao/
- Published: 2019-09-12T19:23:17.000Z
- Updated: 2026-05-16T22:32:37.000Z
- Description: Y
- Author: Zheng Xing
- Tags: Former Lab Staff, PhD, #Import 2024-05-30 10:26, #Import 2025-11-08 22:14

##### 

| Current Position | PhD Candidate                                                                  |
| ---------------- | ------------------------------------------------------------------------------ |
| Employer         | Stevens Institute of Technology                                                |
| Degree           | PhD Financial Engineering - May 2023                                           |
| LinkedIn         | [Link](https://www.linkedin.com/in/zhiyuan-yao-74b84113a/?ref=fsc.stevens.edu) |

#### [General Information](#collapse1) 

I currently am a first-year Ph.D. student in financial engineering at Stevens. I got my bachelor degree of science in mathematics from Nankai University, Tianjin China. Then I got my master degree of science in financial engineering at Stevens. My research interests include machine learning algorithms and their applications in finance, reinforcement learning and optimal control.

#### [Education](#collapse2) 

- **Doctor of Philosophy in Financial Engineering**, Stevens Institute of Technology, Hoboken, NJ  
Expected May 2022
- **Master of Science in Financial Engineering**, Stevens Institute of Technology, Hoboken, NJ  
August 2017 - May 2019
- **Bachelor of Science in Mathematics and Applied Mathematics**, Nankai University, Tianjin, China  
August 2013 - June 2017

#### [Work Experience](#collapse3) 

- **Hanlon Financial Systems Lab**  
Research Assistant, September 2017 - May 2019  

  - Research on deep reinforcement learning (RL), developed distributed simulation system for RL, implemented RL algorithms (PPO, DDPG, etc.) and controlled robotic agents across Internet by RL models.
  
- **Clearpool Group & Stevens Institute of Technology**  
Graduate Researcher, March 2018 - July 2019  

  - Research on dark pool liquidity detection based on ensemble machine learning and traditional statistical models; analysis on the liquidity signals and transition patterns. Improved models are deployed to production.
  
- **WorldQuant, LLC, Beijing, China**  
Intern and Virtual Quantitative Researcher, June 2016 - November 2017  

  - Intern: Research on technical alpha strategies for stocks with top liquidity in U.S. stock market.
  - Virtual Quantitative Researcher: Developed alpha strategies based on fundamental analysis and analytical data. Applied machine learning algorithms to alpha strategy development on web based strategy backtesting system.

#### [Research Interests](#collapse7) 

My research interests include Reinforcement learning algorithms and their applications in finance. 

#### [Projects & Research](#collapse8) 

Reinforcement Learning in Delayed Environment: A Model-Based Approach, September 2019 - Present 

- Construct simulation system for Delayed Markov Decision Process (DMDP) model.
- Parametrized state transition model using variants of Recurrent Neural Networks.
- Combined main-stream actor-critic model with state transition model to improve its performance in delayed environment.

High Frequency Optimal Execution Using Reinforcement Learning, January 2019 - Present 

- Reformulated the optimal execution into dynamic control frameworks and solved by policy optimization.
- Applied continuous and discrete control reinforcement learning algorithms to nd optimal limit order price.
- Used realistic market replication system to justify the trained models and typical optimal execution benchmark.

Alpha Driven Auto-Trading System, November 2018 - March 2019 

- Constructed fundamental and price-volume database and data feeding interface using web scraping and Bloomberg API.
- Built event-driven back-testing system to seek for valid alpha signals, generate statistics and visualize portfolio performance.
- Built automate trading system aggregated with portfolio optimization in terms of target Beta and cash neutralization.

Robotics Applications Platform Integrated Development (RAPID), September 2017 - July 2019 

- Create a system which can control both simulated and physical robot through Robotic Operation System (ROS).
- Allow user to control multiple agents across internet and to stream camera input from agent to control side.
- Some main-stream reinforcement learning algorithms have been implemented and tested on this system.

#### [Awards](#collapse10) 

- **Provost Doctoral Fellowship Award**, Stevens Institute of Technology, June 2019
- **Outstanding Academic Achievement Award**, Stevens Institute of Technology, May 2019
- **Master's Fellowship Award**, Stevens Institute of Technology, August 2017
- **Meritorious Winner**, Interdisciplinary Contest in Modeling, awarded by COMAP, April 2017
- **Gold Medal (Global Rank: 62)**, WorldQuant Challenge, WorldQuant LLC, May 2016
- **The 3rd-Class University Scholarship**, Nankai University, November 2013