Qi Yang

Carpe diem.


I am Qi Yang . I am now working as a NLP Algorithm Engineer in a International Digital Economic Academy(IDEA). Before that, I acquired a Master degree under the supervision of Professor Ke Tang and graduated in 2022. My existing research experiences includes Reinforcement Learning and Evolution Computation and Graph-based Casual Machine Learning.

Before that, I received my B.Eng degree from Huazhong University of Science and Technology, Academy of Artificial Intelligence and Automation. During my undergraduate studies, I completed my thesis under the supervision of Prof. Min OuYang.

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Personality: INTJ; Self-motivated; Nerd (Love reading including science fiction, history, psychology and a little bit philosophy);


  • Code & Programming Proficient in Python 4 years, Know about C/C++, Go
  • AI/ML Framework TensorFlow, Pytorch, Matplotlib, Numpy, Pandas, Gym, Transformers, HuggingFace etc.)
  • Workflow Linux, Shell, Git, Docker, Conda, Singularity
  • Database and Cloud SQL, Access, MySQL, Spark, Alicloud, AWS
  • Others Solidity, HTML/CSS/ Javascript
  • Soft skills Fast Reading and Learning, Grant and Patent Writing, Leadership , Collaboration


International Digital Economy Academy(IDEA)

Research Intern/ NLP ALgorithm Engineer, Cognitive Computing and Natural Languge Centre(CCNL)

Jan. 2022 - now.(ShenZhen , GuangDong)

  • Research Area: Large Pre-trained Langugage Model; Transfer Learning; Derivative-free methods;
  • Designed and developed the semi-supervised cooperative knowledge-based QA system.
  • Researched on parameter-efficient transfer learning algorithms for the large model.
  • Contributed to the open-source to the open-source project Fengshenbang 1.0 and pre-training framework Fengshen

Southern University of Science and Technology

Master of Engineering, Department of Computer Science and Engineering

Sep. 2019 - Jun.2022(ShenZhen , GuangDong)

  • Research Area: Reinforcement Learning; Evolutionary Algorithm; Non-convex Optimization;
  • Lab: NICAL (Supervisor: Ke Tang)
  • Publication : Published 2 published paper (SCI,EI), 1 working paper, 1 invention patent.

HuaZhong University of Science and Technology

Bachelor of Engineering, Academy of Artificial Intelligence and Automation

Sep. 2015 - Jun.2019 (Wuhan, Hubei)

  • GPA : 3.47 , especially reached 3.8 in the last two years
  • Language : IETLS 6.5 band (Reading 8.5 band), CET-4 and CET-6 certification;
  • Award: Outstand Graduate (2019) and Scholarship for Self-improvement (2018)
  • Dissertation: Research on Mechanism of Scaling Law in cities (Funded by National Nature Science Foundation
    of China)
  • Coursework : Math(Probability Theory, Calculus, Linear Algebra, Engineering Mathematics, Operation Research,Equation of Mathematical Physics), Cybernetics(Digital & Analog circuits, Signal Analysis, Control Theory, System Identification, Complexity Science, Systems Engineering, Information System), CS(Data structure & Algorithm, Computer Network, Principle of Micro-Computer, Distributed System, C Programming, Internet Infrastructure), AI(Artificial Intelligence, Machine Learning, Robot Principle, Optimization) etc
  • Social Activities: Elected to President for Science Fiction Association of HUST and Science Fiction Association Union of Wuhan in 2016


  • Awarded as Outstanding Graduate of Huazhong University of Science and Technology (2019)
  • Received RenMin Scholarship for Self-motivated (2018)


  1. Qi Yang, Peng Yang, Ke Tang, Parallel Random Embedding with Negatively Correlated Search, In: proceeding of The Twelfth International Conference on Swarm Intelligence, 2021 (EI,Oral in ICSI2021).
  2. Peng Yang, Qi Yang, Ke Tang, Xin Yao, Parallel Exploration via Negatively Correlated Search, Frontier of Computer Science, 2020 (SCI,Poster in ECOLE2021).
  3. Qi Yang, Peng Yang, Ke Tang, Active Reinforcement Learning over MDPs, 2021(unpublished).
  4. Qi Yang, Peng Yang, Ke Tang, A method to train dynamic obstacle avoiding strategy based on random embedding evolution. Invention Patent. No.2021108449413.


Research on Generalizable and Sample-Efficient Reinforcement Learning by Selective Sampling

Adviser: Ke Tang Nov. 2020 - now

  • Proposed and developed an active learning framework to selectively sampling representative and valuable training sets, which reduced training budget to 50% and achieved competitive performance.
  • Implemented 6 relevant SOTAs on improving generalization and Advanced the procedural content generation environment of RL.

Research on Cooperative Co-evolution Algorithm in Reinforcement Learning

Adviser: Ke Tang Sep. 2019 - Nov.2019

  • Developed a group of derivative-free RL optimization algorithms.
    • to alleviate the performance deterioration in million-scale problem(exceeds SOTA more than 40%).
    • to encourage parallel behavior exploration in multi-modal problem(score 2 to 3 times as many as SOTA).
  • Designed and developed an open-source project for Evolutionary-based RL (including NCNES , NCS and NCSRE algorithms).
  • Wrote 2 SCI/EI indexed papers, technical reports (demand from Huawei) and applied 1 patent as 1st author.

Research on Mechanism of Scaling Law in cities (Dissertation of Bachelor)

Adviser: Prof. Min OuYang Dec.2018 - Jun.2019

  • Collect micro and macro statistic data of over 10 cities in 4 countries.
  • Build a mathematic model and simulation for scaling law in complex cities system and estimate several significant indexes.

Life prediction and Health Diagnosis of Rolling Bearing with Support Vector Machine(Course Project)

Adviser: Prof. Ye Yuan May. 2018 - Jun. 2018

  • Train a SVM-based model to predict the life of rolling bearing and recognize inferior bearing
  • Achieve a 93% accuracy on Industry Real Rolling Bearing Dataset