nguyễn đình bình an

mL Engineer

+84911.752.125

nguyendinhbinhan2001@gmail.com

Ho Chi Minh City, Vietnam

education

UIT, VNUHCM

( 09.2019 – 06.2023 )

Major: Computer Science

GPA: 8.8/10

Received the Studying Encouraging Scholarship twice, which is awarded to only the top 10% of students in the Honor Program class in semester

Certifications

TOEIC Listeninng – Reading 695

skills

Programming Languages

  • Python, JavaScript, Java, C#, Go.

Machine Learning

  • Naive Bayes, Decision Tree, KNN, Random Forest, Linear Regression
  • Clustering, Recommendation
  • Reinforcement Learning
  • Information Retrieval, Computer Vision
  • Hyperparameter Tuning

Deep Learning

  • MLP, LSTM, CNN

Others

  • Database Management: MongoDB, SQL Server, Redis
  • Web crawling: Selenium
  • AWS: EC2, EFS, LBS, S3
  • Backend Framework: Spring Boot, Nodejs
experience

Mitsubishi Corporation Urban Development – HCM city

Junior Backend Developer – (01/2024 – 06/2025)

Developed real estate data pipelines via web crawling and seeding, then validated and debugged behavioral data (buyer/seller interactions) to support backend systems for property transaction platforms.

Projects

Connect4 Game’s AI Agents

AI Project – 2 participants – (12/2021 – 12/2021)

My Roles:

  • Research on various types of Reinforcement Learning algorithms, including Alpha-Beta Pruning, Monte Carlo Tree Search (MCTS) and Deep Q-Learning.
  • Enhance MCTS agent using CNN, inspired by AlphaGo Zero.
  • Implement and evaluate performance of listed agents.

Used Techniques:

  • Language: Python
  • Frameworks: Keras & PyTorch
  • Algorithm performance metrics: Time consuming & Win rate

ADVANCED FFNET

AI Project – 2 participants – (3/2022 – 5/2022)

Improving FFNet Model for Video Fast Forwarding Problem – Reimplement the paper source code and make improvement on it.

My Roles:

  • Implement FFNet and coded improvement ideas.

Used Techniques:

  • Language: Python 3.5
  • Frameworks: Tensorflow 1.10 & PyTorch 1.1
  • Feature Extraction: MobileNet-V3
  • Learning Method: Deep Q-Learning
  • Model Architecture: LSTM & MLP
  • Model accuracy evaluation: F1-score & Coverage

JSFFNet

AI Web Application – 1 participants – (9/2022 – 11/2022)

Only client-side web application of Advanced FFNet on purpose to prove real-time processing possibility as an application. 

My Roles:

  • Design base system flow, UI and API.

Used Techniques:

  • Language: JavaScript, HTML, CSS
  • AI framework: Tensorflowjs

Project References: