CV

Let's get to know me about my education path, work experience and other information through my curriculum vitae.

Basics

Name Tran Dang Trung Duc
Label AI Engineer & AI Lecturer
Email trandangtrungduc@gmail.com
Phone (84) 033.265.8181
Url https://github.com/trandangtrungduc/
Summary AI Engineer at DigiWorker AI and Innotech Vietnam, AI Lecturer at MCI Vietnam.

Work

  • 2022.09 - 2024.11
    Assistant Researcher
    Visual Computing Lab
    Working on Korean government projects and writing a research paper
    • A paper at ACM Multimedia 2024
    • A paper at ACVYS 2024
    • A paper at ICEIC 2025
    • A patent of KR application
  • 2021.04 - 2022.07
    Software Engineer
    Bosch Global Software Technologies
    Working as a software developer for projects from China and Japan. Task: unit testing, integration testing, component/function development, devops, lab testing, static code analysis,...
    • The fastest person can be converted directly to an official employee after 3/6 months of internship.

Education

  • 2022.09 - 2024.12

    Seoul, South Korea

    Master
    Seoul National University of Science and Technology
    Computer Vision - 33 credits in 4 semesters
    • Introduction of Unmanned Robotic Vehicles
    • Reinforcement Learning
    • Machine Learning
    • Statistic Machine Learning
    • Optimization Algorithm
    • Digital Image Processing
    • Advanced Topics on Information Technology
    • Advanced Natural Language Processing
    • Introduction to Deep Learning
    • ...
  • 2021.03 - 2022.11

    HCM City, Vietnam

    Master
    Ho Chi Minh City University of Technology
    Data Science - 18 credits in 1 semester
    • Programming Foundation for Data Analysis and Visualization
    • Advanced Algorithm
    • Mathematical Foundation for Computer Science
    • Methodology of Scientific Research
    • Business Ethics and Corporate Social Responsibility
    • Philosophy
  • 2015.09 - 2021.11

    HCM City, Vietnam

    Engineer
    Ho Chi Minh City University of Technology
    Mechatronics - 268 credits in 5 years
    • Statistic Methods and Data Analysis
    • Robotics
    • Intelligent Actuators
    • Mechatronics System Design
    • Digital Signal Processing and Application
    • Object Oriented Design and Analysis
    • Advanced Programming Language
    • Advanced Data Structure
    • Numerical Analysis and Optimization
    • Linear and Nonlinear Control Systems
    • ...

Achievements

  • 2022.2024
    Scholarship
    Tuition scholarships
    75% tuition scholarship for all semesters at Seoul National University of Science and Technology in South Korea
  • 2022
    Scholarship
    Korean Professor
    Full scholarship for studying and researching at Visual Computing Lab of Seoul National University of Science and Technology in South Korea
  • 2018
    Final round
    Pasona Global
    Full scholarship for students all over Vietnam. Reached the final round (Top 5) of the Pasona International Exchange Program for students doing summer internships in Japan
  • 2018
    Scholarship
    Ho Chi Minh City University of Technology
    Full scholarship to learn Japanese to prepare to work as an engineer in Japan. The only student who does not need to study Japanese language preparation for the scholarship.
  • 2017
    Scholarship
    Tuition scholarships
    Full scholarship for students all over Vietnam. Top 1/2 students received 100% tuition scholarships for Business Administration at International Pacific University in Japan
  • 2017
    Final round
    Mitsubishi Heavy Industries
    Full scholarship for students all over Vietnam to study Japanese and university in Japan. Reached the final round (Top 6) of the scholarship
  • 2016
    Scholarship
    Vietnamese Government
    Full scholarships for 10 students from all over Vietnam to study nuclear energy in Japan

Publications

  • 2025
    SP2Mask4D: Efficient 4D Panoptic Segmentation Using Superpoint Transformers
    IEEE
    The increasing need for precise segmentation in dynamic outdoor environments, particularly with LiDAR data, has brought attention to the 4D panoptic segmentation task. This task requires accurate identification of both objects and semantic labels across spatial and temporal dimensions. In this work, we present SP2Mask4D, a novel approach that replaces the commonly used transformer architecture with a superpoint-based transformer architecture. This modification leads to faster inference and reduced memory consumption, while maintaining competitive performance compared to transformer-based methods. While both approaches use attention mechanisms, traditional transformer models apply attention to all points, resulting in high computational costs. In contrast, SP2Mask4D focuses attention within localized superpoints, significantly lowering the computational burden. Experiments on the SemanticKITTI dataset show that SP2Mask4D reduces inference time by about 32.8% and improves memory efficiency by 60.3%, while preserving segmentation performance comparable to state-of-the-art methods.
  • 2024
    Transformer-supported Tackling 3D Point Cloud Instance Segmentation
    ACVYS 2024
    This study aims to address these challenges by utilizing the strengths of transformer models, a recent innovation in machine learning known for their ability to handle large-scale data efficiently. By reducing the number of point clouds required for training, the approach simplifies the overall model architecture and removes unnecessary intermediate steps. This not only decreases training time but also significantly reduces model complexity, making the process more streamlined and resource-efficient. Despite these reductions, the method maintains high accuracy in classifying and recognizing objects in 3D environments, ensuring robust performance even in dynamic and cluttered scenarios commonly encountered in autonomous driving. Thus, this research presents a more effective and efficient approach to 3D point cloud instance segmentation, pushing the boundaries of what is possible in AI-driven 3D object recognition.
  • 2024
    MSTA3D: Multi-scale Twin-Attention for 3D Instance Segmentation
    ACM Multimedia 2024
    Recently, transformer-based techniques incorporating superpoints have become prevalent in 3D instance segmentation. However, they often encounter an over-segmentation problem, especially noticeable with large objects. Additionally, unreliable mask predictions stemming from superpoint mask prediction further compound this issue. To address these challenges, we propose a novel framework called MSTA3D. It leverages multi-scale feature representation and introduces a twin-attention mechanism to effectively capture them. Furthermore, MSTA3D integrates a box query with a box regularizer, offering a complementary spatial constraint alongside semantic queries.

Skills

Soft Skill
Presentation
Hard-working
Time Management
Leadership
Adaptability
Problem-solving
Hard Skill
Microsoft Office
Python Programming
Github, Docker, PyTorch
Libraries: PLY, Sponv, Open3D, Minkowski, OpenCV, PaddleOCR,...
LLMs: GPT, Gemini, Llama, Claude, Deepseek, Grok,...

Languages

Vietnamese
Native speaker
English
Professional working proficiency
Japanese
Professional working proficiency
French
Limited working proficiency

Interests

Computer Vision
Instance Segmentation
Semantic Segmentation
Panoptic Segmentation
3D/4D Point Cloud
Lidar
Robotics
Autonomous Driving
VR/AR