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 |
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
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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.
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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.
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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 |