Duc Do

Cao Thien Tan, Trang Phan Thi Thu, Duc Nghiem Do, Ho Ngoc Anh, Hanyang Zhuang, Duc Dung Nguyen. 2026. UCAN: Unified Convolutional Attention Network for Expansive Receptive Fields in Lightweight Super-Resolution.

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2026)

Accepted. Preprint available soon.

Duc Nghiem Do, Minh Nghiem Do, Dang Nguyen, Hung N. Huynh, Quan Le, Jacques Kpodonu, Phat K. Huynh. 2026. Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening.

Canadian Artificial Intelligence Association Conference (CAIAC 2026)

Under review. Preprint available soon.

Predictive Analytics, Smart Systems, and Integrated Optimization Lab - North Carolina A&T State University (Remote)

Student Researcher • August 2025 — Present

Research in medical machine learning, with a particular emphasis on cardiology. Perform feature extraction from electrocardiogram (ECG) signals; apply feature selection, dimensionality reduction for visualization, classification, unsupervised clustering, disease phenotype modeling, explainable AI (XAI), self-supervised learning, and fine-tuning of ECG foundation models.

Database and Data Mining Lab - University of Manitoba

Student Researcher • May 2025 — October 2025

Researched on Breast Cancer Detection and LLM Quantization. Collaborated on two research projects (1) developed data pipelines and machine learning techniques for breast cancer detection using mammogram scans, and (2) explored large language model (LLM) quantization to improve memory efficiency and inference performance. Utilized PyTorch, albumentations, pydicom, C-based scripts for weight extraction, and deep learning architectures such as ResNet-50 and Vision Transformers (ViT).

Linagora Vietnam

Software Intern • August 2025 — September 2025

Worked on Twake Calendar frontend tasks using TypeScript and joined meetings across teams (Java, JavaScript, DevOps, QA, and Mobile Flutter) to understand open-source product workflows.

University of Manitoba

Bachelor of Science in Data Science • 2023 — 2028 (Expected)

• GPA 4.19/4.5
• Executive at UM Tech & Innovation Club
• Undergraduate Research Award 2025 recipient (URA)
• Faculty of Science Dean's Honour List
• International Undergraduate Student Scholarship

Coursework and research focused on machine learning, data science, and applied computing/statistics.

Class Scheduler Website

Web Application • 2024

• Developed a website to optimize student class schedules, attracted few hundred users.
• Implemented Backtracking for schedule generation & web scraping for course data.
• Shared on Reddit.

World Cup Database System

SQL, Database Design, CLI, Security • 2025

• Designed and implemented a relational database system for World Cup data, including schema design and table normalization (1NF–3NF).
• Developed a command-line interface in Python to interact with a database server using SQL queries with injection prevention.
• Preprocessed and loaded raw data into the database, ensuring data consistency and referential integrity.

Air Quality Analysis Project

Data Science project • 2025

• Explored relationships between pollutants, sensor data, and environmental factors to build predictive models.
• Conducted exploratory data analysis (EDA), time-series forecasting, and machine learning techniques.

University of Manitoba Student Advisor Chatbot

Web Application • 2025

• Developed an AI-powered chatbot for university student advising.
• Integrated LangChain, SQL, RAG method, Ngrok, and automation.

Harvest Planning Optimization

Optimization project • 2023

• Developed an academic project solving a harvesting optimization problem.
• Utilized Backtracking, Genetic Algorithms, Constraint Programming, and Linear Programming.

Programming

Python, Java, R, C/C++, JavaScript, Shell Script, SQL, HTML/CSS, Markdown, LaTeX.

Tools

Git, Jupyter Notebooks, PyTorch, Optuna, RAG, LangChain, Slurm, Microsoft Azure, Google Cloud Platform, Caddy Web Server, Tableau, RapidMiner, Microsoft Office.

Machine Learning & AI

PCA, t-SNE, Random Forest, SVM, KNN, K-Means, GMM, HDBSCAN, Neural Nets (MLPs, CNNs, Transformers).

Optimization

Gradient Descent, Backtracking, Simulated Annealing, Genetic Algorithms, Constraint Programming.

Personal

Detail-oriented, efficient, adaptable, technical writing, eager to learn new technologies.