A computer‑vision and deep‑learning engineer working at the seam between research and production — currently at Eurofinsin Ho Chi Minh City, recently a student researcher in Tokyo, presently chasing a Master's in AI.
My name is Nguyễn Văn Lộc — a professional and enthusiastic programmer with a particular love for Artificial Intelligence, especially Computer Vision and Deep Learning. I am a quick learner with a self-learning attitude, always eager to explore new technologies and passionate about solving hard problems. Python is my core language and closest tool; I love almost everything that can be built with it.
Over the last several years I've moved between a research lab at the University of Science in Ho Chi Minh City, a student researcher position at the National Institute of Informatics in Tokyo (Echizen Laboratory, under Prof. Isao Echizen), a role building AI agents at Autonomous, and now a Software Engineer position at Eurofins. The throughline is the same: take a model out of the notebook and into something a person can use without thinking about it.
I read widely, ship steadily, and believe that the most interesting problems sit between disciplines — vision and language, research and product, Vietnamese and Japanese, theory and the messy thing it's supposed to model.
A demonstration of a single‑pass removal approach for visually similar object clusters in natural imagery, extending zero‑shot inpainting toward the messy, repetitive scenes that one‑object methods choke on.
A zero‑shot object removal method that leverages pixel‑wise attention dissolution and latent guidance within a diffusion framework — achieving clean inpainting without task‑specific fine‑tuning on the target scene.
Addressing the Ho Chi Minh City AI Challenge 2025, Visionary introduces an adaptive keyframe extraction algorithm, a Qwen3‑VL pre‑processing pipeline for metadata generation with integrated OCR, a flexible architecture supporting multiple embedding models, and Reciprocal Rank Fusion for synthesizing retrieval results across complex, large‑scale video tasks.
A practical pipeline for classifying ENT endoscopy imagery that pairs an ensemble of vision backbones with a curated augmentation schedule, designed to remain robust to the imaging variance typical of clinical capture devices.
Working on something that needs computer vision, agent infrastructure, or a careful pair of hands? Let's talk — research collaborations, contract work, or just an interesting question.