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Nghĩa Trương Thanh

Assistant Professor (Handwritten Mathematical Expression Recognition Labeling)

Japan flagTokyo, Japan
$20.00/hrExpert

Key Skills

Software

No software listed

Top Subject Matter

Handwritten mathematical expression recognition
Medical image diagnosis and organ segmentation
Traffic video parameter estimation and vehicle tracking

Top Data Types

ImageImage
VideoVideo

Top Task Types

Classification
Segmentation
Tracking

Freelancer Overview

Assistant Professor (Handwritten Mathematical Expression Recognition Labeling). Brings 11+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Doctor of Philosophy, Tokyo University of Agriculture and Technology (2022) and Master of Science, Ho Chi Minh University of Technology (2018). AI-training focus includes data types such as Image and Video and labeling workflows including Classification, Segmentation, and Tracking.

ExpertEnglishJapanese

Labeling Experience

Assistant Professor (Handwritten Mathematical Expression Recognition Labeling)

ImageClassification
I developed and deployed AI models for recognizing handwritten mathematical expressions using deep neural networks. This involved curating and labeling image data for training and evaluation of recognition and automatic scoring systems. The work required creating accurate labeled datasets and evaluating model predictions for mathematical handwriting. • Built annotated datasets of handwritten mathematical expressions. • Performed data labeling to enable end-to-end recognition and automatic scoring. • Collaborated on the model evaluation by verifying ground truth data. • Utilized internal/proprietary tools for annotation and model feedback.

I developed and deployed AI models for recognizing handwritten mathematical expressions using deep neural networks. This involved curating and labeling image data for training and evaluation of recognition and automatic scoring systems. The work required creating accurate labeled datasets and evaluating model predictions for mathematical handwriting. • Built annotated datasets of handwritten mathematical expressions. • Performed data labeling to enable end-to-end recognition and automatic scoring. • Collaborated on the model evaluation by verifying ground truth data. • Utilized internal/proprietary tools for annotation and model feedback.

2022 - Present

Researcher (Dataset Creation & Annotation for Mathematical Expression Recognition)

ImageClassification
I created and managed labeled datasets for training deep learning models to recognize handwritten mathematical expressions. My work included annotating datasets, designing labeling guidelines, and conducting model evaluations against labeled data. This role contributed to state-of-the-art results on standard benchmark datasets. • Led proposal and construction of HME recognition datasets. • Developed guidelines for consistent and accurate annotation. • Helped in competition data checks and benchmark dataset evaluation. • Employed internal tools for image annotation tasks.

I created and managed labeled datasets for training deep learning models to recognize handwritten mathematical expressions. My work included annotating datasets, designing labeling guidelines, and conducting model evaluations against labeled data. This role contributed to state-of-the-art results on standard benchmark datasets. • Led proposal and construction of HME recognition datasets. • Developed guidelines for consistent and accurate annotation. • Helped in competition data checks and benchmark dataset evaluation. • Employed internal tools for image annotation tasks.

2019 - 2022

Research Assistant (Liver CT Image Annotation)

ImageSegmentation
I worked on annotating and segmenting CT-images of livers for medical research. Tasks included precisely marking organ boundaries and differentiating anatomical structures in DICOM images. These labels were used to train and validate automated organ segmentation models. • Segmented and labeled livers in CT-images. • Differentiated anatomical regions for model training. • Utilized medical image annotation tools for DICOM files. • Enabled validation of segmentation algorithms with high-quality labels.

I worked on annotating and segmenting CT-images of livers for medical research. Tasks included precisely marking organ boundaries and differentiating anatomical structures in DICOM images. These labels were used to train and validate automated organ segmentation models. • Segmented and labeled livers in CT-images. • Differentiated anatomical regions for model training. • Utilized medical image annotation tools for DICOM files. • Enabled validation of segmentation algorithms with high-quality labels.

2016 - 2018

Master's Researcher (Medical Image Segmentation and Annotation)

ImageSegmentation
I participated in segmenting and annotating medical images for training deep learning models in organ identification and diagnosis. Tasks involved marking human organs on CT images for use in organ reconstruction and diagnostic algorithms. The experience required high attention to detail and use of medical imaging software. • Segmented liver and other organs in CT medical images. • Labeled and prepared datasets for organ segmentation training. • Applied internal annotation tools common in medical imaging. • Supported research with well-annotated datasets for model validation.

I participated in segmenting and annotating medical images for training deep learning models in organ identification and diagnosis. Tasks involved marking human organs on CT images for use in organ reconstruction and diagnostic algorithms. The experience required high attention to detail and use of medical imaging software. • Segmented liver and other organs in CT medical images. • Labeled and prepared datasets for organ segmentation training. • Applied internal annotation tools common in medical imaging. • Supported research with well-annotated datasets for model validation.

2016 - 2018

Research Assistant (Video Annotation for Traffic Analysis)

VideoTracking
I labeled and analyzed video data to estimate and visualize traffic parameters using computer vision techniques. My responsibilities included annotating vehicle positions, trajectories, and behavior over time to create ground truth for tracking models. The data labeling process supported research on traffic analysis and modeling. • Annotated vehicles and movement across video frames. • Labeled trajectories and events for traffic parameter extraction. • Used internal and open-source video annotation tools. • Generated labeled datasets for training and evaluating tracking algorithms.

I labeled and analyzed video data to estimate and visualize traffic parameters using computer vision techniques. My responsibilities included annotating vehicle positions, trajectories, and behavior over time to create ground truth for tracking models. The data labeling process supported research on traffic analysis and modeling. • Annotated vehicles and movement across video frames. • Labeled trajectories and events for traffic parameter extraction. • Used internal and open-source video annotation tools. • Generated labeled datasets for training and evaluating tracking algorithms.

2015 - 2018

Education

T

Tokyo University of Agriculture and Technology

Doctor of Philosophy, Computer Science

Doctor of Philosophy
2019 - 2022
H

Ho Chi Minh University of Technology

Master of Science, Computer Science

Master of Science
2016 - 2018

Work History

W

Wacom-Tuat Joint Research Lab

Assistant Professor

Tokyo
2022 - Present
N

Nakagawa Lab, Tokyo Univ. Of Agriculture And Technology

Researcher

Tokyo
2019 - 2022