ISE-UET-AutoML/frontend
Jul 2025 – Apr 2026
6 Months active
Languages Used
JSXJavaScriptReactCSSHTML
Technical Skills
API IntegrationAnt DesignAuthenticationFront-end DevelopmentFrontend DevelopmentJavaScript
Ngtvu1752
PROFILE
Ngtvu1752
Over six months, contributed to the ISE-UET-AutoML/frontend repository by building and enhancing end-to-end machine learning dataset workflows. Developed robust UI features for multimodal data labeling, including support for image, tabular, audio, and video modalities, and implemented authentication, drag-and-drop uploads, and advanced dataset management. Leveraged React, JavaScript, and Ant Design to deliver seamless user experiences, while integrating API-driven workflows for labeling, training, and anomaly detection. Addressed reliability through bug fixes in authentication, session handling, and data validation. Tuned frontend configuration for performance, such as increasing image batch processing capacity, and maintained clear, maintainable code with precise commit practices.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
54Total
Bugs
13
Commits
54
Features
22
Lines of code
84,357
Activity Months6
Your Network
15 peopleSame Organization
@vnu.edu.vn3
Shared Repositories
12
Trung-Hieu NguyenMember
VbearCodeMember
giapMember
hieuclcMember
Duc Luu LeMember
rootMember
Dr-HeimerdingerMember
Dr-HeimerdingerMember
shinyEazyMember
Work History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 — ISE-UET-AutoML/frontend: Delivered an image processing enhancement increasing ZIP image capacity to 1000, boosting batch processing throughput and scalability. Change implemented as a focused config/tuning commit (6c56c623708d9611bc7015d608cd118f6599c8b1): 'adjust: IMG_NUM_IN_ZIP to 1000'. No major bugs fixed this month in this repo. Impact: supports larger image workloads per operation, reduces processing time per batch, and improves overall responsiveness for image workflows. Skills: frontend configuration tuning, precise commit messaging, and performance/scalability improvements in image processing pipeline.
1 Commits • 1 Features
Apr 1, 2026April 2026 — ISE-UET-AutoML/frontend: Delivered an image processing enhancement increasing ZIP image capacity to 1000, boosting batch processing throughput and scalability. Change implemented as a focused config/tuning commit (6c56c623708d9611bc7015d608cd118f6599c8b1): 'adjust: IMG_NUM_IN_ZIP to 1000'. No major bugs fixed this month in this repo. Impact: supports larger image workloads per operation, reduces processing time per batch, and improves overall responsiveness for image workflows. Skills: frontend configuration tuning, precise commit messaging, and performance/scalability improvements in image processing pipeline.
April 2026
November 2025
4 Commits • 2 Features
Nov 1, 2025November 2025: Focused on expanding data modality support and enhancing dataset quality checks in ISE-UET-AutoML/frontend. Delivered video data support and classification, anomaly detection for datasets, and updated media handling to include video and audio types, improving readiness for video-based model training and data governance.
November 2025
4 Commits • 2 Features
Nov 1, 2025November 2025: Focused on expanding data modality support and enhancing dataset quality checks in ISE-UET-AutoML/frontend. Delivered video data support and classification, anomaly detection for datasets, and updated media handling to include video and audio types, improving readiness for video-based model training and data governance.
October 2025
8 Commits • 3 Features
Oct 1, 2025Month: 2025-10 — ISE-UET-AutoML/frontend Summary: Focused on accelerating ML dataset workflows and improving user experience. Delivered drag-and-drop file upload for text and tabular data with drag state management, input handling, and enhanced visual feedback. Extended dataset creation to support CLUSTERING tasks and audio classification, including UI/backend support and audio file type handling. Improved user-facing login/signup messages for clarity and guidance. Also performed metadata naming cleanup and type ordering refinements to increase consistency and maintainability across the codebase. This work collectively reduces data ingestion friction, enables richer ML experiments, and improves onboarding and usage guidance for end users. Key commits referenced below include: 125d937d1b707c490c2fc1b419140139462f1097; 596843d75c348d24c882a90f5ddd9099f4df37d1; 93d6803a901833300b533c0ba1af0451daac9011; a6b7925ba61db8a7e9ffaa726795c21e288f02e6; ba9a42ab91785025c6178c58f3facbd5927c85dd; 598a20fbfd81d522ef5683352c8cce79e8a386cc; 41448ef7489c0056cea48515cfbfacde6befc45c; e955f948470f7a3d750ea826187a2e6d97817c00
8 Commits • 3 Features
Oct 1, 2025Month: 2025-10 — ISE-UET-AutoML/frontend Summary: Focused on accelerating ML dataset workflows and improving user experience. Delivered drag-and-drop file upload for text and tabular data with drag state management, input handling, and enhanced visual feedback. Extended dataset creation to support CLUSTERING tasks and audio classification, including UI/backend support and audio file type handling. Improved user-facing login/signup messages for clarity and guidance. Also performed metadata naming cleanup and type ordering refinements to increase consistency and maintainability across the codebase. This work collectively reduces data ingestion friction, enables richer ML experiments, and improves onboarding and usage guidance for end users. Key commits referenced below include: 125d937d1b707c490c2fc1b419140139462f1097; 596843d75c348d24c882a90f5ddd9099f4df37d1; 93d6803a901833300b533c0ba1af0451daac9011; a6b7925ba61db8a7e9ffaa726795c21e288f02e6; ba9a42ab91785025c6178c58f3facbd5927c85dd; 598a20fbfd81d522ef5683352c8cce79e8a386cc; 41448ef7489c0056cea48515cfbfacde6befc45c; e955f948470f7a3d750ea826187a2e6d97817c00
October 2025
September 2025
21 Commits • 8 Features
Sep 1, 2025September 2025 highlights focusing on delivering end-to-end ML labeling workflows, stabilizing the platform, and enhancing dataset management. Key features delivered include Time Series Task UI in modal and frontend, multilabel task capabilities (tabular and text variants) with corresponding UI changes, and dataset visualization improvements. Dataset API and lifecycle enhancements were implemented (initialize/finalize endpoints, dataset_id linkage, and advanced filtering). Major bugs fixed to improve reliability and UX include ECONNRESET between backend and DTS, access-token expiry handling and IMG_CLASSIFI label display, UI dataset display bug, corrected userId retrieval, misspelling fixes, an unintended revert, and a dataset-filter-none fix.
September 2025
21 Commits • 8 Features
Sep 1, 2025September 2025 highlights focusing on delivering end-to-end ML labeling workflows, stabilizing the platform, and enhancing dataset management. Key features delivered include Time Series Task UI in modal and frontend, multilabel task capabilities (tabular and text variants) with corresponding UI changes, and dataset visualization improvements. Dataset API and lifecycle enhancements were implemented (initialize/finalize endpoints, dataset_id linkage, and advanced filtering). Major bugs fixed to improve reliability and UX include ECONNRESET between backend and DTS, access-token expiry handling and IMG_CLASSIFI label display, UI dataset display bug, corrected userId retrieval, misspelling fixes, an unintended revert, and a dataset-filter-none fix.
August 2025
8 Commits • 4 Features
Aug 1, 2025Aug 2025 (ISE-UET-AutoML/frontend): Delivered frontend enhancements expanding labeling capabilities and improving data integrity. Implemented Semantic Segmentation UI with color picker, added Multimodal Dataset image column selection and validation, introduced Tabular Regression task type with dataset mapping and build config, and enhanced Dataset Creation/Metadata handling for XML/CSV metadata and optimized polling. Fixed critical login/logout and metadata key issues, and centralized Label Studio integration URL for reliability. Result: accelerated labeling workflows, broadened supported tasks, improved data quality, and strengthened security and integration reliability.
8 Commits • 4 Features
Aug 1, 2025Aug 2025 (ISE-UET-AutoML/frontend): Delivered frontend enhancements expanding labeling capabilities and improving data integrity. Implemented Semantic Segmentation UI with color picker, added Multimodal Dataset image column selection and validation, introduced Tabular Regression task type with dataset mapping and build config, and enhanced Dataset Creation/Metadata handling for XML/CSV metadata and optimized polling. Fixed critical login/logout and metadata key issues, and centralized Label Studio integration URL for reliability. Result: accelerated labeling workflows, broadened supported tasks, improved data quality, and strengthened security and integration reliability.
August 2025
July 2025
12 Commits • 4 Features
Jul 1, 2025July 2025 (ISE-UET-AutoML/frontend) focused on end-to-end Label Studio integration with robust authentication, expansion of data modalities, streamlined labeling-to-training workflows, and UI/UX improvements for dataset management. Delivered fixes to authentication flows, enabling reliable access to Label Studio, and laid groundwork for scalable labeling pipelines and ML training automation.
July 2025
12 Commits • 4 Features
Jul 1, 2025July 2025 (ISE-UET-AutoML/frontend) focused on end-to-end Label Studio integration with robust authentication, expansion of data modalities, streamlined labeling-to-training workflows, and UI/UX improvements for dataset management. Delivered fixes to authentication flows, enabling reliable access to Label Studio, and laid groundwork for scalable labeling pipelines and ML training automation.
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Quality Metrics
Correctness87.2%
Maintainability84.8%
Architecture81.6%
Performance80.2%
AI Usage24.6%
Skills & Technologies
Programming Languages
CSSHTMLJSXJavaScriptReact
Technical Skills
API IntegrationAnt DesignAsynchronous ProgrammingAuthenticationCookie ManagementData ValidationDockerError HandlingFile HandlingForm HandlingFront End DevelopmentFront-end DevelopmentFrontend DevelopmentJavaScriptNode.js
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