Thomas Chuang

Software Engineer

As a software engineer and researcher, I specialize in Vision AI, highly concurrent systems, and clean architecture.

Go Bears!

LinkedIn Email Resume GitHub Medium

Software Engineer 0.99

Map Enthusiast & Adventurer! 0.99

Education

Selected coursework and academic programs

University of California, Berkeley

Concurrent Enrollment StudentComputer Science

Aug 2025 – May 2026

Berkeley, CA

Relevant: CS 61C (Machine Structures), CS 162 (Operating Systems), Data 100 (Data Science)
Planned: CS 164 (Compilers), CS 161 (Computer Security)

National Chengchi University

Dual Bachelor of Arts in Risk Management & Insurance and Land EconomicsMinor in Management Information Systems

Sep 2020 – Jun 2026

Taipei, Taiwan

GPA: 3.86/4.00 • CS Field GPA: 3.98/4.00
Relevant: Data Structures (A+), Algorithms (A+), DBMS (A+), Software Engineering (A+)

Peking University

Exchange Student

Feb 2024 – Jun 2024

Beijing, China

Relevant: Deep Learning Models and Applications, Data Visualization

Professional Experience

Engineering, product, and research

Ultralytics

Associate Software Engineer

Aug 2024 – Present

Remote

Built real-time collaboration with FastAPI WebSockets + Redis for concurrent multi-user editing (sub-100ms perceived latency).
Designed conflict-safe state updates using optimistic concurrency (ETags) to preserve integrity under race conditions.
Integrated Segment Anything Model (SAM) for auto-labeling, reducing manual annotation time by ~40% on vision datasets.
Orchestrated CI/CD workflows for the open-source repo (53.4k stars), improving release reliability for global users.

GitHub Platform YOLO11

Esri R&D Center

Product Engineer Intern

Apr 2024 – Aug 2024

Beijing, China

Improved ArcGIS Earth 3D interaction by profiling rendering bottlenecks and reducing frame drops.
Prototyped a GPT-4o geospatial assistant to automate SQL-style queries and map generation from natural language.
Strengthened product stability through regression testing and code review practices for enterprise releases.

ArcGIS Earth

Academia Sinica

Undergraduate Researcher

Jul 2023 – Dec 2024

Taipei, Taiwan

Researched automated feature extraction for historical maps by combining YOLOv5 with Segment Anything (SAM), achieving an F1 Score of 0.90.
Built a full-stack vectorization & verification system (Flask, PostgreSQL, Docker, OpenLayers).

History Map Project

Projects

Selected work — research-driven, visual, and interactive.

Ultralytics Platform

End-to-End Computer Vision Platform (2024 - Present)

A comprehensive, end-to-end computer vision platform that streamlines the ML workflow from data preparation to model deployment. As an Annotation Lead, I contributed to building an ecosystem that supports dataset management, SAM-based smart annotation, YOLO auto-labeling, cloud training, and global model deployment.

Led annotation tools development including Manual, SAM smart annotation, and YOLO auto-labeling across 5 task types.
Scalable infrastructure managing 4K+ datasets, 14M+ images, and deploying to 43 global regions.
Comprehensive ML lifecycle support: Upload, Annotate, Train (22 cloud GPUs), Export (17 formats), and Deploy.

Platform Documentation

Platform analytics dashboard showing user growth and global distribution

Dataset management and processing workflow

SAM smart annotation and manual labeling tools

Cloud training configuration and real-time metrics

One-click model deployment to global regions

In-browser model inference and testing

Real-time model inference API endpoint

Activity dashboard tracking project events

Account billing and credits management

Historical Map of Taiwan Based on GeoAI

TGIS Best Paper

YOLOv5 + SAM (2023) · YOLOv8 Instance Segmentation (2024)

Developed an automated pipeline for digitizing historical maps using GeoAI. By advancing from YOLOv5+SAM to YOLOv8 Instance Segmentation, the system significantly improves vectorization quality, addressing the critical bottleneck of manual feature extraction in digital humanities and historical geography.

Labeled 8 land-use classes and synthesized training data via random crop-composition to improve generalization.
Upgraded from object detection to instance segmentation, improving the quality of map feature extraction.
Result visualizations include detection, labeling, and prediction comparisons; packaged as a PDF report.

Paper (PDF)

Detection

Label

Prediction

System interface

Result (1)

Result (2)

Pmap — The Map for Precipitation 🌦️

Crowdsourced Reports + CWA Data · Subscription & Notifications · Cloud-Native

Designed and implemented a cloud-native platform that fuses crowdsourced reports with official meteorological data to deliver real-time, high-precision precipitation mapping. The system resolves the challenge of localized weather prediction through scalable infrastructure and an interactive, real-time notification engine.

Fusion of public rain reports + CWA data to compute more accurate precipitation conditions.
Rain reporting flow with photos + real-time map experience (Leaflet) and dark mode.
Subscription & notification system: region-based new reports, scheduled rain updates, and threshold-based alerts (in-app + email).

GitHub Repo

Pmap demo: reporting, map interaction, subscriptions, notifications

System architecture (frontend / backend / notification / cloud)

CI pipeline (GitHub Actions + Docker build/test)

CD pipeline (deployment workflow / Portainer)

Real-time precipitation map (CWA + public reports)

Subscribe to fixed locations / regions

Configure schedules & thresholds

In-app notifications + email alerts

YOLO11.com

Real-time Inference · TensorFlow.js · Backend-free PoC

A demo website showcasing real-time object detection using purely TensorFlow.js without any backend dependencies. The Proof of Concept directly connects to the user's WebCam and is highly optimized to achieve over 80 FPS directly in the browser.

Purely browser-based inference using tfjs (no backend server required).
Direct WebCam integration for real-time video stream processing.
High-performance execution achieving over 80 FPS in-browser.

Demo Website

Real-time object detection (>80 FPS) using purely TensorFlow.js

Real-time object detection using TensorFlow.js

Taiwan Public Bicycle System Analysis

1st Place, National Competition

Time Series + Interactive Mapping (2021) · Spatio-temporal Insights (2024)

Created an end-to-end spatial-temporal analysis framework to investigate public bicycle usage patterns. By combining time-series clustering and Random Forest feature evaluation, this study reveals critical insights into last-mile transportation dynamics and urban mobility integration.

Automated scraping + longitudinal station dataset (pandas) for trend analysis over time.
Interactive Folium map with per-station matplotlib time-series popups for exploration.
2024 analysis: DBSCAN station development, last-mile + MRT transfer issues, Random Forest feature impact; won 1st Place in the National Storymaps Competition.

GitHub (YouBikeAnalysis)StoryMaps (2024)Demo Website

Space-time cube for spatio-temporal dynamics

MRT transfer / bike relationship analysis

Last-mile & MRT transfer relationship insights

Riding route visualization (OD patterns)

Random Forest feature set (1)

Random Forest feature set (2)

Flow visualization of YouBike data

Folium popup embedding matplotlib time series

Heat map of station activity / demand patterns

Taiwan Convenience Store Analysis

1st Place, National Competition

Spatial Pattern (2021) · Spatio-temporal Prediction (2023)

Investigated the Hotelling's spatial competition model in urban retail networks and developed a spatio-temporal prediction pipeline. This work provides an analytical framework for identifying optimal facility locations, balancing competition and coverage in metropolitan planning.

ANN-based spatial pattern analysis for five major convenience store chains (Taipei metro).
Spatio-temporal prediction built with suitability analysis, space-time cube, and MGWR; validated statistically.
Won 1st Place in the National Storymaps Competition; detailed Medium write-up linked.