MultiGrid Multi-Agent PPO
Lightweight multi-agent PPO experiments on MultiGrid with shaped rewards, sliding-window checkpoints, and automated trajectory visualizations
Posts by Collection
portfolio
CostalSeg Coastal Erosion Platform
UW applied project for local coastal erosion monitoring with DeepLabV3+ image segmentation, ViT-H-14 quality control, and SuperGlue alignment
Unity VR 3DGS Viewer · Meta Quest 2
A Unity-based Meta Quest 2 VR app for browsing 3D Gaussian Splatting scenes.
RAG Chat Bot
This chatbot uses a multi-agent system to answer questions about machine learning concepts. Link
Auto Bang
High speed target detection and playing simulation in Android simulator for automatic music game playing
Semantic Search Demo
This demo is used to validate text categorization. Demo Page
CIDAUT AI Fake Scene Classification 2024
Classify if an autonomous driving scene picture is real or fake. Website
ControlNet + LoRA Line-art colorization workflow
This is my project in UW’s EEP596 computer vison course. Slide & Paper
publications
AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration
Published in , 2025
While multi-vehicular collaborative driving demonstrates clear advantages over single-vehicle autonomy, traditional infrastructure-based V2X systems remain constrained by substantial deployment costs and the creation of “uncovered danger zones” in rural and suburban areas. We present AirV2X-Perception, a large-scale dataset that leverages Unmanned Aerial Vehicles (UAVs) as a flexible alternative or complement to fixed Road-Side Units (RSUs). Drones offer unique advantages over ground-based perception: complementary birds-eye-views that reduce occlusions, dynamic positioning capabilities that enable hovering, patrolling, and escorting navigation rules, and significantly lower deployment costs compared to fixed infrastructure. Our dataset comprises 6.73 hours of drone-assisted driving scenarios across urban, suburban, and rural environments with varied weather and lighting conditions. The AirV2X-Perception dataset facilitates the development and standardized evaluation of Vehicle-to-Drone (V2D) algorithms, addressing a critical gap in the rapidly expanding field of aerial-assisted autonomous driving systems. The dataset and development kits are open-sourced at this https URL.
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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