Postdoctoral Scholar · UCF
Postdoctoral Scholar · University of Central Florida

Building efficient autonomous systems — from sensor co-design to urban-scale mobility.

I build efficient perception for autonomous vehicles and AI-powered mobility — from real-time BEV distillation and RAW-to-task sensing co-design to generative AI for urban digital-twin simulation. Postdoctoral Scholar at UCF (CECS) under Dr. Xishun Liao.

Real-Time BEV Sensor Co-Design Knowledge Distillation Digital Twins Mobility AI VLA Policy
About

Focused on efficient autonomy from sensor to decision.

My work connects perception models, sensing design, and deployment constraints. I am interested in systems that can reason reliably under noise, limited compute, and the mismatch between clean benchmarks and field conditions.

I study how to make autonomous perception pipelines smaller, faster, and more robust — from BEV distillation and multimodal fusion to RAW-to-task sensing co-design and VLA policies under sensor degradation. As a Postdoctoral Scholar at UCF, I am extending this work into AI-powered urban mobility, spatiotemporal trajectory modeling, and generative digital-twin simulation.

From edge-deployable perception to city-scale mobility models — always deployment-first, task-driven, and experimentally grounded.

Core Themes BEV autonomy, sensing co-design, mobility AI, digital twins
Current Direction AI-powered mobility, spatiotemporal trajectory modeling & generative urban simulation (UCF)
Research Style Deployment-first, task-driven, experimentally grounded
Recent Activity

News, acceptances, and milestones.

A compact running timeline of papers, awards, and academic progress.

Jul 2026
🏢
Joining the University of Central Florida (CECS) as a Postdoctoral Scholar under Dr. Xishun Liao, focusing on AI-powered mobility, digital twins, and spatiotemporal trajectory modeling.
Jun 2026
🚗
New preprint: Beyond Bayer - Task-Optimal Sensor Co-Design for Robust Autonomous Driving Segmentation.
Mar 2026
🏆
TinyBEV accepted as a poster at CVPR DriveX Workshop 2026 (avg. reviewer score 8.0/10).
Jan 2026
📄
L2S Driving arXiv preprint released and submitted to ECCV Workshop 2026: Learning to Sense for Driving.
Jan 2026
Paper accepted: Adaptive Extensions of Unbiased Risk Estimators for Unsupervised MRI Denoising (CVC 2026).
Dec 2025
🎓
Successfully defended Ph.D. dissertation: Efficient Deep Neural Networks for Autonomous Perception.
Jan 2025
🚗
Paper published: TinyBEV (ICCV WDFM 2025).
Jan 2025
🧠
Paper published: From Noise Estimation to Restoration (VISAPP 2025).
Jan 2025
🧾
Paper published: Learning From Oversampling (IEEE Access 2024).
Jan 2024
🏅
Award: Reginald R. Baxter Graduate Fellowship (2024).
Jan 2023
🏅
Award: EECS Graduate Fellowship, University of Arkansas (2023).
Jan 2022
🏅
Award: College of Engineering Graduate Fellowship, University of Arkansas (2022).
Jan 2019
🌏
Scholarship: Chinese Government Belt and Road Scholarship (2019).
Research

Four problem spaces shaping the work.

Each thread is tied to deployable autonomy — from the sensor to the city — rather than benchmark-only performance.

01

Efficient 3D perception and BEV autonomy

Distilled and modular BEV pipelines for real-time perception, planning, and VLA policies under strict compute budgets and sensor degradation.

02

Robust learning under noisy sensing

Unsupervised denoising, restoration, and risk-aware learning when labels are scarce and measurements are corrupted.

03

Sensing-aware co-design

Joint optics-sensor-model design that optimizes the imaging stack itself — CFA, noise model, and segmentation head — for downstream autonomous tasks.

04

AI-powered mobility and digital twins

Spatiotemporal trajectory mining, generative AI for human/vehicle digital-twin simulation, and controllable urban scenario generation.

Selected Work

Publications

6 total 3 published 1 accepted 2 in review
Professional Service

Reviewer activity & certifications

Peer Review ICML SCALE Workshop, 2026
Peer Review IEEE ICASSP, 2026
Peer Review ECML-PKDD, 2026
Peer Review IEEE Big Data, 2022
Certification NVIDIA Accelerated Computing with CUDA Python (2025)
Certification Generative AI with Diffusion Models, NVIDIA (2025)
Project Demos

Selected videos

Demo 1
Demo 2
Visitors

Visitor map and country counter

World Map
Country Counter
Flag Counter
Contact

Open to research collaborations and technical conversations.