Ruixuan Zhang

ruixuan.zhang@nyu.edu

NYU Tandon School of Engineering

Brooklyn, NY 11201

My research lies at the intersection of urban science and AI, aiming to transform cities into responsive human habitats. I utilize computer vision, deep learning, and generative AI to decode complex urban environments, developing algorithms and tools that translate large-scale city data into actionable insights.

I am a Ph.D. candidate in Transportation Systems at New York University, advised by Prof. Chen Feng (Amazon Scholar) and Prof. Kaan Ozbay. My focus areas include AI for urban mobility, computer vision, and generative AI. Before joining NYU, I earned dual M.S. degrees in Industrial & Operational Engineering and Intelligent Systems from the University of Michigan, Ann Arbor, where I worked with Prof. Neda Masoud on connected and automated vehicles.

selected publications

arXiv
Cvic: Learning Conformal Uncertainty for Video Individual Counting

R. Zhang and others

arXiv preprint, 2026

Abs Bib

We model feature-matching uncertainty in entropy-regularized optimal transport and propagate it to counting uncertainty under KKT-based analysis. A Transformer-based model predicts OT slack variables, achieving statistical coverage guarantees for count intervals and +38% accuracy improvement.
@article{zhang2026cvic, title = {Cvic: Learning Conformal Uncertainty for Video Individual Counting}, author = {Zhang, R. and others}, year = {2026}, journal = {arXiv preprint}, }
ITSC
Learning When to See for Long-Term Traffic Data Collection on Power-Constrained Devices

R. Zhang, Wentao Han, Zhuoran Bian, and 2 more authors

In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023

Abs Bib

We propose a reinforcement learning framework for adaptive scheduling of traffic data collection on battery-powered edge devices, balancing data quality with energy constraints for long-term deployment.
@inproceedings{zhang2023itsc, title = {Learning When to See for Long-Term Traffic Data Collection on Power-Constrained Devices}, author = {Zhang, R. and Han, Wentao and Bian, Zhuoran and Ozbay, Kaan and Feng, Chen}, booktitle = {2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)}, pages = {4676--4682}, year = {2023}, organization = {IEEE}, }
AAP
When Language and Vision Meet Road Safety: Leveraging Multimodal Large Language Models for Video-Based Traffic Accident Analysis

R. Zhang, Binhao Wang, Jiaxin Zhang, and 3 more authors

Accident Analysis & Prevention, 2025

Abs Bib

We develop a decompose-then-aggregate framework for long-horizon video reasoning using MLLMs (GPT-4o), improving coverage under limited context windows. Adaptive visual prompting improves zero-shot video classification and object grounding on the Woven Traffic Safety Dataset.
@article{zhang2025aap, title = {When Language and Vision Meet Road Safety: Leveraging Multimodal Large Language Models for Video-Based Traffic Accident Analysis}, author = {Zhang, R. and Wang, Binhao and Zhang, Jiaxin and Bian, Zhuoran and Feng, Chen and Ozbay, Kaan}, journal = {Accident Analysis \& Prevention}, volume = {219}, pages = {108077}, year = {2025}, publisher = {Elsevier}, }