Shuo Zhang

Shuo Zhang

Curriculum vitae · PhD Candidate, New York University

shuo.z@nyu.edu · New York, USA
shuo9898-zs.github.io
Download CV (PDF)

Research interests

Human–computer interaction; VR-based driving simulation; multimodal human and vehicle sensing; autonomous driving; imitation learning and motion planning.

Education

New York University

PhD, Transportation System

Sep 2023 – May 2027 (expected)

University of California, Los Angeles

MS, Transportation Engineering

Sep 2021 – Mar 2023

Dalian Jiaotong University

BE, Civil Engineering

Sep 2016 – Jun 2020

Research & industry experience

New York University

Graduate Researcher · New York, NY

Sep 2023 – Present

  • Built a dual-user VR–CARLA platform for multimodal data collection on driver–worker interactions in safety-critical, out-of-distribution driving scenarios.
  • Analyzed driving behavior, gaze, physiological signals, and questionnaires; developed ego-vehicle trajectory prediction models using multimodal work-zone driving data.
  • Developing a co-simulation testbed for reinforcement learning-based motion planning, including closed-loop traffic, a PPO baseline, reward design, and generalization tests.

Cepton Inc.

LiDAR Intern · San Jose, CA

Jun – Sep 2022

  • Developed two Python/C tools for real-time LiDAR health monitoring of temperature, voltage, and data rate, replacing Wireshark-based diagnostics.
  • Automated thermal and vibration testing using USB relays, Arduino, and Raspberry Pi, following ISO and GM standards.
  • Designed and conducted LiDAR validation experiments, including optical-module checks, image analysis, burn-in quality control, and data visualization.

Selected research projects

DuoRoad

A dual-participant simulation platform for studying driver–worker interaction in safety-critical road scenarios, with human motion, gaze, vehicle sensing, and CARLA–SUMO co-simulation.

WZDrive

Human-in-the-loop driving data and trajectory prediction in work zones. The dataset captures 1,183 trajectories from 33 drivers across six scenario conditions and three work-zone configurations.

WZ-Plan

A configurable CARLA–SUMO testbed with six scenario families for training and evaluating a PPO motion planner with a structured spatial-temporal scene encoder.

Manuscripts & ongoing work

Published papers

Technical skills

Programming & ML
Python, C++, MATLAB, PyTorch, OpenCV
Simulation & development
CARLA, SUMO, ROS, Unreal Engine 4/5, Unity, VR/AR, Wireshark
Experimentation & analysis
Hardware and ML experiments, human-subject studies, data collection, statistical analysis

Presentations & recognition

  • Invited presentation on a VR-based dual-user driving simulation platform at New York City’s Vision Zero program.
  • Oral presentation at ACM AutomotiveUI.
  • Second place in the poster competition at the ITS-NY Annual Meeting.
  • Presented research on road safety simulation in Kentucky.
  • Presented interactive VR transportation research at National STEM Day with NYU biLAB.