CV — Robotics Software Engineer
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TL;DR — Robotics software engineer with deep field experience in multi-sensor data pipelines, LiDAR + camera synchronization, and real-time perception systems. Shipped fixed-LiDAR + camera pipelines for highway traffic monitoring with NYC agencies, built drone and climbing-robot inspection stacks on ROS / NVIDIA Jetson, and own the end-to-end loop from sensor calibration through ingestion, recording, and downstream ML.
Core skills
Sensors & perception LiDAR (mechanical and solid-state, multi-beam), multi-camera rigs, depth cameras, IMU, hyperspectral, infrared, underwater acoustic. Temporal synchronization (hardware triggers, PTP / NTP timestamp alignment, post-hoc time-correction), spatial calibration (intrinsic/extrinsic, LiDAR–camera cross-calibration, multi-camera rigs), sensor fusion for detection and tracking.
Robotics stack ROS / ROS 2, ROS bag and MCAP recording, Protobuf for sensor message schemas, NVIDIA Jetson (Nano/TX2/Xavier) edge deployment, Raspberry Pi, Arduino, embedded Linux, DepthAI / OAK camera platforms (working knowledge), real-time control (PID, fractional-order, visual servoing).
Software Python (primary), C++, MATLAB, Fortran. Linux/Ubuntu, Docker, AWS (S3, EC2, model serving), CUDA, OpenCV, PyTorch, TensorFlow. Git, CI/CD for research-to-production pipelines.
Calibration & data systems Multi-camera + LiDAR extrinsic calibration workflows, time-sync diagnostics, high-throughput ingestion, MCAP/Protobuf-based recording, dataset format conversion across configurations, background subtraction and segmentation pipelines.
Selected projects (synchronization & multi-sensor focus)
Highway Traffic Monitoring — Fixed LiDAR + Camera Pipeline · 2024–present
AI & Mobility Research Lab, CCNY · NYC DOT collaboration
- Designed and deployed an end-to-end multi-sensor capture pipeline for fixed roadside LiDAR + camera at NYC highway sites, including temporal alignment of LiDAR frames with RGB streams and spatial calibration for downstream 2D-3D fusion.
- Built background subtraction, object segmentation, and CNN-based detection stages on top of synchronized frames; classified vehicles, motorcycles, bicycles, and pedestrians (vulnerable road users).
- Engineered multi-frame vehicle reconstruction that stitches successive sweeps into per-vehicle point cloud models — explicit dependency on tight time-sync between sensor and ego-clock.
- Built a configuration-portable training/inference loop so models trained on one site’s sensor configuration can be retargeted to another with different beam counts, mounting geometry, and frame rates.
- Co-authored two MobiSPC 2025 papers on LiDAR beam-count requirements for VRU detection and a broader sensing-perspectives survey.
Stack: Python, PyTorch, ROS, LiDAR SDKs, OpenCV, calibration toolchains, NYC field deployments.
Bridge Inspection Robot Deployment System (BIRDS) · 2020–2024
Missouri S&T → CCNY Robotics Lab
- Engineered a drone + robotic-arm clamping system for autonomous bridge inspection — included vision-based girder detection on NVIDIA Jetson, PID and fractional-order controllers for the clamping mechanism, and an iOS app for human-in-the-loop control.
- Owned hardware-software integration across multi-camera (visible / infrared / hyperspectral) UAS payloads, including capture, time-tagging, and downstream signal processing.
- Built supporting underwater robot-assisted acoustic imaging rig for bridge scour evaluation on ROS + Arduino + embedded Linux.
- Patent: Unmanned vehicle having flight configuration and surface traverse configuration (US 12,296,994, granted 2025).
Advanced Bridge Inspection Automation · 2022–present
CCNY Robotics Lab
- Trained and deployed CNNs for crack / spalling / stain detection from inspection imagery; pushed models to AWS for scalable cloud inference.
- Built a custom WebODM-based platform integrating segmentation, 3D reconstruction, interactive visualization, and crack measurement — sensor data in, structured defect output out.
- Selected for IEEE IROS 2025 presentation (Feng, Shang, et al., IEEE T-ASE, 2025).
Earlier robotics work · 2010–2019
- 2015–2019: Drone visual servoing with fractional-order control (Raspberry Pi, embedded Linux).
- 2015–2017: SmartCaveDrone — sense-and-avoid + GPS-denied UAV navigation in cave environments.
- 2013–2014: ROS + AR.Drone object tracking quadrotor.
- 2012–2013: Indoor quadrotor UAV with LiDAR (early multi-sensor work).
- 2014 International Aerial Robotics Competition — Best System Control + Best Mission Planning awards (team lead).
Education
- PhD, Civil Engineering (Transportation) — CCNY, NY, USA · 2025–present
- PhD, Pattern Recognition and Intelligent Systems — Northeastern University, China · 2013–2020
- Exchange PhD — UC Merced · 2015–2017 (Mechatronics Embedded Control Systems Lab)
- MEng, Pattern Recognition and Intelligent Systems — Northeastern University, China · 2011–2013
- BEng, Automation — Northeastern University, China · 2007–2011
Experience
- 2025–now — Postdoctoral Scholar, AI & Mobility Research Lab, CUNY City College
- Dec 2022–Dec 2024 — Postdoctoral Researcher, CCNY Robotics Lab
- Jan 2020–Nov 2022 — Postdoctoral Fellow, Missouri University of Science and Technology
- 2015–2017 — Lecturer / Junior Specialist, UC Merced — courses on Mechatronics and Unmanned Aerial Systems; co-designed UAS lab curriculum
- Multiple adjunct / instructor roles teaching Robotics, Mechanics and Control (Vaughn College), Mechatronics, UAS, and Electric Circuits (CUNY City)
Patents
- Chen, Reven, Shang, et al. Unmanned vehicle having flight configuration and surface traverse configuration. US 12,296,994, granted May 2025.
- Wu, Shang, et al. Data/image transmission device based on TCP/IP. CN 102427464 B.
- Zhang, Shang, et al. Internet-based interactive digital media terminal device. CN 102306237 A.
Certifications
- FAA Part 107 — Remote Pilot Certificate for Small Unmanned Aircraft Systems (2016).
Awards (selected)
- 2014 — Best System Control and Best Mission Planning, International Aerial Robotics Competition (AUVSI Foundation, USA). Team lead.
- 2010 — First Prize, Northeastern Region, National Smart Car Competition, Freescale.
- 2025–2030 — PhD Fellowship, CCNY Civil Engineering (Transportation).
Why this role fits
I’ve been the engineer who actually deploys the LiDAR rig at the side of a NYC highway, runs the time-sync diagnostics when the camera and LiDAR disagree by 30 ms, recalibrates the extrinsics, and then trains the detector on whatever messy data the system produced. The multi-camera ingestion + synchronization + calibration + recording + downstream model loop is exactly where I’ve been living for the last several years — and what I want to keep building.
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