Chen Feng
· Assistant Professor of Civil EngineeringNew York University · Computer Science and Engineering
Active 1979–2025
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About
Chen Feng is an Institute Associate Professor at New York University, where he is also the Director of the AI4CE Lab and a Founding Co-Director of the NYU Center for Robotics and Embodied Intelligence (CREO). His research focuses on active and collaborative robot perception and robot learning to address multidisciplinary, use-inspired challenges in construction, manufacturing, and transportation. Prior to joining NYU, he worked as a research scientist in the Computer Vision Group at Mitsubishi Electric Research Laboratories (MERL) in Cambridge, Massachusetts, developing patented algorithms for localization, mapping, and 3D deep learning in autonomous vehicles and robotics. Chen Feng earned his doctoral and master's degrees from the University of Michigan between 2010 and 2015, and his bachelor's degree from Wuhan University in 2010. He is an active contributor to the AI and robotics communities, participating in conferences such as CVPR, IEEE RA-L, and ICRA, where he has served as an area chair and associate editor. In 2023, he was awarded the NSF CAREER Award. His research at NYU involves multidisciplinary, use-inspired projects aimed at advancing robotics and AI, including developing algorithms and systems for intelligent agents to understand and interact with materials and humans in dynamic and unstructured environments. His work addresses infrastructure challenges on Earth and beyond, such as construction robotics, manufacturing automation, and autonomous vehicles.
Research topics
- Artificial Intelligence
- Computer Science
- Data Mining
- Computer vision
- Computer Security
- Algorithm
- Theoretical computer science
- Computer network
- Human–computer interaction
- Transport engineering
Selected publications
IEEE Signal Processing Magazine · 2020 · 231 citations
We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles (AVs), lidar sensors collect 3D point clouds that precisely record the external surfaces of objects and scenes. The tools for 3D point cloud processing and learning are critical to the map creation, localization, and perception modules in an AV. Although much attention has been paid to data collected from cameras, such as images and videos, an increasi…
Real-Time Soft Body 3D Proprioception via Deep Vision-Based Sensing
IEEE Robotics and Automation Letters · 2020 · 51 citations
Senior authorCorrespondingSoft bodies made from flexible and deformable materials are popular in many robotics applications, but their proprioceptive sensing has been a long-standing challenge. In other words, there has hardly been a method to measure and model the high-dimensional 3D shapes of soft bodies with internal sensors. We propose a framework to measure the high-resolution 3D shapes of soft bodies in real-time with embedded cameras. The cameras capture visual patterns inside a soft body, and a convolutional neur…
Collaborative Multi-Object Tracking With Conformal Uncertainty Propagation
IEEE Robotics and Automation Letters · 2024-02-09 · 28 citations
articleObject detection and multiple object tracking (MOT) are essential components of self-driving systems. Accurate detection and uncertainty quantification are both critical for onboard modules, such as perception, prediction, and planning, to improve the safety and robustness of autonomous vehicles. Collaborative object detection (COD) has been proposed to improve detection accuracy and reduce uncertainty by leveraging the viewpoints of multiple agents. However, little attention has been paid to ho…
Robust Collaborative Perception without External Localization and Clock Devices
2024-05-13 · 8 citations
articleA consistent spatial-temporal coordination across multiple agents is fundamental for collaborative perception, which seeks to improve perception abilities through information exchange among agents. To achieve this spatial-temporal alignment, traditional methods depend on external devices to provide localization and clock signals. However, hardware-generated signals could be vulnerable to noise and potentially malicious attack, jeopardizing the precision of spatial-temporal alignment. Rather than…
IEEE Robotics and Automation Letters · 2024-11-11 · 4 citations
articleSenior authorVisual place recognition (VPR) using deep networks has achieved state-of-the-art performance. However, most of them require a training set with ground truth sensor poses to obtain positive and negative samples of each observation's spatial neighborhood for supervised learning. When such information is unavailable, temporal neighborhoods from a sequentially collected data stream could be exploited for self-supervised training, although we find its performance suboptimal. Inspired by noisy label l…
Recent grants
NSF · $398k · 2021–2024
NSF · $900k · 2020–2026
CPS: Medium: Accurate and Efficient Collective Additive Manufacturing by Mobile Robots
NSF · $1.2M · 2019–2024
Frequent coauthors
- 25 shared
Vineet R. Kamat
- 22 shared
Shaojie Shen
University of Hong Kong
- 18 shared
Siheng Chen
Shandong Jiaotong University
- 16 shared
Fei Gao
- 14 shared
Yiming Li
New York University
- 14 shared
John‐Ross Rizzo
- 12 shared
Haojia Li
University of Hong Kong
- 11 shared
Midori Sugaya
Shibaura Institute of Technology
Labs
AI4CE LabPI
Awards & honors
- NSF CAREER Award (2023)
- New York City's Vision Zero Research Award
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