Zengyi Qin 秦增益

qinzy [at] mit.edu

I am a first-year graduate student at Massachusetts Institute of Technology advised by Prof. Chuchu Fan. I obtained my B.E. degree of Electronic Engineering at Tsinghua University.


  • Tsinghua University, 2016 - 2020
    Bachelor of Engineering, Electronic Engineering
    Advisor: Prof. Jiansheng Chen


Learning Safe Multi-agent Control with Decentralized Neural Barrier Certificates
Zengyi Qin, Kaiqing Zhang, Yuxiao Chen, Jingkai Chen and Chuchu Fan
The International Conference on Learning Representations (ICLR), 2021

We study the multi-agent safe control problem where agents should avoid any collision while reaching their goals. Our method can scale up to an arbitrarily large number of agents (e.g., >1000 in our experiments) and achieve a 99-100% safety rate.

paper | video | code | website

Controller synthesis for linear system with reach-avoid specifications
Chuchu Fan, Zengyi Qin, Umang Mathur, Qiang Ning, Sayan Mitra, and Mahesh Viswanathan
IEEE Transactions on Automatic Control (TAC), 2021

We address the problem of synthesizing provably correct controllers for linear systems with reach-avoid specifications. Our solution decomposes the overall synthesis problem into two smaller and more tractable problems, achieving a 2-150 times speedup compared with the previous techniques.


Weakly Supervised 3D Object Detection from Point Clouds
Zengyi Qin, Jinglu Wang and Yan Lu
ACM Multimedia (ACM MM), 2020

A state-of-the-art framework for weakly supervised 3D object detection from point clouds without using any ground truth 3D bounding box for training. The core of our method is the unsupervised 3D object proposal module and the cross-modal knowledge distillation strategy.

paper | code

Learning fine-grained estimation of physiological states from coarse-grained labels by distribution restoration
Zengyi Qin, Jiansheng Chen, Zhenyu Jiang, Xumin Yu, Chunhua Hu, Yu Ma, Suhua Miao and Rongsong Zhou, Scientific Reports, 2020

Our method allows machine learning algorithms to perform fine-grained estimation of physiological states (e.g., sleep depth) even if the training labels are coarse-grained.

paper | code

KETO: Learning Keypoint Representations for Tool Manipulation
Zengyi Qin, Kuan Fang, Yuke Zhu, Li Fei-Fei and Silvio Savarese
The International Conference on Robotics and Automation (ICRA), 2020

KETO is a framework for robots to manipulate unseen objects as tools to complete diverse tasks. We proposed a method to learn the keypoint representations of objects, which simplify the manipulation task and improve the generality to novel objects.

paper | video | website

Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
Zengyi Qin, Jinglu Wang and Yan Lu
The International Conference on Computer Vision and Pattern Recognition (CVPR), 2019

This is a pioneering work on stereo image based 3D object detection without calculating the pixel-level depth maps. We proposed a triangulation learning method to learn the object-level stereo geometric correspondence for 3D object detection.

paper | video | code | website

MonoGRNet: A General Framework for Monocular 3D Object Detection
Zengyi Qin, Jinglu Wang and Yan Lu
The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021

A general monocular 3D object detection framework that flexibly adapts to both fully and weakly supervised learning, which alleviates the need of extensive 3D labels and only requires ground truth 2D bounding boxes during training.


MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization
Zengyi Qin, Jinglu Wang and Yan Lu
The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019, Oral Presentation, Acceptance Rate < 8%

A state-of-the-art monocular 3D object detection approach based on geometric reasoning. We proposed to decompose the whole task into four progressive sub-tasks that significantly facilitates the monocular 3D object detection.

paper | video | code | website

sEMG based Tremor Severity Evaluation for Parkinson's Disease using a Light-weight CNN
Zengyi Qin*, Zhenyu Jiang*, Jiansheng Chen, Chunhua Hu and Yu Ma
IEEE Signal Processing Letters (SPL), 2019

A machine learning framework to assist the diagnosis of Parkinson's Disease by assessing the pathological tremor. We proposed a light-weight convolutional neural network and a similarity learning strategy to handle the scarcity of medical data.

paper | website


  • MathWorks Fellowship, 2021
  • The highest award of Beijing Challenge Cup (首都大学生挑战杯特等奖), 2019
  • The highest award of Tsinghua Challenge Cup (清华大学挑战杯特等奖), 2019
  • Comprehensive Excellence Scholarship, Tsinghua University, 2018
  • The First Prize of Microsoft Imagine Cup, China Finals, 2018