Ling-Hao CHEN     

Hi here, I'm Ling-Hao CHEN (陈凌灏 in Chinese, Evan in English)! Now, I am now an incoming Ph.D. student of Tsinghua University, advised by Prof. Harry Shum. I obtained my Bachelor degree from School of Computer Science and Technology at Xidian University in 2022. My research interest includes Graph representation learning, Anomaly detection, Machine Learning and Computer Vision. I am a student member of IEEE.

Address: Room 3901, Building 1, Chang Fu Jin Mao Tower, 5 Shihua Road, Futian District, Shenzhen, P.R. China.

Motto: Seek the truth, practice real skills and do real things! (求真学问,练真本领,做真东西!)

Email  /  GitHub  /  Zhihu(知乎)  /  WeChat(公众号) /  Google Scholar

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Education
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Shenzhen International Graduate School, Tsinghua University, Shenzhen, P.R. China
Ph. D., from Sep. 2022 (expected)
Majored in Computer Science and Technology.

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School of Computer Science and Technology, Xidian University, Xi'an, P.R. China
B. Eng., from Sep. 2018 to June 2022
Majored in Software Engineering. Rank: 1/398 (less than 1%); GPA: 3.9/4.0.

Awards

  • June 2022, Outstanding Graduate Scholarship (the ONLY student in the S.E. department of XDU).
  • Dec. 2021, Principal Scholarship (Only FIVE students in Xidian University) & Fisrt-class CASC Scholarship (the ONLY student in Xidian University).
  • 2020 ~ 2021, 2019 ~ 2020, 2018 ~ 2019, National Scholarship(<1%), School-level outstanding student model(<1%). (For three years in my undergraduate life!)
  • Sep. 2020, The National College Student Mathematical Modeling Competition (National level), First Prize(<0.7%) [details].
  • Apr. 2021, MCM/ICM, Meritorious Winner.
  • Apr. 2020, MCM/ICM, Honorable Mention.
  • Sep. 2019, The National College Student Mathematical Modeling Competition (Shaanxi Division), First Prize.
  • Sep. 2019, The 12th College Student Mathematics Competition in Shaanxi Province, First Prize.
  • Oct. 2020, The 12th National College Student Mathematics Competition, Second Prize.
  • Oct. 2019, The 11th National College Student Mathematics Competition, Second Prize.

My Research Interests

    My research interests: Graph Representation Learning, Anomaly Detection, Computer Vision (Talking Face Generation), Disentanglement.
    I also follow other research topics: Optimization Algorithm, Trustworthy Machine Learning, Domain Generalization, Out of Distribution Generalization, and The First Principle of Deep Neural Networks.

  • Graph Representation Learning (Especially GNNs)
  • With the development of deep learning, people try to use vector, matrix or other forms to represent the nodes in a graph. The representation learning of graph is mainly composed of Graph Embedding and Graph Neural Networks (GNNs) and I mainly focus on GNNs. The mainstream technologies of GNNs can be divided into spectral methods and spatial methods. Although most spatial methods seem to be an easier way to represent, I think the improvement of GNNs' expressive ability depends on spectral methods. Most people concentrate on the low frequency signals in graph filters, but rare notice that high frequency signals are also meaningful to us. There are still many problems need us to solve, like over-smoothing, designing a "deep" GNN, improving its expressive ability and so on.

  • Anomaly Detection on Graph
  • Anomaly Detection developed rapidly in past decades, but graph-based anomaly detection technologies have only attracted attention in recent years. In graph-based anomaly detection, I think GNNs can have a lot of room to develop. As a non-linear high-dimensional function, GNNs do not rely on assumptions of anomalous data distribution (such as Gaussian distribution, outlier assumption, etc.), but act as low-pass filters to effectively filter the high-frequency components in the anomalous data. Is it a good thing that GNNs filter out anomalous high-frequency signals? I think the answer is "YES" and my latest work (now in review) is to answer this question. Besides, there are also many problems for us to solve about this task.

Publications

          The full list of publications can be obtained via My Google Scholar.
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DetectorNet: Transformer-enhanced Spatial Temporal Graph Neural Network for Traffic Prediction.
He Li, Shiyu Zhang, Xuejiao Li, Hongjie Huang, Liangcai Su, Duo Jin, Linghao CHEN, Jianbin Huang and Jaesoo Yoo.
In International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL) 2021.

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AnomMAN: Detect Anomaly on Multi-view Attributed Networks.
Ling-Hao CHEN, He Li and Wenhao Yang.
Arxiv Preprint 2022.

Student Work
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I has been the chairman of the Inspur Student Club (ISC) [HomePage] of Xidian University from Sep. 2020 to Sep. 2021. I was also mainly responsible for the Machine Learning and Data Mining Group in the club.

Resource
News
  • June 2022. I defended my thesis "Audio-driven Talking Head Reenactment Algorithm" successfully, and the thesis was selected as the OUTSTANDING thesis of XDU. Thanks to Dr. Harry Shum, Dr. Baoyuan Wang, Dr. Lei Zhang, and Mr. Duomin Wang!
  • Jan. 2022. A research talk at IDEA related to Audio-driven Talking Face Generation [slides].
  • Dec. 2021. I was awarded the Principal Scholarship (only FIVE students in Xidian University).
  • Dec. 2021. I was awarded the Fisrt-class CASC Scholarship of China Aerospace Science and Technology Corporation (the ONLY student in Xidian University).
  • Sep. 2021. I was awarded the National Scholarship 2020-2021.
  • Aug. 2021. One paper "DetectorNet: Transformer-enhanced Spatial Temporal Graph Neural Network for Traffic Prediction" is accepted by International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL) 2021.
  • July 2021. A College Student Innovation and Entrepreneurship Training Program (National level) is qualified. Thanks our advisor Hong Han, group Leader Zhendong Jin and our collaborators.
Friends
  • W.H. Yang, Ph. D. candidate of LAMDA@NJU, focus on Machine Learning (especially Online Learning) and Computer Vision.
  • Y.R. Pang, Research Intern of TANK LAB@TJU, focus on Web development and Computer Networks.
  • X.Y. Sun (Sund), postgraduate of XJTU, focus on Air-gapped Attack, Covert Communication and Wireless Sensing.
  • B.Y. Sun (BB Chan), postgraduate of NKU, focus on Computer Vision.
  • M. Chen, postgraduate of ShanghaiTech, focus on Artificial Intelligence and Reinforcement Learning.
  • C.Z. Ran, undergraduate of XDU, focus on Artificial Intelligence and Computer Vision.
  • D.C. Chen (RainCurtain), undergraduate of XDU and R.A. of CUHK, focus on Algorithm Design, chair of ISC@XDU.
  • Y.J. Zhang (YjmStr), undergraduate of XDU, focus on Algorithm Analysis and Design, mentor of ACM group in ISC@XDU.
  • Y.L. Feng, undergraduate of XDU, focus on JAVA development and Big Data.
  • Y.K. Xu (Viking), undergraduate of XDU, focus on Computer Security, mentor of CTF group in ISC@XDU.
  • X.Y. Zeng (Wings), undergraduate of XDU, focus on Algorithm Analysis and Design, mentor of ACM group in MSC@XDU.

(Last update: June 2022)