About Me

I obtained my Ph.D. from the University of Texas at Austin. It's my great honor to have been supervised by Prof. Zhangyang (Atlas) Wang in VITA group. I obtained my Bachelor's degree from Beijing University of Posts and Telecommunications (BUPT) in 2018 when I worked with professor Wenjun Xu, and obtained my Master's from UT Austin in 2020 in professor Nuria González Prelcic's group. I currently work to optimize search experience in Amazon.

Interests

  • Search Experience Optimization
  • Large Language Models
  • Graph Neural Networks

Education

  • The University of Texas at Austin
    M.S. in Engineering, 2018 - 2020
    Ph.D., 2020 - 2023

  • Beijing University of Posts and Telecommunications
    B.Eng. in Communications Engineering, 2014 - 2018

Professional Experience

  • Research Intern, The Home Depot, May 2023 - Aug 2023
    • Provide essential accessory recommendations in the Home Depot site, which are based on content that substitutes user behavior based recommendation.
    • Leverage ChatGPT and other large language models to retrieve the recommendation product taxonomy. Evaluate recommendation recall between extractive QA, text classification and ChatGPT.
    • Host(s): Dr. Omar ElTayeby, Dr. Haozheng Tian, Dr. Janani Balaji, Dr. Xiquan Cui
  • Research Intern, Google Cloud, May 2022 - Aug 2022
    • Performance prediction, workload and platform matching for cloud computing.
    • Host(s): Dr. Eric Zhang, Dr. Chelsea Llull
  • Applied Science Research Intern, Amazon A9, May 2021 - March 2022
    • Cold-start graph embedding learning for recommendation systems: first pretrain a graph model to generate versatile node embeddings using self-supervised learning, then learn a student model that is able to generalize to strict-cold-start nodes.
    • Manager/Mentor(s): Dr. Karthik Subbian, Dr. Nikhil Rao
  • Research Assistant, Visual Informatics Group, the University of Texas at Austin
    • Advisor: Dr. Zhangyang (Atlas) Wang
    • Research topics include:
      • Learning to Optimize: deliver faster and better optimizer for deep neural networks through back-propogate through the optimization procedure and find optimal optimizer in a data driven way.
      • Transformers: leverage the sequence modeling power of transformers in graph modeling/reinforcement learning.
      • Graph Networks: develop efficient graph models that scales and generalize.
  • Research Assistant, WSIL Group, the University of Texas at Austin
    • Estimate user device position through 5G millimeter wave signal, using compressive sensing (Orthogonal Mathing Pursuit) and deep learning (ConvNets, swarm based learning to optimize) approaches.
    • Advisor: Dr. Nuria González Prelcic
  • Research Intern, GEIRI North America
    • Train a Soft Actor-Critic agent to manage large scale power grid: embed the huge discrete geometric actionsinto continuous space; using Graph Neural Networks as preprocessing; Monte-Carlo Tree search as efficientexploration
    • Mentor: Dr. Jiajun Duan
  • Research Assistant, UWC Lab, Beijing University of Posts and Telecommunications
    • Signal processing for wireless communications (Doppler spread estimation)
    • Advisor: Dr. Wenjun Xu

Publications

AutoGAN
Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation
Wenqing Zheng, SP Sharan, Ajay Kumar Jaiswal, Kevin Wang, Yihan Xi,
Dejia Xu, Zhangyang Wang, International Conference on Machine Learning
(ICML), 2023
AutoGAN
Cold Brew: Distilling Graph Node Representations with
Incomplete or Missing Neighborhoods
Wenqing Zheng, Edward W Huang, Nikhil Rao, Sumeet Katariya,
Zhangyang Wang, Karthik Subbian, in International Conference
on Learning Representations (ICLR) 2022
AutoGAN
Symbolic Learning to Optimize: Towards Interpretability and Scalability
Wenqing Zheng, Tianlong Chen, Ting-Kuei Hu, Zhangyang Wang,
in International Conference on Learning Representations (ICLR) 2022
AutoGAN
Anti-Oversmoothing in Deep Vision Transformers via the
Fourier Domain Analysis: From Theory to Practice
Peihao Wang, Wenqing Zheng, Tianlong Chen, Zhangyang Wang,
in International Conference on Learning Representations (ICLR) 2022
AutoGAN
Bag of Tricks for Training Deeper Graph Neural Networks:
A Comprehensive Benchmark Study
Tianlong Chen, Kaixiong Zhou, Keyu Duan, Wenqing Zheng,
Peihao Wang, Xia Hu, and Zhangyang Wang, in TPAMI, 2022
-
Delayed Propagation Transformer: A Universal Computation Engine
towards Practical Control in Cyber-Physical Systems
Wenqing Zheng, Qiangqiang Guo, Hao Yang, Peihao Wang,
Zhangyang Wang, In NeurIPS 2021.

AutoGAN
Blind Image Blur Assessment Based on Markov-Constrained FCM and
Blur Entropy
Yaqian Xu, Wenqing Zheng, Jingchen Qi, Qi Li, 2019 IEEE International
Conference on Image Processing (ICIP). IEEE, 2019: 4519-4523.

EnlightenGAN
5G V2X communication at millimeter wave: rate maps and use cases
W. Zheng, A. Ali, N. González-Prelcic, R. W. Heath Jr., A. Klautau, and
E. Moradi Pari, 2020 IEEE 91st Vehicular Technology Conference
(VTC2020). IEEE, 2020: 1-5.

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