AboutMe

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Tianqi Zhang 9月 28, 2022

Tianqi Zhang(张天启)

[email] [github]

I am currenlty a software develop engineer at ByteDance AML (Applied Machine Learning). Before that, I was a postgraduate student at Shanghai Jiao Tong University in SJTU-ThinkLab under the supervision of Prof. Yan. Currently, my research interests include graph machine learning and deep learning framework development.

 

EDUCATION
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2019 — 2022
Shanghai Jiao Tong University, Shanghai, China
Postgraduate in Computer Science, advised by Prof. Yan

2015 — 2019
Shanghai Jiao Tong University, Shanghai, China
Bachelor in Computer Science

 

EXPERIENCE
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May.2022 — Present
Bytedance Applied Machine Learning, Shanghai, China
SDE, MLSys Group

Jun.2021 — Aug.2021
Meituan Shanghai Office, Shanghai, China
Intern, Deep Learning Compiler Group

Dec.2020 — Jun.2021
AWS Shanghai AI Lab, Shanghai, China
Applied Scientist Intern, DGL Group

Jan.2019 — Jul.2019
Microsoft Research Asia, Beijing, China
Software Development Intern, Innovation Engineering Group(IEG)

Aug.2018 — Nov.2018
Tencent Shanghai Office
Intern, Advertising Algorithm Group

 

OPEN SOURCE CONTRIBUTIONS
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Deep Graph Library
Top 20 Contributor
GXN, SAGPool, DiffPool, HGP-SL, Graculs Clustering, KNN (KD-tree, NN-descent, etc.)

Apache TVM
Contributor
Improve dynamic shape input support. Bug fix of several operatiors.

 

PUBLICATIONS
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ScaleGCN: Efficient and Effective Graph Convolution via Channel-Wise Scale Transformation
Tianqi Zhang, Qitian Wu, Junchi Yan, Yunan Zhao, and Bing Han
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022

Learning High-Order Graph Convolutional Networks via Adaptive Layerwise Aggregation Combination
Tianqi Zhang, Qitian Wu, and Junchi Yan
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021

Combinatorial Learning of Graph Edit Distance via Dynamic Embedding
Wang Runzhong, Tianqi Zhang, Tianshu Yu, Junchi Yan, and Xiaokang Yang
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021

 

RESEARCH INTERESTS
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Machine Learning, Deep Learning Framework Development.