Wanqi Xue
Quantitative Researcher
Singapore, Singapore
Summary
👤
Senior🎓
Top SchoolWanqi Xue is a quantitative researcher and final-year PhD candidate in Computer Science at Nanyang Technological University, specializing in reinforcement learning and multi-agent systems with applications across games, recommendation, and trading. With eight years of industry and research experience, Wanqi has built high- and mid-frequency trading strategies using deep learning and has consulted for Two Sigma after winning its machine learning challenge. As an Apache-SINGA committer and founder of its autograd system, she enabled autograd support for CNNs and RNNs in a major distributed deep learning platform. Her work spans academia and production, having held AI research roles at Huawei Singapore and currently driving quantitative research at MS Capital Singapore. Known for bridging cutting-edge RL research with practical, scalable ML systems, she brings both rigorous theory and hands-on distributed ML engineering to tackle complex, real-world problems.
8 years of coding experience
2 years of employment as a software developer
Master of Science - MS Electrical and Computer Engineering, Master of Science - MS Electrical and Computer Engineering at National University of SingaporeBachelor of Engineering - BE Electronic Science and Technology, Bachelor of Engineering - BE Electronic Science and Technology at University of Electronic Science and Technology of ChinaDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Nanyang Technological University Singapore
Github Skills (9)
mask-rcnn10
faster-rcnn10
deep-learning10
python10
autograd10
tensorflow9
n9
rnn-model9
pytorch9
Programming languages (3)
Github contributions (5)
apache/singa
Mar 2018 - Sep 2019
a distributed deep learning platform
Role in this project:
ML Engineer
ML EngineerContributions:63 commits, 27 PRs, 53 comments in 1 year 5 months
Contributions summary:Wanqi implemented and tested a Multi-Layer Perceptron (MLP) example, incorporating autograd functionality within the distributed deep learning platform. They updated the platform's tensor structure to support autograd, creating an Operation class for forward and backward functions. The user also added and modified code for a convolutional neural network (CNN) example, integrating and testing various layer operations based on the platform's autograd engine. The overall goal of the work was to enable autograd support for CNN and RNN within the distributed deep learning platform.
learning-platformdeep-learningmachine-learningdistributed-deep-learningdistributed
xuewanqi/incubator-singa
Jul 2018 - May 2019
Mirror of Apache Singa
Contributions:13 commits, 5 PRs, 130 pushes in 10 months
javaapache
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