Zhongxiang Dai
Assistant Professor at The Chinese University of Hong Kong, Shenzhen 香港中文大学（深圳）
Singapore
Summary
👤
Senior🎓
Top SchoolZhongxiang Dai is an Assistant Professor and machine learning researcher with a decade of experience spanning PhD training at NUS/MIT-affiliated SMART, postdoctoral work at MIT, and research fellowships in Singapore. His background blends electrical and electronics engineering with deep expertise in AI, computational neuroscience, and urban mobility applications, reflecting interdisciplinary strengths from theory to applied systems. He has a track record of bridging academia and industry—from implementing cryptographic protocols and advanced technology internships to leading ML research projects—and recently transitioned to a faculty role at The Chinese University of Hong Kong, Shenzhen. Known for rigorous experimental work and collaborations across NUS and MIT labs, he brings both hands-on implementation skills and a strong research portfolio to mentoring the next generation of ML practitioners.
10 years of coding experience
2 years of employment as a software developer
Bachelor of Engineering (BEng) Electrical and Electronics Engineering, Bachelor of Engineering (BEng) Electrical and Electronics Engineering at National University of Singapore
English, Chinese
Github Skills (34)
science8
bayesian8
ranking8
portable7
sampling7
java7
gradient7
machine-learning7
computer-science7
react7
scalable6
optimization6
distributed-systems6
python6
gaussian-processes6
Programming languages (3)
Github contributions (5)
daizhongxiang/Bayesian-Optimization-Meets-Bayesian-Optimal-Stopping
May 2019 - Oct 2020
Bayesian Optimization Meets Bayesian Optimal Stopping
Contributions:18 commits, 1 PR, 17 pushes in 1 year 5 months
optimizationmultiobjective-optimizationmeetsbayesian-optimizationgaussian-processes
daizhongxiang/Federated_Bayesian_Optimization
Oct 2020 - Nov 2020
Code for the NeurIPS 2020 paper: "Federated Bayesian Optimization via Thompson Sampling"
Contributions:22 commits, 2 PRs, 20 pushes in 21 days
thompsonneurips-2020optimizationneuripsthompson-sampling
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