Yingzhen Li
Associate Professor In Machine Learning
Stony Stratford, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolYingzhen Li is an Associate Professor in Machine Learning at Imperial College London with 13 years’ experience bridging theoretical deep learning and approximate inference with practical applications. She earned a PhD from Cambridge and previously worked as a senior researcher at Microsoft Research, bringing research rigor to applied problems and student mentorship. Her work spans probabilistic modelling, variational methods and generative models—captured by a playful GitHub bio, "teaching machines to learn and fantasize"—and includes published contributions like a disentangled sequential auto-encoder from an ICML internship. Colleagues know her for combining strong mathematical training (BSc in Mathematics, top 7%) with hands-on experimental skills dating back to interdisciplinary lab work. Based in the UK, she maintains an active academic profile focused on pushing the theoretical foundations of deep learning toward real-world impact.
13 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Machine Learning, Doctor of Philosophy (Ph.D.), Machine Learning at University of CambridgeZhixin High SchoolBachelor of Science (B.S.), mathematics, 91/100 (top 7%), Bachelor of Science (B.S.), mathematics, 91/100 (top 7%) at Sun Yat-Sen University
English, Chinese, Chinese
Github Skills (19)
stochastic8
mathematics8
meta7
machine-learning7
python7
intervals7
stochastic-processes6
meta-learning6
data-science5
backend3
sampling2
benchmarking2
bayesian2
density-estimation2
numpy1
Programming languages (3)
Github contributions (5)
WenboGong/MetaSGMCMC
Jun 2018 - Feb 2019
The code for Meta Learning for SGMCMC
Contributions:9 commits, 1 PR, 5 pushes in 8 months
meta-learningmetamachine-learningstochastic-processesstochastic
nvcuong/variational-continual-learning
Feb 2018 - Jan 2019
Implementation of the variational continual learning method
Contributions:7 commits, 8 pushes in 11 months
methodcontinual-learningmachine-learningcontinualvariational
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