He Xiangnan
Professor at University of Science and Technology of China
Hefei, Anhui, China
Summary
🤩
Rockstar🎓
Top SchoolHe Xiangnan is a Professor at the University of Science and Technology of China with a decade of experience specializing in AI areas such as information retrieval, data mining, machine learning, and knowledge reasoning. His research trajectory spans top-tier industry and academia, including a PhD from NUS, a postdoc at NUS, and research internships at Google and Microsoft where he delivered production-facing algorithms and tools. He has hands-on deep learning engineering experience—contributing notable work on neural collaborative filtering (NeuMF) implementations and evaluation fixes on GitHub—bridging reproducible research and practical recommender systems. Based in Hefei, his profile combines rigorous theoretical foundations with pragmatic optimization for large-scale implicit-feedback models, and he often brings production sensibilities to academic research.
10 years of coding experience
BS, Computer Software Engineering, BS, Computer Software Engineering at East China Normal UniversityDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at National University of Singapore
Github Skills (12)
neural-network10
keras10
eval10
machine-learning10
collaborative-filtering10
deep-learning10
trainings10
python10
evaluation10
modeling10
recommender-system10
argument-parsing8
Programming languages (1)
Github contributions (5)
hexiangnan/neural_collaborative_filtering
Feb 2017 - Dec 2018
Neural Collaborative Filtering
Role in this project:
ML Engineer
ML EngineerContributions:13 commits, 1 PR, 12 pushes in 1 year 11 months
Contributions summary:He focused on developing and refining a neural collaborative filtering (NeuMF) model for a recommender system. Their work included implementing the NeuMF architecture using Keras, fixing evaluation bugs, adding arguments for customization, and cleaning up code. The contributions demonstrate expertise in deep learning model development, specifically for recommendation tasks, and optimization of model performance.
autoencoderdeep-learninghyphenationautoencoderscollaborative-filtering
hexiangnan/attentional_factorization_machine
Aug 2017 - Jul 2018
TensorFlow Implementation of Attentional Factorization Machine
Contributions:10 commits, 14 pushes, 1 branch in 11 months
deep-learningfactorization-machinetensorflowfactorization
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