Tianxin Wei
Research Assistant at University of Illinois Urbana-Champaign
Champaign, Illinois, United States
Summary
👤
Senior🎓
Top SchoolTianxin Wei is a 5th-year PhD researcher at UIUC specializing in personalization, trustworthy ML, data-centric AI, and agent-augmented large language models. With eight years of research and industry experience, he has interned at top labs including Google DeepMind, Meta, Amazon, and multiple universities, and briefly contributed to production-focused multimodal personalization as an Applied Scientist at Amazon. His work bridges theoretical trustworthiness and practical LLM-driven personalization, often focusing on making agents and models more reliable for user-specific needs. Trained at USTC’s School of the Gifted Young and now advised by Prof. Jingrui He, he combines strong academic rigor with hands-on internships across leading AI organizations. An active researcher with a public website and GitHub presence, he brings a rare mix of data-centric thinking and applied systems experience to ML safety and personalization problems.
8 years of coding experience
1 year of employment as a software developer
Bachelor's degree Computer Science School of the Gifted Young, Bachelor's degree Computer Science School of the Gifted Young at University of Science and Technology of ChinaDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at University of Illinois Urbana-Champaign
Github Skills (31)
robustness10
generalization10
graph9
deep-learning8
machine-learning8
artificial-intelligence8
lds7
neural-network7
updating7
graph-convolutional-networks7
compression7
graph-neural-network6
pytorch6
geometric-deep-learning6
theory5
Programming languages (1)
Github contributions (5)
weitianxin/weitianxin.github.io
Mar 2019 - Feb 2024
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Contributions:261 pushes in 4 years 11 months
templategithub-pages-templatemmistakesmistakesjekyll
weitianxin/awesome-distribution-shift
Dec 2022 - Mar 2023
A curated list of papers and resources about the distribution shift in machine learning.
Contributions:56 commits, 1 PR, 16 pushes in 2 months
artificial-intelligencedeep-learningdistribution-shiftdomain-adaptationdomain-generalization
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