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Hi there 👋, I'm Changbin. Nice to meet you.

  • 🔭 I’m currently pursuing my Ph.D. in the Department of Computer Science at the University of Texas at Dallas, supervised by Prof. Feng Chen. I was a research intern at Meta AI (and incoming Research Scientist Intern at Meta AI for Summer 2024) and Bosch Center for AI. During my tenure at Meta AI, I contributed to a multimodal recurring transfer learning project. Subsequently, at BCAI, I focused on the sparsity of the Multimodal Foundation Models (Vision-Language Models).

  • 🌱 My research is in the field of Deep Learning, with a focus on low-resource learning (meta/few-shot/semi/self-supervised learning), uncertainty estimation, robustness, and efficiency in traditional CNNs and vision-language models. My goal is to ensure AI systems safe, robust, reliable and trustworthy. I’m also very interested in Generative AI (large language models (LLMs), and diffusion models), and exploring along these directions.

  • 💬 I'd like to write some blogs sometimes, including Paper Notes and Study Notes. Recently, I am summarizing my previous notes on Generative AI.

  • 👯 I am an incoming Research Scientist Intern at Meta AI this summer, and I’m seeking full-time roles for the end of 2024.

  • 📫 Feel free to contact me via email at [email protected].

Changbin Li's Projects

awesome-graph-embedding icon awesome-graph-embedding

A collection of important graph embedding, classification and representation learning papers with implementations.

cis521 icon cis521

Intro to Artificial Intelligence (Don't steal my code bro!)

d2l-en icon d2l-en

Dive into Deep Learning, Berkeley STAT 157 (Spring 2019) textbook. With code, math, and discussions.

d2l-zh icon d2l-zh

《动手学深度学习》,英文版即伯克利深度学习(STAT 157,2019春)教材。面向中文读者、能运行、可讨论。

evidentialnn icon evidentialnn

evidential neural network and different regularizations

machine-learning-notes icon machine-learning-notes

My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1000+ slides) 我不间断更新的机器学习,概率模型和深度学习的讲义(1000+页)和视频链接

rehession icon rehession

Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach

ups icon ups

"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah (ICLR 2021)

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