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tonyauyeung/README.md

Hi there 👋

I'm Tony RuiKang OuYang (歐陽瑞康 in Chinese; SeoiHong AuYeung in Cantonese). Recently, I completed my MPhil in Machine Learning and Machine Intelligence from the University of Cambridge, where my thesis focus on energy-based models and neural sampler for sampling from Boltzmann distribution and supervised by Prof. José Miguel Hernández-Lobato.

Prior to Cambridge, I obtained a BEng in Data Science at Harbin Institute of Technology, shenzhen (HITsz) in China, with distinction. During my year 3, I spent 7 months visiting in the University of Oxford, fully funded by HITsz, studying Mathematics and Statistics and supervised by Prof. Gesine Reinert.

My current research interests span generative models, neural samplers and their application to molecular generation. Generally, I'm interested in probabilistic machine learning and AI4S, espesially developping powerful, efficient and scalable methods that can applied to physics and molecular modeling.

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    This project would provide codes for Local2GAE2Global (L2G2G) model, which is a scalable GAE approach combing the idea of local2global.

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  2. Machine-Learning-notes Machine-Learning-notes Public

    This repo contains notes in machine learning, mainly probabilistic models.

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  3. curriculum_learning_L361 curriculum_learning_L361 Public

    Forked from QianyiLiu309/curriculum_learning_L361

    Python

  4. The-Family-of-DDPMs The-Family-of-DDPMs Public

    This is the repo of our paper, The Family of DDPMs.

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