Michael Yuan | 袁承博

Michael (Chengbo) Yuan | 袁承博

🦊🦁🐮🐹🐰🐸🐶🐻🐨🐯

I am a Master student in Institute for Interdisciplinary Information Science (IIIS) at Tsinghua University, advised by Prof. Yang Gao🥳. Currently, my research interests mainly focus on Embodied AI (finding a way to achieve scalable robot learning), with the assistance of 3D computer vision (3DV).

Perviously, I have interned at MoonshotAI and MindSpore of Huawei. I am also interested in creating AI products or startups. I received my bachelor degree from the HongYi Honor class at the School of CS, Wuhan University😘, under the supervision of Prof. Yong Luo. I am also minoring in Law at School of Law and have a broad and extensive interest in philosophy and finance.

I welcome exploration, creation and excitement! 🤗🤩🤪

Google Scholar / Github / X(Twitter) / WeBlog / Zhihu

contact me: michaelyuancb@163.com ycb24@mails.tsinghua.edu.cn

Recent

A pathway I envision for scalable robot intelligence, which forms the central theme of my research.

I enrolled in Tsinghua University in August 2024! Looking forward to the story ahead!

Check out General Flow as a possible representation for scalable robot learning !

I am interested in the product-market fit (PMF) and the business story for the first 2 stages of the 3 levels of AGI.

Research

My research interests primarily focus on Embodied AI and 3D Computer Vision, particularly in utilizing vision-based methods to develop intelligent robotic systems with scalable ways. My ultimate aspiration is to create general-propose robots😎 that can truly understand and interact with our physical world.
(representative papers are highlighted)

	General Flow as Foundation Affordance for Scalable Manipulation Learning Chengbo Yuan, Chuan Wen, Tong Zhang, Yang Gao project page / arXiv / code / openreview Conference of Robot Learning (CoRL), 2024 We build a 3D flow prediction model directly from large-scale RGBD human video datasets. Based on this model, we achieve stable zero-shot human-to-robot skill transfer in the real world.
	Depression Diagnosis and Analysis via Multimodal Multi-order Factor Fusion Chengbo Yuan, Yongqian Li, Qiancheng Yang, Qianhui Xu, Yong Luo arXiv International Conference on Artificial Neural Networks (ICANN), 2024 A multi-order factor fusion framework is designed to fuse text, audio, and video information for depression diagnosis.
	MFT: Multi-scale Fusion Transformer for Infrared and Visible Image Fusion Chen-Ming Zhang, Chengbo Yuan, Yong Luo, Xin Zhou Paper International Conference on Artificial Neural Networks (ICANN), 2023 A pyramid network architecture for infrared and visible image fusion.

Project

	MindQuantFinance: High-Performance Quantitative Asset Pricing Library A library for asset pricing (Q-quant) based on MindSpore gitee This is an AI+Finance project I participated in, where we built a high-performance library for derivative pricing based on MindSpore. It offers features such as Black-Scholes calculations, Monte Carlo simulations, and solutions to Backward SDE.
	QFR: Tabular Prediction for Quantative Finance Research A tabular prediction library designed for financial stock prediction. github This is one of the outcomes of an interesting experience, during which I worked with Prof. Jian Li on stock prediction problems. It provide rich tools for factors extraction and results prediction.
	TaoClass: an AI-Driven Course Forum A course forum powered by Large Language Models (LLMs) and Vector Databases (VD). gitee This is a project I led, and it also serves as the final assignment for the Software Engineering course at Wuhan University. The forum utilizes AI technology to offer natural language course selection and intelligent back-end information management.