Yiping LuUndergraduate
Department of Scientific & Engineering Computing 

I'm interested in all computational and statistical methods used in imaging and graphics. Now I am working on data scince, hoping to build the bridge between deep learning and PDE(variation), wavelets and other traditional data analysis methods. Although I'm not major in statistics or computer science, I interested in statical learning theroy applied in artificial intelligence. I am also working on learning on manifolds, mainly semisupervised learning via diffusion or wavelets. At the same time, we want to bring insight to graph CNN designing.
I am also working on learning theory, uncertainty quantification, sparse coding, inverse problem and computer vision.
20122015
Shanghai High School
Mathematics class
20152019 (Expected)
Peking University
School of mathematical sciences, Department of Scientific & Engineering Computing
Major:Information and Computing Science
Beijing International Center for Mathematical Research(BICMR), Peking University
Research Intern(2016.12present)
Supported by the Elite Undergraduate Training Program of the School of Mathematical Sciences at Peking University and National innovation training project.
Advisor:Prof. Bin Dong
Key Laboratory of Machine Perception (MOE), Peking Unviersity
Research Intern(2017.12present)
Advisor:Prof. Liwei Wang
MIT CSAIL Geometry Data Processing Group, MIT
Visiting Undergraduate Student(2018.62018.8)
Advisor:Prof. Justin Solomon
Visual Computing Group, Microsoft Research Asia
Research Intern(2018.112019.7)
Advisor:David Wipf
Seeking for PHD position.....
1st prize in Chinese Mathematical Olympiad 2013.12 
2nd prize in Chinese Mathematical Olympiad 2014.12 
DTZ/Cushman & Wakefield Scholarship 2015206 
Merit Student in PKU (top 5%) 2015206 
The elite undergraduate training program of Pure Math 2016present 
The elite undergraduate training program of Applied Math 2017present 
Theory without practice is empty, but equally, practice without theory is blind.  I. Kant
People who wish to analyze nature without using mathematics must settle for a reduced understanding.  Richard Feynman