Xingyu Ni

Xingyu Ni

PhD Student at Center on Frontiers of Computing Studies

Peking University


Xingyu Ni is currently a PhD student at Center on Frontiers of Computing Studies (CFCS) in Peking University, advised by Prof. Baoquan Chen and Prof. John E. Hopcroft, and an intern at Advanced Innovation Center for Future Visual Entertainment (AICFVE) in Beijing Film Academy, advised by Dr. Bin Wang. He graduated from Turing Class, Peking University in July 2020 and earned dual bachelor’s degrees in computer science and physics.


  • Computational Physics
  • Computer Graphics
  • Physical Simulation
  • Physically Based Rendering


  • PhD Student in Computer Science, from 2020

    Center on Frontiers of Computing Studies, Peking University

  • BSc in Physics (Dual Degree), 2020

    School of Physics, Peking University

  • BSc in Computer Science, 2020

    School of Electronics Engineering and Computer Science, Peking University


In reverse chronological order

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A Level-Set Method for Magnetic Substance Simulation

A Level-Set Method for Magnetic Substance Simulation

This paper presents a versatile, numerical approach to simulating various magnetic phenomena using a level-set method, which contains a novel two-way coupling mechanism between a magnetic field and magnetizable objects.

Visual Data Analysis and Simulation Prediction for COVID-19

The COVID-19 (formerly, 2019-nCoV) epidemic has become a global health emergency, as such, WHO declared PHEIC. China has taken the most hit since the outbreak of the virus, which could be dated as far back as late November by some experts. It was not until January 23rd that the Wuhan government finally recognized the severity of the epidemic and took a drastic measure to curtain the virus spread by closing down all transportation connecting the outside world. In this study, we seek to answer a few questions: How did the virus get spread from the epicenter Wuhan city to the rest of the country? To what extent did the measures, such as, city closure and community quarantine, help controlling the situation? More importantly, can we forecast any significant future development of the event had some of the conditions changed? By collecting and visualizing publicly available data, we first show patterns and characteristics of the epidemic development; we then employ a mathematical model of disease transmission dynamics to evaluate the effectiveness of some epidemic control measures, and more importantly, to offer a few tips on preventive measures.