Zhihang Liu

Zhihang Liu

Complexity | Complex Networks | Computational Social Science | Urban Computing



I am currently a master's student at Peking University with a keen focus on Urban Computing, Computational Social Science, Complexity, and Complex Networks. From September 2022 to September 2023, I had the privilege to serve as a research assistant in the Big Geospatial Data Management Group at the Technical University of Munich, Germany, under the guidance of Prof. Dr. Martin Werner. Presently, I am advancing my research at the Institute of Space and Earth Information Science at The Chinese University of Hong Kong, mentored by Professor Mei-Po Kwan.

Since 2020, I have been working as a part-time research assistant at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, under the leadership of Professor Li Yurui, delving into regional complex systems simulation and Multi-Agent Reinforcement Learning.

My aspiration is to integrate cutting-edge artificial intelligence algorithms into my research, with a particular interest in deep learning and large language models. My academic vision revolves around constructing a research framework based on the principles of spatiotemporal dynamics, causality, and scale, aiming for a deeper understanding of complex system dynamics.

You can reach me via email: liuzhihang@stu.pku.edu.cn

Download my resumé.

  • Complexity
  • Complex Networks
  • Computational Social Science
  • Urban Computing
  • MSc in Urban Planning (Research in Urban Computing), 2024

    Peking University

  • BA in Public Administration (Research in Computational Social Science), 2021

    Sun Yat-Sen University



Full -time Research Assistant / The Chinese University of Hong Kong
2023.9-2024.3 HongKong

Participation in Project: “Vehicle Detection and Vehicle-kilometrage Estimation Based on Remote Sensing Technologies”, Smart Traffic Fund, The Government of Hong Kong SAR, No. PSRI/44/2208/PR, 2023 – 2025. (PI: Prof. Mei-Po Kwan)

Research Direction: Deep Learning of Remote Sensing and Modeling of Complex Transportation Systems

Supervisor: Prof. Mei-Po Kwan

Summer Intern / The JTL Urban Mobility Lab at MIT

Research Direction: Mobility AI with urban imagery, networks, and natural language

Completed Project: Holistic urban sensing based on remote sensing and street view images using Vision Transformers

Full -time Research Assistant / Technical University of Munich

Participation in Project: “Towards a NAS Benchmark for Classification in Earth Observation”, Sponsored by: TUM Data Science in Earth Observation cooperating with the German Aerospace Center (DLR), 2022-2023

Research Direction: Complexity & Complex Networks, Auto ML

Output papers: Efficiency and equity of the multimodal travel between public transit and bike-sharing accounting for multiscale, Spatio-temporal Analysis of Urban Economic Resilience during Covid-19 with Multilayer Complex Networks

Supervisor: Prof. Dr. Martin Werner

Part- time Research Assistant/IGSNRR, Chinese Academy of Sciences
2020.9- present

Participation in Project: “Cooperative Observation, Transformation Mechanism and Scenario Simulation of Rural Areal System”, Major Program of the National Natural Science Foundation of China, No. 42293270, 2023 – 2027. (PI: Prof. Liu Yansui)

Research Direction: Regional complex systems simulation & Multi-Agent Reinforcement Learning

Output paper: Exploring Patterns of Rural Development through Collective Behavior-Based AI Simulation

Supervisor: Prof. Yurui Li


The taxi track in Shenzhen, China.
We visualized the taxi tracks and buildings in shenzhen using the deck.gl.
Example Project
The relationship between air pollution distribution(Pm2.5) and taxi track in Jinan, China.
We visualized the taxi tracks and air pollution distribution in Jinan
Example Project
Detection of different types of multi-modal travel OD networks in Shenzhen, China.
We detected the different types of multi-modal travel OD networks in Shenzhen, China.
Example Project
Tale of Two Cities: Exploring Pandemic Fatigue under China's COVID-19 Policies.
We illustrate the Covid Fatigue during the Covid-19 between Wuhan and Shanghai.
Example Project


Peer Reviewed Articles

Working Papers


Code PDF

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