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About Me

  • First Name - Mi
  • Last Name - Feng
  • Date of Birth - 31 Dec 1993
  • Nationality - China

Hello, I'm Mi Feng from Hong Kong Baptist University. My research focuses on studying how temporal and spatial properties affect transmission in complex networks. For temporal effects, we analyze these effects on transmission accuracy using Markovian and non-Markovian approaches, and further develop control strategies, accuracy criteria, and rectification methods for these theories. Regarding spatial effects, the loop structure in networks poses a major challenge, reducing the accuracy of contemporary numerical and theoretical approaches, and transforming the problem into an NP-hard question. To address this issue, we are now employing deep learning (graph neural networks) to study and overcome these limitations.

My Resume

Education
  • Bachelor of Physics
  • Sep. 2012 - Jun. 2016
  • Lanzhou University (LZU)
  • Master Of Computer Science
  • Sep. 2016 - Jun. 2019
  • University of Electronic Science and Technology of China (UESTC)
  • Doctorate Of Physics
  • Sep. 2020 - Jun. 2024 (estimated)
  • Hong Kong Baptist University (HKBU)
Visiting Experience
  • Visiting Student
  • Aug. 2017 - Aug. 2018
  • East China Normal University (ECNU)
  • Visiting Scholar
  • Feb. 2023 - Aug. 2023
  • Arizona State Univeristy (ASU)
Interests & Hobbies
  • Biking
  • Gym
  • Swimming
  • Reading
  • Coding
  • Skiing

Publication

My Portfolio

Application-ValMarMem
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Validity of Markovian for Memory
Web Applications
Application-ValMarMem

Application-ValMarMem

  • Client : Application for paper
  • Date : Jul. 2023
  • Categories : Web Applications

The web-based application for the paper, "Validity of Markovian modeling for transient memory-dependent epidemic dynamics", is a tool designed to rectify R0 estimation and epidemic forecasting within the Markovian modeling context. It also provides insights into how the infection, removal, and generation time distributions, along with their average times, are influenced by changes in distribution parameters.

Paper-ValMarMem
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Validity-of-Markovian-for-Memory
Papers
Paper-ValMarMem

Validity-of-Markovian-for-Memory

  • Client : paper
  • Date : Jul. 2023
  • Categories : Papers

The initial transient phase of an emerging epidemic is of critical importance for data-driven model building, model-based prediction of the epidemic trend, and articulation of control/prevention strategies. In principle, quantitative models for real-world epidemics need to be memory-dependent or non-Markovian, but this presents difficulties for data collection, parameter estimation, computation and analyses. In contrast, the difficulties do not arise in the traditional Markovian models. To uncover the conditions under which Markovian and non-Markovian models are equivalent for transient epidemic dynamics is outstanding and of significant current interest. We develop a comprehensive computational and analytic framework to establish that the transient-state equivalence holds when the average generation time matches and average removal time, resulting in minimal Markovian estimation errors in the basic reproduction number, epidemic forecasting, and evaluation of control strategy. Strikingly, the errors depend on the generation-to-removal time ratio but not on the specific values and distributions of these times, and this universality will further facilitate estimation rectification. Overall, our study provides a general criterion for modeling memory-dependent processes using the Markovian frameworks.

Paper-EquMarNon
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Equivalence-Markovian-non-Markovian
Paper
Paper-EquMarNon

Equivalence-Markovian-non-Markovian

  • Client : Paper
  • Date : Aug. 2019
  • Categories : Papers

Epidemic spreading processes in the real world depend on human behaviors and, consequently, are typically non-Markovian in that the key events underlying the spreading dynamics cannot be described as a Poisson random process and the corresponding event time is not exponentially distributed. In contrast to Markovian type of spreading dynamics for which mathematical theories have been well developed, we lack a comprehensive framework to analyze and fully understand non-Markovian spreading processes. Here we develop a mean-field theory to address this challenge, and demonstrate that the theory enables accurate prediction of both the transient phase and the steady states of non-Markovian susceptible-infected-susceptible spreading dynamics on synthetic and empirical networks. We further find that the existence of equivalence between non-Markovian and Markovian spreading depends on a specific edge activation mechanism. In particular, when temporal correlations are absent on active edges, the equivalence can be expected; otherwise, an exact equivalence no longer holds.

Paper-NonMarRes
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non-Markovian-Resilient
Papers
Paper-NonMarRes

non-Markovian-Resilient

  • Client : Papers
  • Date : May 2020
  • Categories : Papers

Non-Markovian spontaneous recovery processes with a time delay (memory) are ubiquitous in the real world. How does the non-Markovian characteristic affect failure propagation in complex networks? We consider failures due to internal causes at the nodal level and external failures due to an adverse environment, and develop a pair approximation analysis taking into account the two-node correlation. In general, a high failure stationary state can arise, corresponding to large-scale failures that can significantly compromise the functioning of the network. We uncover a striking phenomenon: memory associated with nodal recovery can counter-intuitively make the network more resilient against large-scale failures. In natural systems, the intrinsic non-Markovian characteristic of nodal recovery may thus be one reason for their resilience. In engineering design, incorporating certain non-Markovian features into the network may be beneficial to equipping it with a strong resilient capability to resist catastrophic failures.

Paper-MecOptPre
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Mechanism-of-Optimal-on-Prediction
Papers
Paper-MecOptPre

Mechanism-of-Optimal-on-Prediction

  • Client : papers
  • Date : Oct. 2021
  • Categories : Papers

Vaccination is essential for controlling the coronavirus disease (COVID-19) pandemic. An effective time-course strategy for the allocation of COVID-19 vaccines is crucial given that the global vaccine supply will still be limited in some countries/regions in the near future and that mutant strains have emerged and will continue to spread worldwide. Both asymptomatic and symptomatic transmission have played major roles in the COVID-19 pandemic, which can only be properly described as a typical non-Markovian process. However, the prioritization of vaccines in the non-Markovian framework still lacks sufficient research, and the underlying mechanism of the time-course vaccine allocation optimization has not yet been uncovered. In this paper, based on an age-stratified compartmental model calibrated through clinical and epidemiological data, we propose optimal vaccination strategies (OVS) through steady-state prediction in the non-Markovian framework. This OVS outperforms other empirical vaccine prioritization approaches in minimizing cumulative infections, cumulative deaths, or years of life lost caused by the pandemic. We found that there exists a fast decline in the prevention efficiency of vaccination if vaccines are solely administered to a selected age group, which indicates that the widely adopted strategy to continuously vaccinate high-risk group is not optimal. Through mathematical analysis of the model, we reveal that dynamic vaccine allocations to combinations of different age groups is necessary to achieve optimal vaccine prioritization. Our work not only provides meaningful references for vaccination in countries currently lacking vaccines and for vaccine allocation strategies to prevent mutant strains in the future, but also reveals the mechanism of dynamic vaccine allocation optimization, forming a theoretical and modelling framework empirically applicable to the optimal time-course prioritization.

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netsyst
Packages
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Online Shopping Landing Page

  • Client : ...
  • Date : 2021
  • Categories : Packages

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physet
Packages
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Web Design Agency Landing Page

  • Client : ...
  • Date : 2021
  • Categories : Packages

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Code-ValMarMem
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Code for "Validity of Markovian modeling for transient memory-dependent epidemic dynamics"
Packages
Code-ValMarMem

Code for "Validity of Markovian modeling for transient memory-dependent epidemic dynamics"

  • Client : Code Availability
  • Date : 2023
  • Categories : Packages

The extension modules, experimental codes, figure codes and application package for the paper titled "Validity of Markovian modeling for transient memory-dependent epidemic dynamics"

Contact Me

  • Not Available Now
  • Phone
  • fengmi9312@gmail.com
  • Email
  • Hong Kong, China
  • Address