What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Theoretical computer science, Information transfer, Transfer entropy, Complex system and Cellular automaton.
His Theoretical computer science research is multidisciplinary, incorporating elements of Centrality, Katz centrality, Measure, Information theory and Computation.
His biological study spans a wide range of topics, including Entropy, Chaotic, Artificial intelligence and Information processing.
In his work, Landauer's principle, Theory of computation and Estimation theory is strongly intertwined with Statistical physics, which is a subfield of Transfer entropy.
His Complex system study integrates concerns from other disciplines, such as Management science, Cognitive science, Relation, Adaptation and Field.
The study incorporates disciplines such as Domain, Filter and Metric in addition to Cellular automaton.
His most cited work include:
- An information-theoretic primer on complexity, self-organization, and emergence (221 citations)
- Modelling transmission and control of the COVID-19 pandemic in Australia. (214 citations)
- Local information transfer as a spatiotemporal filter for complex systems. (195 citations)
What are the main themes of his work throughout his whole career to date?
Mikhail Prokopenko focuses on Artificial intelligence, Theoretical computer science, Statistical physics, Information transfer and Complex system.
His study in Theoretical computer science is interdisciplinary in nature, drawing from both Transfer entropy, Information theory, Computation and Cellular automaton.
His research investigates the connection between Information theory and topics such as Entropy that intersect with problems in Entropy.
The various areas that Mikhail Prokopenko examines in his Computation study include Swarm behaviour and Information processing.
Mikhail Prokopenko interconnects Principle of maximum entropy, Phase transition, Fisher information and Measure in the investigation of issues within Statistical physics.
His study on Information transfer is mostly dedicated to connecting different topics, such as Distributed computing.
He most often published in these fields:
- Artificial intelligence (25.49%)
- Theoretical computer science (32.94%)
- Statistical physics (17.25%)
What were the highlights of his more recent work (between 2018-2021)?
- Econometrics (3.92%)
- Statistical physics (17.25%)
- Pandemic (4.31%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Econometrics, Statistical physics, Pandemic, Agent-based model and Dynamics.
His Econometrics study frequently links to other fields, such as Assortativity.
Classification of discontinuities is closely connected to Phase transition in his research, which is encompassed under the umbrella topic of Statistical physics.
His Pandemic study frequently draws connections to adjacent fields such as Social distance.
His Agent-based model research incorporates elements of Volatility and Dimension.
His Control study incorporates themes from Fraction, Public economics and Environmental health.
Between 2018 and 2021, his most popular works were:
- Modelling transmission and control of the COVID-19 pandemic in Australia. (214 citations)
- Revealing COVID-19 transmission in Australia by SARS-CoV-2 genome sequencing and agent-based modeling. (100 citations)
- Global socio-economic losses and environmental gains from the Coronavirus pandemic. (63 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Mikhail Prokopenko mainly focuses on Pandemic, Infectious disease, Social distance, Phase transition and Statistical physics.
Pandemic is integrated with Nonlinear coupling, Genetics, Whole genome sequencing, Transmission and Disease cluster in his study.
Many of his research projects under Infectious disease are closely connected to Intervention, Imitation, Game theory and Risk analysis with Intervention, Imitation, Game theory and Risk analysis, tying the diverse disciplines of science together.
His work in Social distance incorporates the disciplines of Public economics, Isolation, Intervention, Control and Social isolation.
His studies in Phase transition integrate themes in fields like Spatial ecology, Cartography, Spatial distribution and Fisher information.
Statistical physics is closely attributed to Social dynamics in his study.
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