What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Computational complexity theory
Piotr Faliszewski mainly focuses on Artificial intelligence, Mathematical economics, Computer security, Computational social choice and Computational complexity theory.
In the subject of general Artificial intelligence, his work in Multi-agent system is often linked to Control and Work, thereby combining diverse domains of study.
His Mathematical economics study frequently links to other fields, such as Preference.
His Computer security study frequently links to related topics such as Control.
His studies deal with areas such as Fraction and Theoretical computer science as well as Computational complexity theory.
His Theoretical computer science research is multidisciplinary, incorporating elements of Probabilistic logic, Preference data, Approximation algorithm and Counting problem.
His most cited work include:
- Llull and Copeland voting computationally resist bribery and constructive control (216 citations)
- How hard is bribery in elections (209 citations)
- Using complexity to protect elections (185 citations)
What are the main themes of his work throughout his whole career to date?
Piotr Faliszewski mainly investigates Mathematical economics, Theoretical computer science, Computational complexity theory, Condorcet method and Control.
His work on Minimax and Social choice theory as part of general Mathematical economics research is often related to Rationalizability and Veto, thus linking different fields of science.
His Theoretical computer science research includes elements of Algorithm and Approximation algorithm.
In his research, Computational problem, Aggregation problem and Discrete mathematics is intimately related to Parameterized complexity, which falls under the overarching field of Computational complexity theory.
His work on Anti-plurality voting and Approval voting as part of general Condorcet method study is frequently connected to Control, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Control combines with fields such as Artificial intelligence, Time complexity, Outcome, Rational number and Pairwise comparison in his work.
He most often published in these fields:
- Mathematical economics (30.10%)
- Theoretical computer science (23.47%)
- Computational complexity theory (19.39%)
What were the highlights of his more recent work (between 2017-2021)?
- Theoretical computer science (23.47%)
- Combinatorics (9.18%)
- Mathematical optimization (10.71%)
In recent papers he was focusing on the following fields of study:
Piotr Faliszewski focuses on Theoretical computer science, Combinatorics, Mathematical optimization, Mathematical economics and Parameterized complexity.
His Isomorphism study in the realm of Combinatorics connects with subjects such as Domain and Group.
His work carried out in the field of Mathematical optimization brings together such families of science as Computational complexity theory and Preference.
Piotr Faliszewski is interested in Social choice theory, which is a field of Mathematical economics.
His Social choice theory research is multidisciplinary, incorporating perspectives in Pareto principle and Relation.
His work in the fields of Complement overlaps with other areas such as Computational social choice, Single peaked preferences and Constructive.
Between 2017 and 2021, his most popular works were:
- Opinion Diffusion and Campaigning on Society Graphs (23 citations)
- A Framework for Approval-Based Budgeting Methods (21 citations)
- Achieving fully proportional representation by clustering voters (16 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Artificial intelligence
- Game theory
Piotr Faliszewski mostly deals with Theoretical computer science, Mathematical optimization, Metric, Characterization and Diffusion process.
The concepts of his Theoretical computer science study are interwoven with issues in Approximation algorithm, Partition and Cluster analysis.
His research integrates issues of Computational complexity theory and Preference in his study of Mathematical optimization.
His study of Metric brings together topics like Combinatorics, Isomorphism, Preference, Measure and Space.
Piotr Faliszewski has included themes like Social choice theory and Pairwise comparison in his Characterization study.
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