What is she best known for?
The fields of study she is best known for:
- Quantum mechanics
- Statistics
- Mathematical analysis
Her scientific interests lie mostly in Combinatorics, Discrete mathematics, Phase transition, Statistical physics and Percolation.
Her Combinatorics study integrates concerns from other disciplines, such as Upper and lower bounds and Bounded function.
Her Phase transition research includes elements of Correlation function, Order, Torus and Boolean data type.
Her studies deal with areas such as Probability distribution, Cutoff, Exponential function and Degree distribution as well as Statistical physics.
Her Percolation research incorporates elements of Complex system and Mathematical analysis.
As a part of the same scientific study, Jennifer Chayes usually deals with the Random graph, concentrating on Scaling and frequently concerns with Condensed matter physics, Mean field theory and Satisfiability.
Her most cited work include:
- Convergent sequences of dense graphs I: Subgraph frequencies, metric properties and testing (486 citations)
- Finite-Size Scaling and Correlation Lengths for Disordered Systems (397 citations)
- Maximizing social influence in nearly optimal time (370 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of investigation include Combinatorics, Discrete mathematics, Random graph, Statistical physics and Phase transition.
Periodic boundary conditions is closely connected to Lattice in her research, which is encompassed under the umbrella topic of Combinatorics.
Her research on Discrete mathematics focuses in particular on Dense graph.
Her work in Random graph addresses subjects such as Preferential attachment, which are connected to disciplines such as Degree distribution, Power law and Degree.
Her Statistical physics study frequently draws parallels with other fields, such as Percolation.
The concepts of her Percolation study are interwoven with issues in Condensed matter physics, Cluster, Mathematical analysis and Percolation critical exponents.
She most often published in these fields:
- Combinatorics (31.75%)
- Discrete mathematics (21.43%)
- Random graph (10.32%)
What were the highlights of her more recent work (between 2016-2020)?
- Discrete mathematics (21.43%)
- Random graph (10.32%)
- Dense graph (4.37%)
In recent papers she was focusing on the following fields of study:
Jennifer Chayes spends much of her time researching Discrete mathematics, Random graph, Dense graph, Theoretical computer science and Machine learning.
Her work carried out in the field of Discrete mathematics brings together such families of science as Random measure, Scale and Constant.
Her work deals with themes such as Bipartite graph and Large deviations theory, Rate function, which intersect with Random graph.
As a member of one scientific family, she mostly works in the field of Rate function, focusing on Symmetry breaking and, on occasion, Combinatorics.
Her study of Vertex is a part of Combinatorics.
She has included themes like Equivalence and Probabilistic logic in her Dense graph study.
Between 2016 and 2020, her most popular works were:
- Entropy-SGD: Biasing Gradient Descent Into Wide Valleys. (168 citations)
- Tackling Climate Change with Machine Learning (112 citations)
- Entropy-SGD: biasing gradient descent into wide valleys* (57 citations)
In her most recent research, the most cited papers focused on:
- Quantum mechanics
- Statistics
- Mathematical analysis
Her primary areas of study are Machine learning, Artificial intelligence, Discrete mathematics, Equivalence and Dense graph.
Her studies deal with areas such as Classifier, Emergency management, Humanity and Word embedding as well as Machine learning.
Her Range study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Smart grid, Greenhouse gas, Focus and Intersection, bridging the gap between disciplines.
Her Discrete mathematics research integrates issues from Mathematical proof, Quotient and Limit theory.
She regularly ties together related areas like Power law in her Equivalence studies.
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