2021 - Wald Memorial Lecturer
2019 - Member of the National Academy of Sciences
2015 - John von Neumann Lecturer
2014 - Fellow of the American Academy of Arts and Sciences
2013 - Fellow of the American Mathematical Society
2010 - ACM Fellow For contributions to the foundations of dynamic random networks in theoretical computer science.
2005 - Fellow of the American Association for the Advancement of Science (AAAS)
1989 - Fellow of Alfred P. Sloan Foundation
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 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.
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.
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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Finite-Size Scaling and Correlation Lengths for Disordered Systems
J. T. Chayes;L. Chayes;Daniel S. Fisher;T. Spencer.
Physical Review Letters (1986)
Maximizing social influence in nearly optimal time
Christian Borgs;Michael Brautbar;Jennifer Chayes;Brendan Lucier.
symposium on discrete algorithms (2014)
Convergent sequences of dense graphs I: Subgraph frequencies, metric properties and testing
C. Borgs;Jennifer T. Chayes;László Lovász;Vera T. Sós.
Advances in Mathematics (2008)
Directed scale-free graphs
Béla Bollobás;Christian Borgs;Jennifer Chayes;Oliver Riordan.
symposium on discrete algorithms (2003)
Discontinuity of the magnetization in one-dimensional 1/¦x−y¦2 Ising and Potts models
M. Aizenman;J. T. Chayes;L. Chayes;C. M. Newman.
Journal of Statistical Physics (1988)
Convergent Sequences of Dense Graphs II. Multiway Cuts and Statistical Physics
Christian Borgs;Jennifer T. Chayes;László Lovász;Vera T. Sós.
Annals of Mathematics (2012)
Trust-based recommendation systems: an axiomatic approach
Reid Andersen;Christian Borgs;Jennifer Chayes;Uriel Feige.
the web conference (2008)
Dynamics of bid optimization in online advertisement auctions
Christian Borgs;Jennifer Chayes;Nicole Immorlica;Kamal Jain.
the web conference (2007)
Multi-unit auctions with budget-constrained bidders
Christian Borgs;Jennifer Chayes;Nicole Immorlica;Mohammad Mahdian.
electronic commerce (2005)
Tackling Climate Change with Machine Learning
David Rolnick;Priya L. Donti;Lynn H. Kaack;Kelly Kochanski.
arXiv: Computers and Society (2019)
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