What is she best known for?
The fields of study she is best known for:
- Statistics
- Algorithm
- Computer network
Her primary areas of investigation include Distributed computing, Combinatorics, Network topology, Discrete mathematics and Theoretical computer science.
The concepts of her Distributed computing study are interwoven with issues in Degree, Computer network, Bounded function and Parallel computing.
The Graph research Eli Upfal does as part of her general Combinatorics study is frequently linked to other disciplines of science, such as Value, therefore creating a link between diverse domains of science.
Her work deals with themes such as Hash function and Load balancing, which intersect with Discrete mathematics.
Her Theoretical computer science research incorporates elements of Computation, Graph, Random graph and Search engine.
In Random graph, Eli Upfal works on issues like Randomness, which are connected to Random walk.
Her most cited work include:
- Probability and Computing: Randomized Algorithms and Probabilistic Analysis (2017 citations)
- Balanced Allocations (713 citations)
- Stochastic models for the Web graph (627 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Algorithm, Discrete mathematics, Theoretical computer science, Combinatorics and Mathematical optimization.
Her Algorithm study combines topics from a wide range of disciplines, such as Sampling and DNA sequencing.
Her Discrete mathematics research is multidisciplinary, incorporating perspectives in Node and Random walk.
Her work in Theoretical computer science addresses subjects such as Computation, which are connected to disciplines such as Distributed computing.
Her Distributed computing research is multidisciplinary, relying on both Routing table, Equal-cost multi-path routing, Routing protocol, Stochastic process and Multipath routing.
Her study in Mathematical optimization is interdisciplinary in nature, drawing from both Destination-Sequenced Distance Vector routing, DSRFLOW, Metric space and Sequence.
She most often published in these fields:
- Algorithm (19.87%)
- Discrete mathematics (16.16%)
- Theoretical computer science (14.48%)
What were the highlights of her more recent work (between 2013-2021)?
- Theoretical computer science (14.48%)
- Algorithm (19.87%)
- Mathematical optimization (12.46%)
In recent papers she was focusing on the following fields of study:
Eli Upfal mainly investigates Theoretical computer science, Algorithm, Mathematical optimization, Artificial intelligence and Graph.
The Theoretical computer science study combines topics in areas such as Approximations of π, Social network, Power graph analysis, Betweenness centrality and Suite.
Eli Upfal combines subjects such as Uniform convergence and Rademacher complexity with her study of Algorithm.
Her Mathematical optimization research includes themes of Node, Pathwidth and Metric space.
Cluster analysis and Parallel algorithm is closely connected to Disjoint sets in her research, which is encompassed under the umbrella topic of Graph.
Her Folded cube graph study contributes to a more complete understanding of Discrete mathematics.
Between 2013 and 2021, her most popular works were:
- Fast Distributed PageRank Computation (84 citations)
- TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size (72 citations)
- Machine Learning in High Energy Physics Community White Paper (56 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Algorithm
- Computer network
Her scientific interests lie mostly in Theoretical computer science, Betweenness centrality, Distributed computing, Streaming algorithm and Algorithm.
A large part of her Theoretical computer science studies is devoted to PageRank.
Her biological study spans a wide range of topics, including Discrete mathematics and Power graph analysis.
Eli Upfal interconnects Job scheduler and Query expansion in the investigation of issues within Distributed computing.
Her study looks at the relationship between Streaming algorithm and fields such as Sequential algorithm, as well as how they intersect with chemical problems.
Her Algorithm study incorporates themes from Tree-depth, Pathwidth, Block graph, Indifference graph and Chordal graph.
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