Eli Upfal

About

Eli Upfal is the Rush C. Hawkins professor of computer science at Brown University. He served as the department chair from 2002 to 2007. Prior to joining Brown in 1998, he was a researcher and project manager at the IBM Almaden Research Center in California and a professor at the Weizmann Institute in Israel. He holds an undergraduate degree in mathematics and statistics and a doctorate degree in computer science from the Hebrew University in Jerusalem, Israel. His research focuses on the design and analysis of algorithms, with particular interest in randomized algorithms and probabilistic analysis of algorithms. His work has applications in combinatorial and stochastic optimization, routing and communication networks, computational biology, and computational finance. Additionally, he notes his Erdos number is 2 and that he is a mathematical descendant of Eli Shamir, Jacques Hadamard (4th generation), Simeon Denis Poisson (8th generation), and Pierre-Simon Laplace (9th generation).

Research topics

• Machine Learning
• Computer Science
• Artificial Intelligence

Selected publications

• Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees

  International Conference on Artificial Intelligence and Statistics · 2021-03-18 · 5 citations

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• Distributed Graph Diameter Approximation

  Algorithms · 2020-09-01 · 5 citations

  articleOpen accessSenior author

  We present an algorithm for approximating the diameter of massive weighted undirected graphs on distributed platforms supporting a MapReduce-like abstraction. In order to be efficient in terms of both time and space, our algorithm is based on a decomposition strategy which partitions the graph into disjoint clusters of bounded radius. Theoretically, our algorithm uses linear space and yields a polylogarithmic approximation guarantee; most importantly, for a large family of graphs, it features a…

  PublisherOA PDFDOI

• Reducing polarization and increasing diverse navigability in graphs by inserting edges and swapping edge weights

  Data Mining and Knowledge Discovery · 2022-10-02 · 3 citations

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• Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with\n Weak Mixing Time Bounds

  arXiv (Cornell University) · 2021-11-14 · 2 citations

  preprintOpen accessSenior author

  We present a novel method for reducing the computational complexity of\nrigorously estimating the partition functions (normalizing constants) of Gibbs\n(Boltzmann) distributions, which arise ubiquitously in probabilistic graphical\nmodels. A major obstacle to practical applications of Gibbs distributions is\nthe need to estimate their partition functions. The state of the art in\naddressing this problem is multi-stage algorithms, which consist of a cooling\nschedule, and a mean estimator in each…

  PublisherOA PDFDOI

• Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes

  arXiv (Cornell University) · 2022-05-25 · 1 citations

  preprintOpen access

  We develop a rigorous mathematical analysis of zero-shot learning with attributes. In this setting, the goal is to label novel classes with no training data, only detectors for attributes and a description of how those attributes are correlated with the target classes, called the class-attribute matrix. We develop the first non-trivial lower bound on the worst-case error of the best map from attributes to classes for this setting, even with perfect attribute detectors. The lower bound characteri…

  PublisherOA PDFDOI

Recent grants

• Efficient Distributed Approximation Algorithms

  NSF · $25k · 2009–2011

• RI: Small: Statistically Sound and Computationally Efficient Data Analysis Through Algorithmic Applications of Rademacher Averages

  NSF · $466k · 2018–2023

• Analytical Approaches to Massive Data Computation with Applications to Genomics

  NIH · $283k · 2013–2018

Frequent coauthors

• Fabio Vandin

  University of Padua

  78 shared

• Matteo Riondato

  61 shared

• Gopal Pandurangan

  University of Houston

  56 shared

• Benjamin J. Raphael

  49 shared

• Lorenzo De Stefani

  44 shared

• Andrea Pietracaprina

  University of Padua

  40 shared

• Geppino Pucci

  University of Padua

  38 shared

• Ahmad Mahmoody

  Microsoft (Finland)

  32 shared

Similar researchers at Brown University

• Michael L. Littmanh-83
• Maurice P Herlihyh-72
• Roberto Tamassiah-69