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- Leonard J. Schulman

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Computer Science
D-index
35
Citations
5,860
122
World Ranking
7603
National Ranking
3571

- Algorithm
- Mathematical analysis
- Algebra

Leonard J. Schulman mainly focuses on Theoretical computer science, Algorithm, Discrete mathematics, Combinatorics and Information theory. His Theoretical computer science research is multidisciplinary, relying on both Asymptotically optimal algorithm and Shannon–Hartley theorem. His research integrates issues of Dimension, Artificial intelligence and Pattern recognition in his study of Algorithm.

His study explores the link between Pattern recognition and topics such as Probabilistic analysis of algorithms that cross with problems in Simple. He combines subjects such as Tree network, Computation, Group and Bounded function with his study of Discrete mathematics. Leonard J. Schulman specializes in Combinatorics, namely Time complexity.

- Splitters and near-optimal derandomization (269 citations)
- Broadcasting on trees and the Ising model (246 citations)
- The Effectiveness of Lloyd-Type Methods for the k-Means Problem (236 citations)

His primary areas of study are Combinatorics, Discrete mathematics, Algorithm, Mathematical optimization and Upper and lower bounds. His work deals with themes such as Binary number, Exponential function and Regular polygon, which intersect with Combinatorics. The concepts of his Discrete mathematics study are interwoven with issues in Function, Quantum algorithm and Bounded function.

Within one scientific family, he focuses on topics pertaining to Quantum computer under Quantum algorithm, and may sometimes address concerns connected to Computation. His Algorithm research includes elements of Rate of convergence, Norm and Numerical linear algebra. His research in Mathematical optimization intersects with topics in Stackelberg competition, Latency and Special case.

- Combinatorics (37.42%)
- Discrete mathematics (34.97%)
- Algorithm (12.88%)

- Combinatorics (37.42%)
- Discrete mathematics (34.97%)
- Mathematical economics (5.52%)

His primary areas of investigation include Combinatorics, Discrete mathematics, Mathematical economics, Constant and Nash equilibrium. A large part of his Combinatorics studies is devoted to Binary logarithm. His Discrete mathematics study incorporates themes from Computational complexity theory, Measure, Zero-sum game, Decoding methods and Exponential growth.

His work in Computational complexity theory covers topics such as Symbolic integration which are related to areas like Simple. His Mathematical economics research incorporates themes from Convergence, Mathematical optimization and Algebraic connectivity. The Constant study combines topics in areas such as Tree, Core, Binary tree and Arity.

- Optimal Coding for Streaming Authentication and Interactive Communication (46 citations)
- The adversarial noise threshold for distributed protocols (14 citations)
- Extractors for Near Logarithmic Min-Entropy (13 citations)

- Quantum mechanics
- Algorithm
- Mathematical analysis

Discrete mathematics, Binary logarithm, Combinatorics, Mathematical optimization and Constant are his primary areas of study. Leonard J. Schulman works in the field of Discrete mathematics, focusing on Directed graph in particular. The various areas that Leonard J. Schulman examines in his Binary logarithm study include Matching, Current, Logarithm and Limit.

His work carried out in the field of Combinatorics brings together such families of science as Unsupervised learning and Distribution. His Mathematical optimization research incorporates elements of Simple and Incentive compatibility. His biological study spans a wide range of topics, including Tree, Exponential growth, Binary tree and Arity.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

The effectiveness of lloyd-type methods for the k-means problem

Rafail Ostrovsky;Yuval Rabani;Leonard J. Schulman;Chaitanya Swamy.

Journal of the ACM **(2013)**

899 Citations

Splitters and near-optimal derandomization

M. Naor;L.J. Schulman;A. Srinivasan.

foundations of computer science **(1995)**

360 Citations

Coding for interactive communication

L.J. Schulman.

international symposium on information theory **(1995)**

283 Citations

Broadcasting on trees and the Ising model

William Evans;Claire Kenyon;Yuval Peres;Leonard J. Schulman.

Annals of Applied Probability **(2000)**

267 Citations

A Two-Round Variant of EM for Gaussian Mixtures

Sanjoy Dasgupta;Leonard J. Schulman.

uncertainty in artificial intelligence **(2000)**

209 Citations

A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians

Sanjoy Dasgupta;Leonard Schulman.

Journal of Machine Learning Research **(2007)**

207 Citations

Quantum mechanical algorithms for the nonabelian hidden subgroup problem

Michelangelo Grigni;Leonard Schulman;Monica Vazirani;Umesh Vazirani.

symposium on the theory of computing **(2001)**

194 Citations

Asymptotically good codes correcting insertions, deletions, and transpositions

L.J. Schulman;D. Zuckerman.

IEEE Transactions on Information Theory **(1999)**

154 Citations

A random walk model of wave propagation

M. Franceschetti;J. Bruck;L.J. Schulman.

IEEE Transactions on Antennas and Propagation **(2004)**

153 Citations

Communication on noisy channels: a coding theorem for computation

L.J. Schulman.

foundations of computer science **(1992)**

147 Citations

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