D-Index & Metrics Best Publications

Li Zhang

Google (United States)
United States

D-Index & Metrics 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.

Discipline name Citations Publications World Ranking National Ranking

Computer Science D-index 39 Citations 10,491 113 World Ranking 5961 National Ranking 2874

Overview

What is he best known for?

The fields of study he is best known for:

Artificial intelligence
Algorithm
Computer network

Li Zhang spends much of his time researching Artificial intelligence, Differential privacy, Machine learning, Deep learning and Distributed computing. Artificial intelligence is closely attributed to Computer vision in his work. Li Zhang has included themes like Norm, Bounded function, Principal component analysis and Online algorithm in his Differential privacy study.

The Machine learning study combines topics in areas such as Information sensitivity and Private information retrieval. His study in Information sensitivity is interdisciplinary in nature, drawing from both Variety and Privacy software. His studies deal with areas such as Wireless sensor network, Graph, Adjacency list and Line segment as well as Distributed computing.

His most cited work include:

Deep Learning with Differential Privacy (1299 citations)
Scalable Influence Maximization in Social Networks under the Linear Threshold Model (622 citations)
TreeJuxtaposer: scalable tree comparison using Focus+Context with guaranteed visibility (286 citations)

What are the main themes of his work throughout his whole career to date?

Li Zhang mostly deals with Combinatorics, Mathematical optimization, Artificial intelligence, Algorithm and Kinetic data structure. The concepts of his Combinatorics study are interwoven with issues in Discrete mathematics, Convex geometry and Regular polygon. His Mathematical optimization research incorporates elements of Bounded function, Resource allocation and Convex optimization.

His research in Artificial intelligence intersects with topics in Machine learning and Computer vision. His Machine learning study combines topics in areas such as Private information retrieval and Differential privacy. His research investigates the link between Kinetic data structure and topics such as Collision detection that cross with problems in Motion, Robot and Motion planning.

He most often published in these fields:

Combinatorics (19.64%)
Mathematical optimization (16.07%)
Artificial intelligence (14.29%)

What were the highlights of his more recent work (between 2015-2021)?

Differential privacy (10.71%)
Artificial intelligence (14.29%)
Machine learning (6.25%)

In recent papers he was focusing on the following fields of study:

Li Zhang focuses on Differential privacy, Artificial intelligence, Machine learning, Private information retrieval and Data science. His Differential privacy study incorporates themes from Null and Computation. His study ties his expertise on Computer vision together with the subject of Artificial intelligence.

The Stochastic gradient descent research Li Zhang does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Work, therefore creating a link between diverse domains of science. His Private information retrieval research incorporates themes from Artificial neural network, Information sensitivity, Deep learning and Privacy software. In his articles, Li Zhang combines various disciplines, including Artificial neural network and Inference attack.

Between 2015 and 2021, his most popular works were:

Deep Learning with Differential Privacy (1299 citations)
Learning Differentially Private Recurrent Language Models (267 citations)
Deep Learning with Differential Privacy (196 citations)

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.

Best Publications

Deep Learning with Differential Privacy

Martin Abadi;Andy Chu;Ian Goodfellow;H. Brendan McMahan.
computer and communications security (2016)

2662 Citations

Scalable Influence Maximization in Social Networks under the Linear Threshold Model

Wei Chen;Yifei Yuan;Li Zhang.
international conference on data mining (2010)

1015 Citations

Geometric spanners for routing in mobile networks

Jie Gao;L.J. Guibas;J. Hershberger;Li Zhang.
IEEE Journal on Selected Areas in Communications (2005)

533 Citations

TreeJuxtaposer: scalable tree comparison using Focus+Context with guaranteed visibility

Tamara Munzner;François Guimbretière;Serdar Tasiran;Li Zhang.
international conference on computer graphics and interactive techniques (2003)

472 Citations

Learning Differentially Private Recurrent Language Models

H. Brendan McMahan;Daniel Ramage;Kunal Talwar;Li Zhang.
international conference on learning representations (2018)

434 Citations

Geometric spanner for routing in mobile networks

Jie Gao;Leonidas J. Guibas;John Hershberger;Li Zhang.
mobile ad hoc networking and computing (2001)

410 Citations

Tycoon: An implementation of a distributed, market-based resource allocation system

Kevin Lai;Lars Rasmusson;Eytan Adar;Li Zhang.
Multiagent and Grid Systems (2005)

405 Citations

GLIDER: gradient landmark-based distributed routing for sensor networks

Qing Fang;Jie Gao;L.J. Guibas;V. de Silva.
international conference on computer communications (2005)

371 Citations

Soft 3D reconstruction for view synthesis

Eric Penner;Li Zhang.
ACM Transactions on Graphics (2017)

289 Citations

Discrete mobile centers

Jie Gao;Leonidas J. Guibas;John Hershberger;Li Zhang.
symposium on computational geometry (2001)

222 Citations

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