Daniel Ting

731 citations
24 papers · 420 · h-index 9

Impact in

Papers in

Daniel Ting

22 papers receiving 400 citations

Peers

Daniel Ting
Comparison fields: 5 of 80
  • Signal Processing 75
  • Computer Networks and Communications 153
  • Artificial Intelligence 176
  • Hardware and Architecture 17
  • Molecular Biology 129
Replace Artūrs Bačkurs with:
Artūrs Bačkurs United States
Søren Kamaric Riis United Kingdom
Daniel Cullina United States
Angelo Troina Italy
Zhicong Huang China
Loÿs Thimonier France
Ioan Todinca France
Andrea Sgarro Italy
Yngve Villanger Norway
Alexandru Ioan Tomescu Finland
Daniel Ting relative to Artūrs Bačkurs United States Artūrs Bačkurs's profile →
Citations per field
00.5×1.5×2×2.3×
Artūrs Bačkurs · 1×
Citations per year

Countries citing papers authored by Daniel Ting

Since Specialization
Citations

This map shows the geographic impact of Daniel Ting's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Ting with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Ting more than expected).

Fields of papers citing papers by Daniel Ting

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Ting. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Ting. The network helps show where Daniel Ting may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Ting, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Ting Line = papers co-authored together Daniel Ting links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010149
2 201874
3 201430
4 201827
5 201926
6 200821
7 201619
8 202018
9 202010
10 20219
11
Optimal Subsampling with Influence Functions
20186
12 20135
13
An Analysis of the Convergence of Graph Laplacians
20104
14 20224
15 20223
16 20253
17 19723
18 20192
19 20252
20
Online Semi-Supervised Learning on Quantized Graphs
20102

About Daniel Ting

Daniel Ting is a scholar working on Signal Processing, Computer Networks and Communications, Artificial Intelligence, Hardware and Architecture and Statistics and Probability, having authored 24 papers that have together received 420 indexed citations. Recurring topics across this work include Data Management and Algorithms (8 papers), Advanced Database Systems and Queries (8 papers), Data Stream Mining Techniques (3 papers), Internet Traffic Analysis and Secure E-voting (3 papers), Bayesian Methods and Mixture Models (2 papers), Caching and Content Delivery (2 papers), Statistical Methods and Inference (2 papers) and Algorithms and Data Compression (2 papers). The work is most often cited by research in Signal Processing (75 citations), Computer Networks and Communications (153 citations), Artificial Intelligence (176 citations), Hardware and Architecture (17 citations) and Molecular Biology (129 citations). Daniel Ting has collaborated with scholars based in United States, Spain and Italy. Frequent co-authors include Michael I. Jordan, Guoli Wang, Rajib Kumar Mitra, Roland L. Dunbrack, Maxim V. Shapovalov, Pedro Reviriego, Stephen E. Fienberg, Mario Trottini, Eric Brochu and Jonathan Malkin. Their work appears in journals such as IEEE Communications Letters, IEEE Transactions on Dependable and Secure Computing, IEEE Access, PLoS Computational Biology and Cell Reports Medicine.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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