Diego Klabjan

5.1k total citations
156 papers, 3.0k citations indexed

About

Diego Klabjan is a scholar working on Industrial and Manufacturing Engineering, Artificial Intelligence and Management Information Systems. According to data from OpenAlex, Diego Klabjan has authored 156 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Industrial and Manufacturing Engineering, 43 papers in Artificial Intelligence and 31 papers in Management Information Systems. Recurrent topics in Diego Klabjan's work include Supply Chain and Inventory Management (29 papers), Vehicle Routing Optimization Methods (24 papers) and Advanced Manufacturing and Logistics Optimization (17 papers). Diego Klabjan is often cited by papers focused on Supply Chain and Inventory Management (29 papers), Vehicle Routing Optimization Methods (24 papers) and Advanced Manufacturing and Logistics Optimization (17 papers). Diego Klabjan collaborates with scholars based in United States, Germany and United Kingdom. Diego Klabjan's co-authors include Timothy M. Sweda, Bernardo Almada‐Lobo, George L. Nemhauser, Sergey Shebalov, Héctor González, Irina Dolinskaya, Ellis L. Johnson, Xiaolei Li, Jiawei Han and Daniel Adelman and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and European Journal of Operational Research.

In The Last Decade

Diego Klabjan

145 papers receiving 2.8k citations

Peers

Diego Klabjan
Comparison fields: 5 of 137
  • Industrial and Manufacturing Engineering 1.1k
  • Automotive Engineering 673
  • Electrical and Electronic Engineering 649
  • Management Information Systems 485
  • Management Science and Operations Research 470
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Citations per field, relative to Diego Klabjan
Diego Klabjan · 1×
Citations per year, relative to Diego Klabjan
Diego Klabjan · 1×

Countries citing papers authored by Diego Klabjan

Since Specialization
Citations

This map shows the geographic impact of Diego Klabjan'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 Diego Klabjan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego Klabjan more than expected).

Fields of papers citing papers by Diego Klabjan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Diego Klabjan. 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 Diego Klabjan. The network helps show where Diego Klabjan may publish in the future.

Co-authorship network of co-authors of Diego Klabjan

This figure shows the co-authorship network connecting the top 25 collaborators of Diego Klabjan. A scholar is included among the top collaborators of Diego Klabjan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Diego Klabjan. Diego Klabjan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 0
3 0
4 3
5 1
6 0
7 1
8 2
9 3
10
Portfolio Optimization for American Options
2
11
Convergence Analysis of Batch Normalization for Deep Neural Nets.
7
12
Bayesian Network Learning via Topological Order
9
13
Improving the Expected Improvement Algorithm
11
14
Iteratively Reweighted Least Squares Algorithms for L1-Norm Principal Component Analysis
1
15 14
16
Optimal Expediting Policies for a Serial Inventory System with Stochastic Lead Time and Visibility of Orders in Transit
4
17 1
18 14
19 7
20
Airline Crew Pairing Generation in Parallel
11

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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