Ioannis Ch. Paschalidis

6.6k citations
228 papers · 4.1k indexed · 1 hit paper · h-index 33

Ioannis Ch. Paschalidis

215 papers receiving 3.9k citations

Hit Papers

Federated learning of predictive models from federated El...5862018202620202023100200300400500

Peers

Ioannis Ch. Paschalidis
Comparison fields: 5 of 178
  • Health Informatics 142
  • Management Information Systems 538
  • Computer Networks and Communications 1.1k
  • Artificial Intelligence 1.2k
  • Health Information Management 133
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Simon Fong Macao
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Citations per field
00.5×3.4×
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Citations per year

Countries citing papers authored by Ioannis Ch. Paschalidis

Since Specialization
Citations

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

Fields of papers citing papers by Ioannis Ch. Paschalidis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ioannis Ch. Paschalidis. 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 Ioannis Ch. Paschalidis. The network helps show where Ioannis Ch. Paschalidis may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Ioannis Ch. Paschalidis, 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 Ioannis Ch. Paschalidis Line = papers co-authored together Ioannis Ch. Paschalidis links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20242
4 20244
5 20234
6 20231
7 20230
8 202212
9 202123
10 202114
11 202152
12 202037
13 202029
14 20209
15
Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression
20193
16
Asymptotic Network Independence in Distributed Optimization for Machine Learning
20191
17
A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient Descent
20196
18 20133
19 20130
20 20123

About Ioannis Ch. Paschalidis

Ioannis Ch. Paschalidis is a scholar working on Computer Networks and Communications, Management Information Systems and Health Informatics, having authored 228 papers that have together received 4.1k indexed citations. Recurring topics across this work include Energy Efficient Wireless Sensor Networks (26 papers), Advanced Queuing Theory Analysis (24 papers), Reinforcement Learning in Robotics (20 papers), Protein Structure and Dynamics (19 papers), Smart Grid Energy Management (18 papers), Advanced Wireless Network Optimization (16 papers), Mobile Ad Hoc Networks (16 papers) and Machine Learning in Healthcare (15 papers). The work is most often cited by research in Health Informatics (142 citations), Management Information Systems (538 citations) and Computer Networks and Communications (1.1k citations). Ioannis Ch. Paschalidis has collaborated with scholars based in United States, China and Italy. Frequent co-authors include Dimitris Bertsimas, Ruidi Chen, Theodora S. Brisimi, John N. Tsitsiklis, Alex Olshevsky, Theofanie Mela, Wei Shi, John N. Tsitsiklis, Christos G. Cassandras and Michael C. Caramanis. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Neurology.

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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