Igor Molybog

2.7k citations
7 papers · 29 · h-index 4

Impact in

    • Fault Detection and Control Systems
    • Smart Grid Security and Resilience
    • Adversarial Robustness in Machine Learning
    • Anomaly Detection Techniques and Applications
    • Topic Modeling
    • Natural Language Processing Techniques

Papers in

Igor Molybog

7 papers receiving 28 citations

Peers

Igor Molybog
Comparison fields: 5 of 20
  • Control and Systems Engineering 11
  • Artificial Intelligence 15
  • Numerical Analysis 2
  • Statistical and Nonlinear Physics 3
  • Computational Mechanics 4
Replace Kushal Chakrabarti with:
Kushal Chakrabarti United States
Martijn van Veelen Netherlands
Christiane Peters United States
Clare Lyle United Kingdom
Andy E Williams Kenya
Brendan J. Orenstein Australia
Bernardo Ávila Pires Canada
A. Ryzhikov Russia
Seyed Kamyar Seyed Ghasemipour Canada
Gaurav Manek United States
Igor Molybog relative to Kushal Chakrabarti United States Kushal Chakrabarti's profile →
Citations per field
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Kushal Chakrabarti · 1×
Citations per year

Countries citing papers authored by Igor Molybog

Since Specialization
Citations

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

Fields of papers citing papers by Igor Molybog

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 21 scholars most cited alongside Igor Molybog, 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 Igor Molybog Line = papers co-authored together Igor Molybog links everyone, so they are left out of the graph.

All Works

About Igor Molybog

Igor Molybog is a scholar working on Computational Mechanics, Mathematical Physics, Control and Systems Engineering, Artificial Intelligence and Computer Networks and Communications, having authored 7 papers that have together received 29 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (3 papers), Numerical methods in inverse problems (2 papers), Drilling and Well Engineering (1 paper), Adversarial Robustness in Machine Learning (1 paper), Control Systems and Identification (1 paper), Topic Modeling (1 paper), Seismic Imaging and Inversion Techniques (1 paper) and Anomaly Detection Techniques and Applications (1 paper). The work is most often cited by research in Control and Systems Engineering (11 citations), Artificial Intelligence (15 citations), Numerical Analysis (2 citations), Statistical and Nonlinear Physics (3 citations) and Computational Mechanics (4 citations). Igor Molybog has collaborated with scholars based in United States. Frequent co-authors include Javad Lavaei, Somayeh Sojoudi, Ming Jin, Ramtin Madani, Hejia Zhang, Shruti Bhosale, Barlas Oğuz, Hao Ma, Angela Fan and Rui Hou. Their work appears in journals such as Mathematical Programming and IEEE Transactions on Smart Grid.

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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