N
Nafis Sadik Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
faculty
6 h-index
33 pubs
410 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Nafis Sadik's research investigates the application of machine learning to predict light resonance modes in disordered dielectric scatterer arrays. His work also explores the impact of higher-order network structures in power grids on factors such as optimal power flow relaxation tightness and line outage distribution. Sadik has published 33 papers, accumulating 410 citations and an h-index of 6. His recent publications in 2022 and 2023 indicate ongoing activity in these research areas.
Metrics
- h-index: 6
- Publications: 33
- Citations: 410
Selected Publications
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Assessing the Impact of Higher Order Network Structure on Tightness of OPF Relaxation (2023)
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Impact of Higher-Order Structures in Power Grids' Graph on Line Outage Distribution Factor (2023)
Collaboration Network
Top Collaborators
Mohammad Rasoul Narimani
California State University, Northridge (US)
4 shared publications
- Assessing the impact of Higher Order Network Structure on Tightness of OPF Relaxation
- Impact of Higher-Order Structures in Power Grids' Graph on Line Outage Distribution Factor
- Impact of Higher-Order Structures in Power Grids' Graph on Line Outage Distribution Factor
- Assessing the Impact of Higher Order Network Structure on Tightness of OPF Relaxation
Mohammad Ali
1 shared publication
- Predicting strongly localized resonant modes of light in disordered arrays of dielectric scatterers: a machine learning approach
A. Haque
BRAC University (BD)
1 shared publication
- Predicting strongly localized resonant modes of light in disordered arrays of dielectric scatterers: a machine learning approach
Tashfiq Ahmed
1 shared publication
- Predicting strongly localized resonant modes of light in disordered arrays of dielectric scatterers: a machine learning approach
Md Zunaid Baten
1 shared publication
- Predicting strongly localized resonant modes of light in disordered arrays of dielectric scatterers: a machine learning approach
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