Firas Hamze

3.0k citations
19 papers · 346 · h-index 11

Impact in

Papers in

    • Bayesian Methods and Mixture Models 4
    • Bayesian Modeling and Causal Inference 3
    • Neural Networks and Applications 3
    • Neural Networks and Reservoir Computing 3
    • Quantum Computing Algorithms and Architecture 2
    • Markov Chains and Monte Carlo Methods 10

Firas Hamze

19 papers receiving 318 citations

Peers

Firas Hamze
Comparison fields: 5 of 58
  • Artificial Intelligence 233
  • Statistics and Probability 46
  • Condensed Matter Physics 62
  • Computational Theory and Mathematics 56
  • Hardware and Architecture 16
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Citations per year

Countries citing papers authored by Firas Hamze

Since Specialization
Citations

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

Fields of papers citing papers by Firas Hamze

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

19 of 19 papers shown
#Work
1 201484
2 201650
3 200431
4
Adaptive MCMC with Bayesian Optimization
201228
5 201120
6 201118
7 201017
8 202015
9 201814
10 202012
11 201010
12
Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs
20059
13 20139
14
Hot coupling: a particle approach to inference and normalization on pairwise undirected graphs of arbitrary topology
20058
15 20108
16
A Stochastic approximation method for inference in probabilistic graphical models
20097
17
Intracluster moves for constrained discrete-space MCMC
20103
18 20152
19 20241

About Firas Hamze

Firas Hamze is a scholar working on Artificial Intelligence, Statistics and Probability, Condensed Matter Physics, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics, having authored 19 papers that have together received 346 indexed citations. Recurring topics across this work include Markov Chains and Monte Carlo Methods (10 papers), Theoretical and Computational Physics (8 papers), Bayesian Methods and Mixture Models (4 papers), Bayesian Modeling and Causal Inference (3 papers), Neural Networks and Applications (3 papers), Neural Networks and Reservoir Computing (3 papers), Quantum many-body systems (3 papers) and Quantum Computing Algorithms and Architecture (2 papers). The work is most often cited by research in Artificial Intelligence (233 citations), Statistics and Probability (46 citations), Condensed Matter Physics (62 citations), Computational Theory and Mathematics (56 citations) and Hardware and Architecture (16 citations). Firas Hamze has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Helmut G. Katzgraber, Nando de Freitas, Ruben S. Andrist, Kamran Karimi, Neil G. Dickson, Stefan Schnabel, Ziyu Wang, Zheng Zhu, Jack Raymond and P. Bunyk. Their work appears in journals such as Physical review. E, Physical Review X, ACM Transactions on Modeling and Computer Simulation, International Journal of Modern Physics C and The International Journal of High Performance Computing Applications.

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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