Chirag Modi

1.8k total citations
27 papers, 481 citations indexed

About

Chirag Modi is a scholar working on Astronomy and Astrophysics, Artificial Intelligence and Instrumentation. According to data from OpenAlex, Chirag Modi has authored 27 papers receiving a total of 481 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Astronomy and Astrophysics, 8 papers in Artificial Intelligence and 5 papers in Instrumentation. Recurrent topics in Chirag Modi's work include Galaxies: Formation, Evolution, Phenomena (18 papers), Cosmology and Gravitation Theories (6 papers) and Gaussian Processes and Bayesian Inference (5 papers). Chirag Modi is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (18 papers), Cosmology and Gravitation Theories (6 papers) and Gaussian Processes and Bayesian Inference (5 papers). Chirag Modi collaborates with scholars based in United States, Germany and Canada. Chirag Modi's co-authors include Uroš Seljak, Martin White, Emanuele Castorina, Shi-Fan Chen, ChangHoon Hahn, Simone Ferraro, Bruno Régaldo-Saint Blancard, Pablo Lemos, Shirley Ho and Michael Eickenberg and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Chirag Modi

27 papers receiving 449 citations

Peers

Chirag Modi

INSTR

NHEP

SNP

George Stein United States

Simone Ferraro United States

Miguel Quartin Brazil

Zachary Slepian United States

Xu Zhou China

Reimar Leike Germany

Boudewijn F. Roukema Poland

P. B. Graff United States

Silvestre Paredes Spain

Elena Sellentin Netherlands

George Stein United States

Chirag Modi

211 ×0.6

61 ×0.6

INSTR

58 ×0.7

NHEP

15 ×0.2

SNP

21 ×0.4

Citations per year, relative to Chirag Modi Chirag Modi (= 1×) peers George Stein

Countries citing papers authored by Chirag Modi

Since Specialization

Citations

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

Fields of papers citing papers by Chirag Modi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chirag Modi

This figure shows the co-authorship network connecting the top 25 collaborators of Chirag Modi. A scholar is included among the top collaborators of Chirag Modi 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 Chirag Modi. Chirag Modi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Modi, Chirag, et al.. (2024). Sensitivity analysis of simulation-based inference for galaxy clustering. Monthly Notices of the Royal Astronomical Society. 536(1). 254–265. 9 indexed citations

Modi, Chirag, A. Ghosh, Lee Lindblom, et al.. (2024). Neural simulation-based inference of the neutron star equation of state directly from telescope spectra. Journal of Cosmology and Astroparticle Physics. 2024(9). 9–9. 11 indexed citations

Nguyên, Tri, Chirag Modi, L. Y. Aaron Yung, & Rachel S. Somerville. (2024). FLORAH: a generative model for halo assembly histories. Monthly Notices of the Royal Astronomical Society. 533(3). 3144–3163. 1 indexed citations

Yung, L. Y. Aaron, Rachel S. Somerville, Tri Nguyên, et al.. (2024). Characterizing ultra-high-redshift dark matter halo demographics and assembly histories with the gureft simulations. Monthly Notices of the Royal Astronomical Society. 530(4). 4868–4886. 13 indexed citations

Hahn, ChangHoon, Pablo Lemos, Liam Parker, et al.. (2024). Cosmological constraints from non-Gaussian and nonlinear galaxy clustering using the SimBIG inference framework. Nature Astronomy. 8(11). 1457–1467. 11 indexed citations

Blancard, Bruno Régaldo-Saint, ChangHoon Hahn, Shirley Ho, et al.. (2024). Galaxy clustering analysis with SimBIG and the wavelet scattering transform. Physical review. D. 109(8). 21 indexed citations

Horowitz, Benjamin, ChangHoon Hahn, François Lanusse, Chirag Modi, & Simone Ferraro. (2024). Differentiable stochastic halo occupation distribution. Monthly Notices of the Royal Astronomical Society. 529(3). 2473–2482. 2 indexed citations

Hahn, ChangHoon, Michael Eickenberg, Shirley Ho, et al.. (2024). Cosmological constraints from the nonlinear galaxy bispectrum. Physical review. D. 109(8). 18 indexed citations

Lemos, Pablo, Liam Parker, ChangHoon Hahn, et al.. (2024). Field-level simulation-based inference of galaxy clustering with convolutional neural networks. Physical review. D. 109(8). 26 indexed citations

10.

Modi, Chirag, Alex H. Barnett, & Bob Carpenter. (2023). Delayed rejection Hamiltonian Monte Carlo for sampling multiscale distributions. Bayesian Analysis. 19(3). 2 indexed citations

11.

Modi, Chirag, Yin Li, & David M. Blei. (2023). Reconstructing the universe with variational self-boosted sampling. Journal of Cosmology and Astroparticle Physics. 2023(3). 59–59. 9 indexed citations

12.

Hahn, ChangHoon, Michael Eickenberg, Shirley Ho, et al.. (2023). SimBIG: mock challenge for a forward modeling approach to galaxy clustering. Journal of Cosmology and Astroparticle Physics. 2023(4). 10–10. 26 indexed citations

13.

Modi, Chirag, et al.. (2023). Joint velocity and density reconstruction of the Universe with nonlinear differentiable forward modeling. Journal of Cosmology and Astroparticle Physics. 2023(6). 46–46. 15 indexed citations

14.

Hahn, ChangHoon, Michael Eickenberg, Shirley Ho, et al.. (2023). A forward modeling approach to analyzing galaxy clustering with S im BIG. Proceedings of the National Academy of Sciences. 120(42). e2218810120–e2218810120. 24 indexed citations

15.

Modi, Chirag, Vanessa Böhm, Simone Ferraro, George Stein, & Uroš Seljak. (2021). Estimating COVID-19 mortality in Italy early in the COVID-19 pandemic. Nature Communications. 12(1). 2729–2729. 53 indexed citations

16.

Modi, Chirag, François Lanusse, & Uroš Seljak. (2020). FlowPM: Distributed TensorFlow Implementation of the FastPM Cosmological\n N-body Solver. arXiv (Cornell University). 32 indexed citations

17.

Modi, Chirag, Shi-Fan Chen, & Martin White. (2020). Simulations and symmetries. Monthly Notices of the Royal Astronomical Society. 492(4). 5754–5763. 56 indexed citations

18.

Hand, Nick, Yu Feng, Florian Beutler, et al.. (2019). nbodykit: Massively parallel, large-scale structure toolkit. Astrophysics Source Code Library. 1 indexed citations

19.

Modi, Chirag, Martin White, & Zvonimir Vlah. (2017). Modeling CMB lensing cross correlations with CLEFT. eScholarship (California Digital Library). 27 indexed citations

20.

Modi, Chirag, Emanuele Castorina, & Uroš Seljak. (2017). Halo bias in Lagrangian space: estimators and theoretical predictions. Monthly Notices of the Royal Astronomical Society. 472(4). 3959–3970. 45 indexed citations

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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