Daniel Muthukrishna

1.3k total citations
10 papers, 198 citations indexed

About

Daniel Muthukrishna is a scholar working on Astronomy and Astrophysics, Computational Mechanics and Nuclear and High Energy Physics. According to data from OpenAlex, Daniel Muthukrishna has authored 10 papers receiving a total of 198 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Astronomy and Astrophysics, 3 papers in Computational Mechanics and 3 papers in Nuclear and High Energy Physics. Recurrent topics in Daniel Muthukrishna's work include Gamma-ray bursts and supernovae (9 papers), Pulsars and Gravitational Waves Research (4 papers) and Astrophysics and Cosmic Phenomena (3 papers). Daniel Muthukrishna is often cited by papers focused on Gamma-ray bursts and supernovae (9 papers), Pulsars and Gravitational Waves Research (4 papers) and Astrophysics and Cosmic Phenomena (3 papers). Daniel Muthukrishna collaborates with scholars based in United States, United Kingdom and Australia. Daniel Muthukrishna's co-authors include B. Tucker, David Parkinson, Michelle Lochner, Gautham Narayan, Sara Webb, Kaisey S. Mandel, M. W. Coughlin, C. Stachie, N. Christensen and Konstantin Malanchev and has published in prestigious journals such as The Astrophysical Journal, Monthly Notices of the Royal Astronomical Society and Astronomy and Astrophysics.

In The Last Decade

Daniel Muthukrishna

10 papers receiving 180 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel Muthukrishna United States 9 162 59 42 32 28 10 198
Konstantin Malanchev Russia 9 176 1.1× 55 0.9× 35 0.8× 51 1.6× 35 1.3× 43 233
A. Möller Australia 6 153 0.9× 63 1.1× 28 0.7× 20 0.6× 24 0.9× 16 193
T. Boch France 6 120 0.7× 56 0.9× 33 0.8× 13 0.4× 34 1.2× 25 160
M. V. Pruzhinskaya Russia 10 191 1.2× 76 1.3× 33 0.8× 48 1.5× 29 1.0× 29 241
A. Volnova Russia 9 182 1.1× 76 1.3× 35 0.8× 45 1.4× 30 1.1× 44 228
P. Sanchéz-Sáez Chile 11 251 1.5× 50 0.8× 58 1.4× 9 0.3× 25 0.9× 36 299
Ryan Riegel United States 4 214 1.3× 50 0.8× 73 1.7× 34 1.1× 11 0.4× 8 254
Kate Storey-Fisher United States 9 197 1.2× 50 0.8× 73 1.7× 31 1.0× 9 0.3× 13 237
A. O. Clarke United Kingdom 6 149 0.9× 80 1.4× 26 0.6× 6 0.2× 13 0.5× 7 166
L. Faccioli France 9 175 1.1× 33 0.6× 88 2.1× 35 1.1× 13 0.5× 22 229

Countries citing papers authored by Daniel Muthukrishna

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Muthukrishna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Muthukrishna

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

All Works

10 of 10 papers shown
1.
Muthukrishna, Daniel, et al.. (2025). Transfer learning for transient classification: from simulations to real data and ZTF to LSST. Monthly Notices of the Royal Astronomical Society Letters. 542(1). L132–L138. 3 indexed citations
2.
Moldovan, Dan, Michelle Kunimoto, Chelsea X. Huang, et al.. (2023). Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations. The Astronomical Journal. 165(3). 95–95. 9 indexed citations
3.
Ishida, Émille E. O., J. Peloton, A. Möller, et al.. (2023). Enabling the discovery of fast transients. Astronomy and Astrophysics. 677. A77–A77. 10 indexed citations
4.
Fausnaugh, Michael, P. Vallely, M. A. Tucker, et al.. (2023). Four Years of Type Ia Supernovae Observed by TESS: Early-time Light-curve Shapes and Constraints on Companion Interaction Models. The Astrophysical Journal. 956(2). 108–108. 13 indexed citations
5.
Muthukrishna, Daniel, Kaisey S. Mandel, Michelle Lochner, Sara Webb, & Gautham Narayan. (2022). Real-time detection of anomalies in large-scale transient surveys. Monthly Notices of the Royal Astronomical Society. 517(1). 393–419. 16 indexed citations
6.
Chatterjee, Deep, Gautham Narayan, P. Aleo, Konstantin Malanchev, & Daniel Muthukrishna. (2021). El-CID: A filter for Gravitational-wave Electromagnetic Counterpart Identification. arXiv (Cornell University). 8 indexed citations
7.
Webb, Sara, Michelle Lochner, Daniel Muthukrishna, et al.. (2020). Unsupervised machine learning for transient discovery in deeper, wider, faster light curves. Monthly Notices of the Royal Astronomical Society. 498(3). 3077–3094. 20 indexed citations
8.
Stachie, C., M. W. Coughlin, N. Christensen, & Daniel Muthukrishna. (2020). Using machine learning for transient classification in searches for gravitational-wave counterparts. Monthly Notices of the Royal Astronomical Society. 497(2). 1320–1331. 10 indexed citations
9.
Muthukrishna, Daniel. (2019). RAPID: Early Classification of Explosive Transients Using Deep Learning. Figshare. 81 indexed citations
10.
Muthukrishna, Daniel, David Parkinson, & B. Tucker. (2019). DASH: Deep Learning for the Automated Spectral Classification of Supernovae and Their Hosts. The Astrophysical Journal. 885(1). 85–85. 28 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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