Alex I. Malz

517 total citations
23 papers, 161 citations indexed

About

Alex I. Malz is a scholar working on Astronomy and Astrophysics, Instrumentation and Artificial Intelligence. According to data from OpenAlex, Alex I. Malz has authored 23 papers receiving a total of 161 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Astronomy and Astrophysics, 10 papers in Instrumentation and 3 papers in Artificial Intelligence. Recurrent topics in Alex I. Malz's work include Astronomy and Astrophysical Research (10 papers), Galaxies: Formation, Evolution, Phenomena (10 papers) and Gamma-ray bursts and supernovae (9 papers). Alex I. Malz is often cited by papers focused on Astronomy and Astrophysical Research (10 papers), Galaxies: Formation, Evolution, Phenomena (10 papers) and Gamma-ray bursts and supernovae (9 papers). Alex I. Malz collaborates with scholars based in United States, Germany and United Kingdom. Alex I. Malz's co-authors include Alexander Gagliano, David W. Hogg, P. Aleo, Gabriella Contardo, Daniel Foreman-Mackey, Rafael Izbicki, J. Zuntz, Peter E. Freeman, Gregory R. Zeimann and M. A. Troxel and has published in prestigious journals such as The Astrophysical Journal, Monthly Notices of the Royal Astronomical Society and The Astrophysical Journal Supplement Series.

In The Last Decade

Alex I. Malz

15 papers receiving 143 citations

Peers

Alex I. Malz
S. Àvila Spain
A. Boucaud France
Caroline Heneka Germany
P. Tsalmantza Germany
C. Sánchez Spain
C. To United States
S. Jouvel United States
R. Joseph United States
Victoria G. Laidler United States
Dezi Liu China
S. Àvila Spain
Alex I. Malz
Citations per year, relative to Alex I. Malz Alex I. Malz (= 1×) peers S. Àvila

Countries citing papers authored by Alex I. Malz

Since Specialization
Citations

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

Fields of papers citing papers by Alex I. Malz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alex I. Malz. 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 Alex I. Malz. The network helps show where Alex I. Malz may publish in the future.

Co-authorship network of co-authors of Alex I. Malz

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

All Works

20 of 20 papers shown
1.
Palaversa, L., Željko Ivezić, Neven Čaplar, et al.. (2025). PhotoD with LSST: Stellar Photometric Distances Out to the Edge of the Galaxy. The Astronomical Journal. 169(3). 119–119.
2.
Leistedt, Boris, M. L. Graham, Andrew J. Connolly, et al.. (2025). Quantifying the Impact of LSST u -band Survey Strategy on Photometric Redshift Estimation and the Detection of Lyman-break Galaxies. The Astrophysical Journal Supplement Series. 281(2). 54–54.
3.
Liu, Xin, Samuel J. Schmidt, Alex I. Malz, et al.. (2025). DeepDISC-photoz: Deep Learning-Based Photometric Redshift Estimation for Rubin LSST. The Open Journal of Astrophysics. 8.
4.
Gagliano, Alexander, et al.. (2025). ORACLE: A Real-time, Hierarchical, Deep Learning Photometric Classifier for the LSST. The Astrophysical Journal. 995(1). 4–4. 1 indexed citations
5.
Malz, Alex I., et al.. (2025). A Holistic Exploration of the Potentially Recoverable Redshift Information of Stage IV Galaxy Surveys. The Astrophysical Journal. 985(2). 227–227. 1 indexed citations
6.
Boucaud, A., Camille Avestruz, É. Aubourg, et al.. (2025). The Blending ToolKit: A simulation framework for evaluation of galaxy detection and deblending. The Open Journal of Astrophysics. 8.
7.
Kalmbach, J. Bryce, et al.. (2024). Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows. The Astronomical Journal. 168(2). 80–80. 12 indexed citations
8.
Lee, J, M. Šako, R. Keßler, & Alex I. Malz. (2024). Astrometric Redshifts of Supernovae. The Astrophysical Journal. 977(2). 199–199.
9.
Villar, V. Ashley, E. Berger, Sebastián Gómez, et al.. (2024). Superphot+: Real-time Fitting and Classification of Supernova Light Curves. The Astrophysical Journal. 974(2). 169–169. 8 indexed citations
10.
Connolly, Andrew J., et al.. (2024). A Python Project Template for Healthy Scientific Software. Research Notes of the AAS. 8(5). 141–141.
11.
Malz, Alex I., Mi Dai, K. A. Ponder, et al.. (2024). Are light curve classification metrics good proxies for SN Ia cosmological constraining power?. Astronomy and Astrophysics. 694. A130–A130. 1 indexed citations
12.
Joachimi, Benjamin, E. Charles, Patricia Larsen, et al.. (2024). Impact of survey spatial variability on galaxy redshift distributions and the cosmological 3 × 2-point statistics for the Rubin Legacy Survey of Space and Time (LSST). Monthly Notices of the Royal Astronomical Society. 535(4). 2970–2997. 1 indexed citations
13.
Lokken, Martine, Alexander Gagliano, Gautham Narayan, et al.. (2023). The simulated catalogue of optical transients and correlated hosts (SCOTCH). Monthly Notices of the Royal Astronomical Society. 520(2). 2887–2912. 10 indexed citations
14.
Gagliano, Alexander, Gabriella Contardo, Daniel Foreman-Mackey, Alex I. Malz, & P. Aleo. (2023). First Impressions: Early-time Classification of Supernovae Using Host-galaxy Information and Shallow Learning. The Astrophysical Journal. 954(1). 6–6. 18 indexed citations
15.
Malz, Alex I., et al.. (2022). The Sensitivity of GPz Estimates of Photo-z Posterior PDFs to Realistically Complex Training Set Imperfections. Publications of the Astronomical Society of the Pacific. 134(1034). 44501–44501. 4 indexed citations
16.
17.
Lawler, Andrew, J. Zuntz, Alex I. Malz, et al.. (2020). Ridges in the Dark Energy Survey for cosmic trough identification. Monthly Notices of the Royal Astronomical Society. 500(1). 859–870. 4 indexed citations
18.
Ishida, Émille E. O., S. González–Gaitán, Rafael S. de Souza, et al.. (2020). Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients. Institutional Repository of the University of Granada (University of Granada). 3115–3124. 4 indexed citations
19.
Souza, Rafael S. de, Émille E. O. Ishida, Alex I. Malz, et al.. (2019). Stress testing the dark energy equation of state imprint on supernova data. Physical review. D. 99(12). 8 indexed citations
20.
Chang, C., M. Wang, Scott Dodelson, et al.. (2018). A unified analysis of four cosmic shear surveys. Monthly Notices of the Royal Astronomical Society. 482(3). 3696–3717. 24 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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