Daniel Rothchild

504 citations
3 papers · 167 · 1 hit paper · h-index 2

Impact in

Papers in

Daniel Rothchild

3 papers receiving 162 citations

Daniel Rothchild's Hit Papers

The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink 2022 · 164 citations
1640+1+2Years since publication50100150

Peers

Daniel Rothchild
Comparison fields: 5 of 62
  • Health Informatics 7
  • Computer Science Applications 12
  • Safety Research 16
  • Artificial Intelligence 51
  • Hardware and Architecture 10
Replace Lennart Heim with:
Lennart Heim Canada
Gerard O’Regan Kyrgyzstan
Jaime Sevilla Canada
Marius Hobbhahn United Kingdom
Marïa José Ramírez-Quintana Spain
Panagiotis Papadopoulos Greece
Viet-Man Le Austria
Daniel Hesslow France
Alberto Blanco-Justicia Spain
Martin Briesch Germany
Daniel Rothchild relative to Lennart Heim Canada Lennart Heim's profile →
Citations per field
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Lennart Heim · 1×
Citations per year

Countries citing papers authored by Daniel Rothchild

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Rothchild

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 13 scholars most cited alongside Daniel Rothchild, 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 Daniel Rothchild Line = papers co-authored together Daniel Rothchild links everyone, so they are left out of the graph.

All Works

3 of 3 papers shown
#Work
1
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
Hit paper breakdown →
2022164
2 20192
3 20241

About Daniel Rothchild

Daniel Rothchild is a scholar working on Atomic and Molecular Physics, and Optics, Molecular Biology, Astronomy and Astrophysics, Instrumentation and Artificial Intelligence, having authored 3 papers that have together received 167 indexed citations. Recurring topics across this work include Adaptive optics and wavefront sensing (1 paper), Explainable Artificial Intelligence (XAI) (1 paper), Machine Learning in Materials Science (1 paper), Advanced Chemical Physics Studies (1 paper), Astronomy and Astrophysical Research (1 paper), Stellar, planetary, and galactic studies (1 paper) and Protein Structure and Dynamics (1 paper). The work is most often cited by research in Health Informatics (7 citations), Computer Science Applications (12 citations), Safety Research (16 citations), Artificial Intelligence (51 citations) and Hardware and Architecture (10 citations). Daniel Rothchild has collaborated with scholars based in United States. Frequent co-authors include Joseph E. Gonzalez, David S. Patterson, Liang Chen, Lluís-Miquel Munguía, David R. So, Quoc V. Le, Urs Hölzle, Maud Texier, Jeff Dean and C. W. Stubbs. Their work appears in journals such as Publications of the Astronomical Society of the Pacific, Chemical Science and Computer.

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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