This map shows the geographic impact of Milton Halem'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 Milton Halem with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Milton Halem more than expected).
This network shows the impact of papers produced by Milton Halem. 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 Milton Halem. The network helps show where Milton Halem may publish in the future.
Co-authorship network of co-authors of Milton Halem
This figure shows the co-authorship network connecting the top 25 collaborators of Milton Halem.
A scholar is included among the top collaborators of Milton Halem 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 Milton Halem. Milton Halem is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Halem, Milton, et al.. (2019). Compressive Geospatial Analytics. AGU Fall Meeting Abstracts. 2019.2 indexed citations
5.
Halem, Milton, et al.. (2019). Satellite Data Fusion of Multiple Observed XCO2 using Compressive Sensing and Deep Learning. AGU Fall Meeting Abstracts. 2019.2 indexed citations
6.
Nearing, Grey, et al.. (2018). Machine Learning for Carbon Monitoring. AGU Fall Meeting Abstracts. 2018.
7.
Nguyen, Phuong T. & Milton Halem. (2018). Prediction of CO 2 flux using Long Short Term Memory (LSTM) Recurrent Neural Networks with data from Flux towers and OCO-2 remote sensing. AGU Fall Meeting Abstracts. 2018.1 indexed citations
Pelissier, Craig, et al.. (2016). Image Registration and Data Assimilation as a QUBO on the D-Wave Quantum Annealer. AGU Fall Meeting Abstracts. 2016.1 indexed citations
10.
Price, Adam, et al.. (2014). AsonMaps: A platform for aggregation visualization and analysis of disaster related human sensor network observations.. ISCRAM.11 indexed citations
11.
Nearing, Grey, et al.. (2014). Data Assimilation on a Quantum Annealing Computer: Feasibility and Scalability. 2014 AGU Fall Meeting. 2014.1 indexed citations
Halem, Milton, et al.. (2011). Assimilation of Real-Time Satellite And Human Sensor Networks for Modeling Natural Disasters. AGUFM. 2011.1 indexed citations
14.
Halem, Milton, et al.. (2010). Collaborative Science: Human Sensor Networks for Real-time Natural Disaster Prediction. AGUFM. 2010.1 indexed citations
15.
Chapman, David, et al.. (2009). Towards Producing a 40 Year Earth Science Data Record of IR Radiances. AGU Fall Meeting Abstracts. 2009.1 indexed citations
16.
Golpayegani, N., et al.. (2008). Cloud Computing Infusion for Generating ESDRs of Visible Spectra Radiances. AGU Fall Meeting Abstracts. 2008.1 indexed citations
17.
Halem, Milton, et al.. (2007). SOAR: A System for the Analysis of Atmospheric Radiances. AGUFM. 2007(52).1 indexed citations
Halem, Milton, Michael Ghil, Robert Atlas, Joel Susskind, & William J. Quirk. (1978). The GISS sounding temperature impact test. NASA Technical Reports Server (NASA).10 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.