James G. Malcolm
Impact in
- Computational Mathematics top 2%
- Neurology top 2%
- Traumatic Brain Injury and Neurovascular Disturbances
Papers in
-
- Tensor decomposition and applications 8
- Neurology 16
- Traumatic Brain Injury and Neurovascular Disturbances 9
- Co-authors
- Yogesh RathiMartha E. ShentonAllen TannenbaumFaiz U. AhmadGustavo PradillaRima S. RindlerJonathan A GrossbergJason Chu
- Journals
- Neurosurgery (8 papers)World Neurosurgery (6 papers)Journal of Neuro-Oncology (2 papers)The American Surgeon (1 paper)Magnetic Resonance in Medicine (1 paper)
- Partner nations
- United StatesCanadaIsrael
In The Last Decade
James G. Malcolm
60 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 120
- Computational Mathematics 58
- Neurology 555
- Radiology, Nuclear Medicine and Imaging 629
- Computer Vision and Pattern Recognition 217
- Cognitive Neuroscience 191
Countries citing papers authored by James G. Malcolm
This map shows the geographic impact of James G. Malcolm'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 James G. Malcolm with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James G. Malcolm more than expected).
Fields of papers citing papers by James G. Malcolm
This network shows the impact of papers produced by James G. Malcolm. 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 James G. Malcolm. The network helps show where James G. Malcolm may publish in the future.
Co-authorship network
The 25 scholars most cited alongside James G. Malcolm, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 3 | |
| 3 | 2023 | 1 | |
| 4 | 2020 | 14 | |
| 5 | 2020 | 13 | |
| 6 | 2017 | 45 | |
| 7 | 2017 | 1 | |
| 8 | 2017 | 13 | |
| 9 | 2017 | 9 | |
| 10 | 2016 | 38 | |
| 11 | 2011 | 6 | |
| 12 | 2010 | 5 | |
| 13 | 2010 | 22 | |
| 14 | 2010 | 173 | |
| 15 | 2009 | 30 | |
| 16 | 2009 | 26 | |
| 17 | 2009 | 10 | |
| 18 | 2008 | 7 | |
| 19 | 2008 | 3 | |
| 20 | 2007 | 16 |
About James G. Malcolm
James G. Malcolm is a scholar working on Computational Mathematics, Neurology, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Pathology and Forensic Medicine, having authored 65 papers that have together received 1.7k indexed citations. Recurring topics across this work include Advanced Neuroimaging Techniques and Applications (14 papers), Medical Image Segmentation Techniques (10 papers), Spine and Intervertebral Disc Pathology (9 papers), Cerebrospinal fluid and hydrocephalus (9 papers), Traumatic Brain Injury and Neurovascular Disturbances (9 papers), Tensor decomposition and applications (8 papers), Advanced MRI Techniques and Applications (8 papers) and Spinal Fractures and Fixation Techniques (7 papers). The work is most often cited by research in Computational Mathematics (58 citations), Neurology (555 citations), Radiology, Nuclear Medicine and Imaging (629 citations), Computer Vision and Pattern Recognition (217 citations) and Cognitive Neuroscience (191 citations). James G. Malcolm has collaborated with scholars based in United States, Canada and Israel. Frequent co-authors include Yogesh Rathi, Martha E. Shenton, Allen Tannenbaum, Faiz U. Ahmad, Gustavo Pradilla, Rima S. Rindler, Jonathan A Grossberg, Jason Chu, Marco Reisert and Ting‐Shuo Yo. Their work appears in journals such as Neurosurgery, World Neurosurgery, Journal of Neuro-Oncology, The American Surgeon and Magnetic Resonance in Medicine.
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.