Geospatial AI researcher at UT Austin working on location embeddings, geographic INRs, and Earth-system simulation.
2
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
—
works · OpenAlex
30
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-16) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Not yet scored. Work counts exist in the raw feed (40 works in 2026; 82 over 2024–2026), but Evolve's composite output axis — venue quality, publication volume, and author-role weighting — is not computed for this profile yet.
Impact—
Not yet scored. The raw feed carries citation indices (h-index 30, i10-index 66), but Evolve's composite impact axis is pending; it would measure citation influence, field-normalized uptake, and landmark works.
Focus0/100
The radar focus score is 0, reflecting an even 50/50 split between geospatial machine learning / remote sensing and machine learning for Earth-system simulation — breadth across two adjacent tracks rather than one narrow lane. Topic counts reinforce this spread: Geographic Information Systems Studies (63), Topic Modeling (36), Semantic Web and Ontologies (32), Advanced Graph Neural Networks (29), and Data Management and Algorithms (25).
Collaboration58/100
Collaboration scores 58 (moderate) with 17 distinct coauthors in this snapshot and 12 recurring collaborators. The roster spans the geospatial deep-learning community — Isaac Corley, Esther Rolf, Konstantin Klemmer, Nathan Jacobs, Hannah Kerner, Evan Shelhamer, Marc Rußwurm, Nils Lehmann, Arjun Rao — alongside Zhihao Wang, Ruichen Wang, and Ruohan Li. Both catalog papers list Mai as a middle author and not corresponding, consistent with team-based projects.
Activity100/100
Activity is the strongest axis at 100. The works-by-year record climbs from 3 (2014) and 9 (2018) to 11 (2021), 20 (2022), 25 (2023), 27 (2025), and 40 in 2026 — the highest year on record — with 82 works in the most recent three-year window. Both Evolve-reviewed entries are 2026 arXiv preprints, matching this late surge.
Reproducibility78/100
Reproducibility scores 78, a mean over the 2 papers Evolve has assessed (reproMean 78, reproPapers 2). The split is uneven: MIND the Gap is rated high readiness — its CoordBench evaluation supports re-implementation — while the Earth-system world model paper is medium, with Evolve flagging that intervention validation remains circular.
Research summary
Gengchen Mai, based at The University of Texas at Austin (US), works across two evenly weighted tracks — geospatial machine learning / remote sensing and machine learning for Earth-system simulation (50/50 in the field distribution). The OpenAlex-derived record spans 2014 to 2026 and shows steeply rising output: from 3 works in 2014 and single digits through 2020 to 11 (2021), 20 (2022), 25 (2023), 27 (2025), and 40 in 2026, with 82 works in the last three years — an activity score of 100. Topic clusters are led by Geographic Information Systems Studies (63), Topic Modeling (36), Semantic Web and Ontologies (32), Advanced Graph Neural Networks (29), and Data Management and Algorithms (25). The raw feed reports an h-index of 30 and an i10-index of 66, though Evolve's composite output and impact scores are not yet computed. Collaboration scores 58 with 17 distinct coauthors in this snapshot. Reproducibility averages 78 across the two Evolve-assessed papers. Both catalog entries are 2026 arXiv preprints with middle-author placement: MIND the Gap (geographic implicit neural representations, high reproducibility readiness) and an Earth-system world model for ecosystem what-if simulation (medium).
Collaboration pattern
Mai's collaboration footprint is moderate (score 58, 17 distinct coauthors in this snapshot) but densely connected to the geospatial deep-learning community: Isaac Corley, Esther Rolf, Konstantin Klemmer, Nathan Jacobs, Hannah Kerner, Evan Shelhamer, Marc Rußwurm, Nils Lehmann, and Arjun Rao recur, alongside Zhihao Wang, Ruichen Wang, and Ruohan Li. On both Evolve-reviewed 2026 preprints Mai sits in a middle-author, non-corresponding slot — a pattern consistent with contributed expertise inside larger, student-led teams.
Research keywords
Highlights
Adjustable-scale geographic implicit neural representations (MIND the Gap)
Action-conditioned world models for ecosystem what-if simulation
Timeline
- 2014Earliest year in the works-by-year record: 3 works logged.
- 20189 works, the strongest year of the early phase.
- 202111 works — output accelerates into a sustained growth phase.
- 202325 works, a step change from 20 in 2022.
- 202527 works on the way to the record-setting year.
- 202640 works — the highest year in the record; two Evolve-reviewed arXiv preprints appear: MIND the Gap and the Earth-system world model.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometrics are derived from OpenAlex only; this profile is name-resolved from public bibliographic data.
- Evolve's output and impact dimensions are unscored; the h-index (30) and i10-index (66) come from the raw feed and are not independently verified.
- The reproducibility mean (78) covers only the 2 papers Evolve has assessed so far.
- Both catalog papers are 2026 arXiv preprints; Evolve verdicts are internal review notes, not peer review.