Jonathan H. Chan

King Mongkut's University of Technology Thonburi · Thailand

Unclaimed · AI-structured profileOpenAlex

AI researcher at KMUTT building evaluation frameworks for legal reasoning and explainable medical image segmentation

papers here

reviewed by Evolve

first-author

corresponding

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works · OpenAlex

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 scored yet: the OpenAlex snapshot shows no usable output counts (h-index and i10-index both 0, only 2 works in the last three years), so this axis — which would measure publication volume and productivity over time — cannot be estimated from the data at hand.

Impact—

Not scored: the snapshot carries no citation evidence (h-index and i10-index both 0, no per-paper or per-year citation counts), so this axis — measuring citation uptake and scholarly influence of the 2026 preprints — is left unscored rather than estimated.

Focus0/100

OpenAlex assigns the snapshot an even 0.5/0.5 split between legal NLP (computational law) and medical image analysis (computer vision), and the five topic labels each appear once, yielding a focus score of 0 — no dominant topic cluster. The two catalogued papers sit in different subfields, connected mainly by a shared evaluation-and-verification methodology rather than a concentrated subject area.

Collaboration32/100

The snapshot records 4 distinct coauthors and a collaboration score of 32, indicating a small active network. Both 2026 preprints are last-authored without a corresponding-author role, consistent with a senior supervisory position on compact teams; the recurring co-author pool includes Jiakang Xu, Wantong Huo, Udom Silparcha, and Jiyoo Noh.

Activity32/100

Activity is scored 32 on a thin recent record: countsByYear shows only 2 works, both in 2026, and recent3yWorks is likewise 2, both arXiv preprints. This reads as a newly visible or recently active period in OpenAlex rather than a sustained multi-year cadence, so the score should be interpreted as low recent volume, not inactivity.

Reproducibility55/100

Evolve's reproducibility assessment covers 2 reviewed papers with a mean readiness of 55, both rated medium. GRACE provides a 1,915-example grounded dataset and a verification pipeline but evaluates against its own teacher; the echocardiography framework is honest yet descriptive, with pseudo-label circularity and thin reproducibility detail. Methodological transparency is a recurring strength; complete artifact support is not yet demonstrated.

Research summary

Jonathan H. Chan is a researcher at King Mongkut's University of Technology Thonburi (KMUTT), Thailand; this profile is name-resolved from public OpenAlex bibliographic data, since the name is common and no ORCID is linked. The visible record centers on two 2026 arXiv preprints with last-author, non-corresponding positions, splitting evenly (0.5/0.5) between legal NLP (computational law) and medical image analysis (computer vision). The first, GRACE, contributes a 1,915-example grounded Canadian legal dataset with a citation-verification pipeline; Evolve's review notes that evaluation against its own teacher model limits correctness claims. The second proposes a quantitative Grad-CAM framework for temporal explainability in echocardiographic video segmentation — described as honest but descriptive, with pseudo-label circularity and thin reproducibility. Topic assignments (topic modeling, medical image segmentation, AI in healthcare and education, explainable AI, and AI in law) echo a consistent agenda: building evaluation and verification frameworks rather than new model architectures. OpenAlex records 2 works in the last three years, 4 distinct coauthors, and h-index and i10-index of 0, so output and impact are left unscored; both reviewed papers carry medium reproducibility readiness (mean 55).

Collaboration pattern

The visible collaboration footprint is compact: 4 distinct coauthors in the OpenAlex snapshot, with a collaboration score of 32. Both catalogued 2026 papers are last-authored, non-corresponding contributions from small teams, consistent with a senior supervisory role. The recurring co-author pool — Jiakang Xu, Wantong Huo, Udom Silparcha, and Jiyoo Noh — spans the legal NLP and medical imaging tracks, suggesting one working group operating across both domains rather than two separate clusters.

Research keywords

adversarial legal reasoningstatutory reasoningCanadian lawdataset constructioncitation verificationLLM-as-judgeexplainable AI (XAI)Grad-CAMechocardiographyvideo segmentationtemporal consistencyConvLSTM U-Net

Highlights

GRACE: a grounded, verified dataset for adversarial reasoning over Canadian law
The 2026 last-author preprint contributes a 1,915-example grounded Canadian legal dataset with a citation-verification pipeline; Evolve rates its reproducibility medium but flags that evaluation against its own teacher model limits correctness claims.
Quantifying temporal explainability in echocardiographic video segmentation
A Grad-CAM-based framework scores the temporal stability of explanations for ConvLSTM U-Net echo video segmentation; Evolve finds it honest but descriptive, noting pseudo-label circularity and thin reproducibility at medium readiness.
One evaluation-first agenda across two fields
Both 2026 releases are last-authored and prioritize evaluation and verification frameworks over new architectures, splitting evenly (0.5/0.5) between legal NLP and medical image analysis, with both rated medium reproducibility (mean 55).

Timeline

2026Last-author preprint GRACE appears on arXiv, introducing grounded adversarial reasoning over Canadian law with a 1,915-example dataset and a citation-verification pipeline.
2026Last-author preprint on quantitative temporal explainability for echocardiographic video segmentation appears on arXiv, built around Grad-CAM and a ConvLSTM U-Net.
2026OpenAlex registers both works in its 2026 countsByYear (recent3yWorks = 2, 4 distinct coauthors), with an even 0.5/0.5 split between legal NLP and medical image analysis.

Papers in the Evolve catalog

Frequent co-authors

Jiakang Xu×1 Wantong Huo×1 Udom Silparcha×1 Jiyoo Noh×1

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.

Bibliometric indicators come from a name-resolved OpenAlex snapshot only; 'Jonathan H. Chan' is a common name and no ORCID is linked, so identity confidence rests on the OpenAlex disambiguation.
Output and impact are unscored: h-index and i10-index are both 0 in the snapshot and no per-paper citation counts are available.
The Evolve catalog holds only 2 papers, both 2026 arXiv preprints; earlier or non-arXiv work is not represented in this profile.
Collaboration and activity scores rest on just 2 works and 4 distinct coauthors, so they are sensitive to small changes in the underlying data.