Youting Wang

Mountain View Clinical Research · United States

Unclaimed · AI-structured profileOpenAlex

Early-stage ML researcher working across image denoising, explainable AI, and network-based retail analytics

papers here

reviewed by Evolve

first-author

corresponding

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works · OpenAlex

h-index · OpenAlex

Bibliometric figures come from OpenAlex (2026-10-15) 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—

Output is not scored: beyond a recent three-year count of 11 works, the full publication record this axis needs — career-wide output volume, venue mix, and yearly production — is unavailable in the current data, so no estimate is made.

Impact—

Impact is not scored: current citation signals (h-index 0, i10-index 0) reflect an early, sparse record rather than measured influence. This axis would assess citation uptake and scholarly reach once a longer citation history accumulates.

Focus0/100

Focus scores 0, reflecting a profile spread evenly across two fields: the OpenAlex field distribution assigns 0.5 to computer vision/machine learning (image denoising) and 0.5 to data mining/network science (retail analytics). Topic counts reinforce this breadth — adversarial robustness (6), explainable AI (4), privacy-preserving technologies (3), topic modeling (2), and AI ethics (2). The two reviewed papers sit in different fields, so no single dominant research line has consolidated yet.

Collaboration48/100

Collaboration scores 48, a mid-range value built on 10 distinct co-authors in the recent window. Author-list roles differ by project: last author on the co-purchase network paper (a position often associated with senior or supervising roles) and middle author on the Noise2Noise denoising study. Recurring collaborators include Dingyan Shang, Zhenyu Xu, Bonan Shen, Bowen Liu, Xiao Han, Zhen Zhang, Xin Zhao, Jiechun Lei, and Moxuan Zheng, indicating team-based rather than solitary work.

Activity72/100

Activity scores 72. OpenAlex records 11 works in the last three years, with counts-by-year listing all 11 in 2026 — an intense recent burst rather than a long cumulative record. The two Evolve-reviewed papers are both 2026 arXiv releases, consistent with this concentrated output. The score reflects recent volume; the absence of earlier years in the counts-by-year series means long-term pace cannot be verified from available data.

Reproducibility78/100

Reproducibility scores 78, computed as the mean readiness rating across 2 Evolve-reviewed papers. The Noise2Noise denoising study is rated high readiness — described as a careful, credible empirical study — while the co-purchase network paper is rated medium, with Evolve noting a plausible, well-designed analysis but untested inference and no released code. The spread shows reproducibility practices vary across the portfolio rather than being uniformly strong.

Research summary

Youting Wang is an early-stage researcher affiliated with Mountain View Clinical Research (US) whose public bibliographic record, resolved from OpenAlex, centers on machine learning methods and applied data mining. The record shows 11 works in the recent three-year window, all logged in 2026, with topic weights spread across adversarial robustness in machine learning (6), explainable AI (4), privacy-preserving technologies (3), topic modeling (2), and AI ethics (2). Field distribution is evenly split — 0.5 computer vision/machine learning (image denoising) and 0.5 data mining/network science (retail analytics) — yielding a focus score of 0 and a profile not yet consolidated around one line. Evolve has reviewed two 2026 arXiv papers: a Noise2Noise denoising study finding that training-pair distributions dominate loss choice (high reproducibility readiness), and a co-purchase network analysis arguing that support thresholds, not algorithms, limit rare-association recovery (medium readiness; Wang listed as last author). Citation footprint is minimal (h-index 0, i10-index 0), so output and impact are left unscored, while activity (72) and reproducibility (78) are the stronger axes and collaboration sits mid-range (48) with 10 distinct co-authors. Overall this reads as a fast-moving but very recent research profile whose long-term trajectory cannot yet be judged from the available data.

Collaboration pattern

Collaboration scores 48 (mid-range). The recent window includes 10 distinct co-authors, with recurring names such as Dingyan Shang, Zhenyu Xu, Bonan Shen, Bowen Liu, Xiao Han, Zhen Zhang, Xin Zhao, Jiechun Lei, and Moxuan Zheng. Roles vary by project: last author on the co-purchase network paper, middle author on the denoising study, suggesting a mix of senior and contributing positions. Team-based co-authorship across both fields is the dominant pattern, though a stable long-term core team is not yet visible.

Research keywords

Adversarial robustness in machine learningExplainable artificial intelligence (XAI)Privacy-preserving technologiesTopic modelingEthics and social impacts of AIMarket basket analysisAssociation rule miningCo-purchase networksNetwork backbone extractionImage denoising (Noise2Noise)Self-supervised learningLoss function choice (L1 vs L2)

Highlights

Training-pair distributions, not loss choice, dominate Noise2Noise denoising
In a 2026 arXiv study (middle author), the team shows that the training-pair distribution governs self-supervised denoising performance — an ~8 dB regime gap versus a sub-dB effect from loss choice (L1 vs L2) — based on experiments with real camera noise. Evolve rates it a careful, credible empirical study with high reproducibility readiness.
Support thresholds, not algorithms, limit rare-association recovery in co-purchase networks
As last author on a 2026 arXiv paper, Wang's team argues that minimum support thresholds, not the mining algorithm (e.g., Apriori), block rare co-purchase associations; noise-corrected (NC) filtering recovers 80–100% of rare associations with persistent significance. Evolve finds the design plausible but flags untested inference and no released code, requesting revision.
Reproducibility signals: solid empirical work, pending code release
Across the two reviewed papers, reproducibility readiness averages 78: the denoising study is rated high, while the co-purchase network paper is rated medium because its inference is untested and no code is released — the main revision Evolve requests before the claim can be considered settled.

Timeline

2026Noise2Noise Revisited: Training Pair Distributions Dominate Loss Choice in Self-Supervised Denoising appears on arXiv, with Wang as a middle author; Evolve calls it a careful, credible empirical study with high reproducibility readiness.
2026Support Thresholds, Not Algorithms, Limit Rare-Association Recovery in Co-Purchase Networks appears on arXiv with Wang as last author; the Evolve verdict requests revision over untested inference and missing code.
2026OpenAlex logs 11 works in the recent three-year window, all recorded in 2026 — the entire visible output concentrates in this single year.

Papers in the Evolve catalog

Frequent co-authors

Dingyan Shang×1 Zhenyu Xu×1 Youting Wang×1 Bonan Shen×1 Bowen Liu×1 Xiao Han×1 Zhen Zhang×1 Xin Zhao×1 Jiechun Lei×1 Moxuan Zheng×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.

All bibliometrics come from OpenAlex only; no ORCID is linked, so the profile is name-resolved from public bibliographic data and the name Youting Wang may be ambiguous.
Citation metrics are minimal (h-index 0, i10-index 0) and counts-by-year covers only 2026, so output and impact are unscored and any long-term trajectory is unverifiable.
Only two papers have been reviewed by Evolve, both 2026 arXiv preprints; peer-reviewed status is unknown.
The listed institution (clinical research) differs from the ML/data-mining topical profile, and the researcher's own name appears in the co-author list — possible entity-resolution artifacts.