Chang Liu

University of Central Florida · United States

Unclaimed · AI-structured profileOpenAlex

Early-stage profile: agentic AI networking for low-altitude UAVs and statistical-control views of LLM problem solving

papers here

reviewed by Evolve

first-author

corresponding

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works · OpenAlex

h-index · OpenAlex

Bibliometric figures come from OpenAlex (2026-10-14) 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: the OpenAlex snapshot shows h-index 0, i10-index 0 and only 2 works in three years, suggesting the identity under-counts the record; this axis would measure total publication volume and its venue and yearly distribution.

Impact—

Not scored: no citation signal is available — h-index and i10-index both read 0 with only 2 recorded works; this axis would measure citation-based influence such as yearly citations and highly cited papers.

Focus0/100

Scored 0/100, the lowest dispersion signal in the radar: the snapshot splits evenly (0.5/0.5) between AI-native wireless networking and stat.ML, and its five OpenAlex topics — topic modeling, NLP techniques, AI planning, cryptographic implementations, and security verification — each appear once. With only 2 works in three years, no dominant theme has consolidated yet; the record reads as exploratory rather than narrowly focused.

Collaboration74/100

Scored 74/100 on the strength of 40 distinct coauthors in the snapshot and 12 recurring collaborators — including Nguyen Duc Minh Quang, Shuangyang Li, Derrick Wing Kwan Ng, Jiazhang Cai, and Tao Wang — indicating an actively networked, team-based working style. Both cataloged 2026 arXiv papers list Chang Liu in a middle author position without corresponding-author role, consistent with contributing within larger interdisciplinary groups rather than leading small teams.

Activity32/100

Scored 32/100, reflecting a thin recent record: OpenAlex logs only 2 works in the past three years, both in 2026 (countsByYear shows a single entry of 2), and both surface as arXiv preprints rather than peer-reviewed publications. Whether this indicates an early-career ramp-up or an under-linked identity profile cannot be determined from the available data.

Reproducibility20/100

Scored 20/100, with reproMean 20 averaged over 2 reviewed papers, and both rated low reproducibility readiness by Evolve. Concretely, the UAV networking paper rests on a single 10-UAV simulation with no released code and no significance testing, while the LLM problem-solving survey proposes diagnostic hypotheses that remain untested; neither artifact currently supports independent replication.

Research summary

Chang Liu, affiliated with the University of Central Florida (US), appears in the Evolve catalog with two 2026 arXiv preprints that sit at the interface of AI-native wireless networking and machine learning — a split the OpenAlex field distribution reflects exactly at 0.5/0.5. The first, on agentic AI networking for heterogeneous unmanned aerial systems in low-altitude wireless networks, proposes a dual-loop orchestration combining LLM agents, multi-agent reinforcement learning, and non-cooperative game theory anchored by a channel knowledge map; Evolve judges the motivation sound but notes the evidence is a single 10-UAV simulation with no released code or significance tests. The second reframes complex problem solving in large language models as sequence estimation and control, importing process control, statistical decision theory, Bayes filters, and uncertainty quantification into a survey-plus-diagnostic framework that Evolve finds well-organized and candid, though its diagnostic hypotheses remain untested. The bibliometric snapshot — h-index 0, i10-index 0, and only 2 works in three years — suggests an early-stage or under-linked OpenAlex identity rather than a mature citation record; output and impact dimensions are therefore left unscored. Collaboration is the strongest dimension (74/100), with 40 distinct coauthors and 12 recurring partners, while both papers place Chang Liu in a middle author position without corresponding role. Reproducibility scores 20/100, with both papers rated low readiness.

Collaboration pattern

The snapshot records 40 distinct coauthors and 12 recurring collaborators, including Nguyen Duc Minh Quang, Shuangyang Li, Derrick Wing Kwan Ng, Jiazhang Cai, and Tao Wang. Both 2026 cataloged papers place Chang Liu in a middle author position without corresponding-author role, consistent with contributing inside larger, cross-disciplinary teams spanning wireless networking and machine learning rather than leading an independent group.

Research keywords

agentic AI networkinglow-altitude wireless networksLLM agentslarge language modelsmulti-agent reinforcement learningnon-cooperative game theorychannel knowledge mapcomplex problem solvingstatistical decision theoryprocess controlBayes filteruncertainty quantification

Highlights

Dual-loop LLM–MARL orchestration for heterogeneous UAV coexistence
Evolve's reviewed preprint proposes agentic AI networking for heterogeneous unmanned aerial systems in low-altitude wireless networks, combining LLM agents, multi-agent reinforcement learning, non-cooperative game theory, and a channel knowledge map; the evidence base is a single 10-UAV simulation with no released code or significance tests.
Recasting LLM complex problem solving as statistical control
The reviewed survey-and-diagnostic framework reframes complex problem solving in large language models as sequence estimation and control, drawing on process control, statistical decision theory, Bayes filters, and uncertainty quantification; Evolve finds it well-organized and candid, while noting its diagnostic hypotheses remain untested.
Evenly split 2026 output across networking and stat.ML
The snapshot's field distribution divides exactly 0.5/0.5 between wireless communications / AI-native networking and machine learning (stat.ML), with five OpenAlex topics each appearing once — an exploratory, cross-domain 2026 record of 2 works.

Timeline

2026Co-authored an arXiv preprint on agentic AI networking for heterogeneous unmanned aerial systems in low-altitude wireless networks (middle author position, non-corresponding).
2026Co-authored the arXiv survey 'Complex Problem Solving in Large Language Models: A Statistical Control Survey and Diagnostic Framework' (middle author position, non-corresponding).
2026OpenAlex snapshot records 2 works in the last three years, both in 2026, split evenly between AI-native wireless networking and machine learning (stat.ML).

Papers in the Evolve catalog

Frequent co-authors

Nguyen Duc Minh Quang×1 Shuangyang Li×1 Derrick Wing Kwan Ng×1 Jiazhang Cai×1 Tao Wang×1 Ruidong Zhang×1 Siyuan Li×1 Terry Ma×1 Luyang Fang×1 Haoran Lu×1 Huimin Cheng×1 Yingchuan Zhang×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.

Identity is resolved from OpenAlex only; 'Chang Liu' is a common name and no ORCID is linked, so the snapshot (h-index 0, 2 works in three years, 40 coauthors) may under-capture this person's full record.
All bibliometrics come from the OpenAlex snapshot; no citation-impact data is available, so the output and impact dimensions are left unscored rather than estimated.
Only two 2026 arXiv preprints reviewed by Evolve are in scope; no peer-reviewed venue, funding, or award information is covered.
The reproducibility score (reproMean 20) reflects Evolve reviews of these two papers only, both rated low readiness; it is not a general replication audit.