Leopold Müller

University of Bayreuth · Germany

Unclaimed · AI-structured profileOpenAlex

Information systems researcher at Bayreuth building generative-AI pipelines and evidence for personalized advertising

papers here

reviewed by Evolve

first-author

corresponding

—

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—

No output score yet: Evolve lacks the publication-volume data this axis measures, such as total works, venue types, and the yearly output trajectory. Current OpenAlex traces show only 7 recent works and no curated productivity profile.

Impact—

No impact score yet: the citation-based measures this axis captures — citations, h-index trajectory, field-normalized impact — are unavailable in the current snapshot. With an h-index of 1 and an i10-index of 0, the record is too young to score.

Focus0/100

Scores 0 because the work is spread evenly rather than concentrated: the OpenAlex field distribution splits in exact thirds across generative AI in marketing, AI adoption in professional services, and AI & marketing. Topic counts reinforce the spread — digital marketing and social media (3), AI in service interactions (3), marketing and advertising strategies (2), plus single counts for topic modeling and software engineering research. This breadth reads as early-stage exploration rather than settled specialization.

Collaboration39/100

Scores 39, a moderate level: 6 distinct co-authors appear across the 7 works of the last three years, forming a stable Bayreuth-centered circle that includes Niklas Kühl, Claas Christian Germelmann, Victor Kolominsky-Rabas, Claudius Budcke, and Felicia Perpina. All three reviewed preprints are middle-authored team efforts rather than solo projects, indicating genuine but still small-scale collaboration.

Activity61/100

Scores 61 on rising output: OpenAlex counts 2 works in 2025 and 5 in 2026, with 7 works over the last three years — more than doubling year over year. The 2026 cluster contains all three Evolve-reviewed arXiv preprints, suggesting an intensive current production phase, though the absolute volume remains that of an early-career researcher.

Reproducibility32/100

Scores 32, based on Evolve's assessment of 3 papers (mean 32/100). The personalization experiment reaches medium readiness through preregistration and its N=100 sample, while the AdMan pipeline and the interview study are rated low — no baselines with single-run, judge-limited evaluation, and a qualitative design respectively. Transparency practices exist but are uneven across the portfolio.

Research summary

Leopold Müller is an early-stage researcher at the University of Bayreuth (Germany) whose work sits at the intersection of information systems, marketing, and human-computer interaction, applied to generative AI. OpenAlex records 7 works in the last three years — 2 in 2025 and 5 in 2026 — with an h-index of 1 and an i10-index of 0, consistent with a career at its starting phase. The Evolve-reviewed catalog holds three 2026 arXiv preprints, on all of which Müller is a middle author: a preregistered N=100 experiment on personalization in AI-generated advertising imagery, a multi-agent pipeline ('I Am AdMan') for automatic generation of personalized ad images, and an eleven-interview Gioia-methodology study of generative AI adoption in German real estate marketing. Topic data concentrates on digital marketing and social media (3) and AI in service interactions (3), with side excursions into marketing and advertising strategies, topic modeling, and software engineering research. The field distribution splits evenly in thirds across generative AI in marketing, AI adoption in professional services, and AI & marketing, yielding a focus score of 0. Reproducibility readiness is modest (mean 32/100 across 3 papers), reflecting limited baselines, single-run evaluation, and qualitative designs. Collaboration is Bayreuth-centered: 6 distinct co-authors including Niklas Kühl, Claas Christian Germelmann, Victor Kolominsky-Rabas, Claudius Budcke, and Felicia Perpina, with an activity score of 61 driven by the 2026 output cluster.

Collaboration pattern

Collaboration is small-scale and Bayreuth-centered: 6 distinct co-authors across the last three years' 7 works (collaboration score 39). The frequent-coauthor list centers on Niklas Kühl and Claas Christian Germelmann, with Victor Kolominsky-Rabas, Claudius Budcke, and Felicia Perpina also recurring. All three reviewed preprints position Müller as a middle author on team projects spanning information systems, marketing, and applied field studies — a pattern of interdisciplinary, single-institution collaboration rather than large-consortium work.

Research keywords

generative AIadvertising personalizationtext-to-image generationmulti-agent systemsLLM-as-judgedynamic creative optimizationpersonalization-privacy paradoxperceived creepinesssemi-structured interviewsGioia methodologyhuman-in-the-loopdigital marketing

Highlights

Preregistered evidence: moderate personalization wins in AI-generated ad imagery
A preregistered N=100 experiment finds that GenAI advertising imagery works best at moderate personalization; Evolve deems the study credible while noting that level-2 personalization effects are confounded with image quality. Reproducibility readiness: medium.
I Am AdMan: a multi-agent pipeline for personalized advertising imagery
Proposes a multi-agent pipeline that turns customer data into personalized advertising images; Evolve calls it a credible feasibility demonstration but flags missing baselines, single-run evaluation, and judge-limited assessment. Reproducibility readiness: low.
Mapping GenAI adoption in German real estate marketing
An eleven-interview study using the Gioia methodology maps current versus potential generative AI use cases in German real estate marketing; Evolve rates it credible qualitative work, limited by an AI-user-only sample and a human-in-the-loop framing. Reproducibility readiness: low.

Timeline

2026Releases a preregistered N=100 study on enabling and understanding personalization in AI-generated advertising imagery (arXiv).
2026Publishes the I Am AdMan pipeline for automatic generation of personalized advertising imagery (arXiv).
2026Publishes an eleven-interview study on generative AI use cases, adoption, and constraints in German real estate marketing (arXiv).

Papers in the Evolve catalog

Frequent co-authors

Victor Kolominsky-Rabas×3 Claudius Budcke×2 Niklas Kühl×2 Felicia Perpina×1 Claas Christian Germelmann×1 Niklas Kühl×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 figures come from OpenAlex only; no ORCID is linked, so for this common German name the profile is name-resolved from public bibliographic data.
Output and impact dimensions are unscored because the snapshot's citation and productivity data are insufficient (h-index 1, i10-index 0, no citation history).
All three reviewed works are 2026 arXiv preprints rather than peer-reviewed publications; OpenAlex also counts 2 works in 2025 that are not part of the reviewed catalog.
The reproducibility score reflects Evolve's assessment of only 3 papers, so it should be read as an early, low-confidence signal.