UT Austin multi-agent systems researcher: coordination robustness, social laws, and ad hoc teamwork tooling.
2
papers here
2
reviewed by Evolve
1
first-author
0
corresponding
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works · OpenAlex
3
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—
Unscored: aggregate publication-volume data is insufficient in the underlying record. This axis would measure total works, annual output, and authorship roles; only per-year counts and 5 works in the last three years are visible.
Impact—
Unscored: citation-impact data is insufficient. The only related raw signals are h-index 3 and i10-index 1 — too thin to characterize influence. This axis would measure citations and field-normalized impact percentiles.
Focus0/100
Scores 0/100, reflecting an even 50/50 field-distribution split between multi-agent systems/AI planning and multi-agent reinforcement learning. Both sit inside one broader area, so the zero reads as breadth across adjacent subfields rather than scattered work; OpenAlex topics confirm a coherent center, led by Reinforcement Learning in Robotics (10), Multi-Agent Systems and Negotiation (7), and Distributed Control Multi-Agent Systems (6).
Collaboration59/100
Scores 59/100 from 18 distinct coauthors in the OpenAlex record. The frequent-collaborator list pairs established group leads (Peter Stone, Ufuk Topcu, Erez Karpas) with earlier-career peers such as Caleb Probine, Tyler Lee, and Jeffrey Chen, plus recurring partners including Muhammad Arrasy Rahman, Caroline Wang, and Jiaxun Cui — a pattern consistent with lab-based team science spanning planning and learning efforts.
Activity52/100
Scores 52/100. Per-year counts show a record starting in 2018 (3 works), peaking at 4 works in 2021, then running at 1-2 works per year through 2022-2026. Recent output is modest but continuous: 5 works in the last three years, including both 2026 arXiv papers cataloged by Evolve — an early-career cadence rather than a dormant record.
Reproducibility78/100
Scores 78/100, the mean readiness across the 2 Evolve-reviewed papers. JaxAHT rates high — an open JAX library covering the full ad hoc teamwork lifecycle with compute-controlled benchmarks — while the social-laws paper rates medium: sound alpha-robustness theory and an MDP-reduction verifier, but evaluation limited to toy two-agent grid worlds. Together they suggest code-first, benchmark-aware working habits.
Research summary
Rolando Fernandez is a multi-agent systems researcher at The University of Texas at Austin whose OpenAlex-resolved record centers on coordination under uncertainty. Bibliometric topics are led by Reinforcement Learning in Robotics (10 works) and Multi-Agent Systems and Negotiation (7), with further clusters in distributed multi-agent control, AI in games, and path planning. Two 2026 arXiv papers anchor the Evolve catalog. As first author of 'Social Laws for Multi-agent Coordination in Stochastic Environments,' he extends social-law robustness to stochastic, reward-based settings via alpha-robustness and an MDP-reduction verifier — a theoretically sound contribution that Evolve notes is evaluated only on toy two-agent grid worlds. As a middle author on 'JaxAHT,' he contributes to the first JAX library covering the full ad hoc teamwork lifecycle; its compute-controlled benchmark is convincing, though the headline 95x speedup rests on extrapolation. The record is early-stage: h-index 3 and i10-index 1, with impact and aggregate output unscored. Field distribution splits evenly between multi-agent systems/AI planning and multi-agent reinforcement learning, and 18 distinct coauthors include Peter Stone, Ufuk Topcu, and Erez Karpas.
Collaboration pattern
Collaboration is dense for an early-stage record: 18 distinct coauthors, with a frequent-collaborator core of Caleb Probine, Tyler Lee, Jeffrey Chen, Caroline Wang, and Zelal Su Mustafaoglu alongside established leads Peter Stone, Ufuk Topcu, and Erez Karpas. Concurrent planning-side (social laws) and learning-systems (JaxAHT) papers suggest parallel working groups, with Muhammad Arrasy Rahman, Jiaxun Cui, and Lingyun Xiao as further recurring partners.
Research keywords
Highlights
First-author theory: social laws made robust to stochastic rewards
Co-building JaxAHT, the first JAX library for ad hoc teamwork
A coordination-centric, code-first early record
Across the 2 reviewed papers the reproducibility mean is 78/100, and the 50/50 field split between multi-agent systems/AI planning and multi-agent reinforcement learning — plus topics like Multi-Agent Systems and Negotiation (7) and Artificial Intelligence in Games (4) — points to one coherent program: making multi-agent coordination robust and verifiable.
Timeline
- 2018Earliest works appear in the OpenAlex record, with 3 publications in 2018.
- 2021Most productive year in the record, with 4 works — the annual peak.
- 2024A steady recent cadence takes shape: 2 works in 2024, 1 in 2025, and 2 already in 2026 — 5 works over the last three years.
- 2026Both Evolve-cataloged papers appear on arXiv: the first-author social-laws paper and the JaxAHT library paper.
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.
- Bibliometric statistics come from OpenAlex only; citation-impact and aggregate output axes remain unscored due to insufficient data.
- Qualitative judgments rest on just two Evolve-reviewed papers (both 2026 arXiv preprints); the reproducibility mean covers those 2 papers.
- Identity is name-resolved from public bibliographic data (OpenAlex); 'Rolando Fernandez' is a common name — confirm via ORCID 0000-0001-6950-6251.
- No venue, award, or career-stage facts beyond the provided record are included.