MIT researcher spanning RF analog computing for edge AI and formal verification of quantum information
2
papers here
2
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
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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 yet scored: publication-volume data for this profile is insufficient. This axis would measure total research output over time — publications per year and authorship position — once a fuller bibliographic record is linked to the ORCID.
Impact—
No score yet: citation and influence data are unavailable for this profile. This axis would measure citation counts, field-weighted influence, and uptake of released benchmarks and datasets by other research groups; none of these figures exist in the current snapshot.
Focus0/100
Focus scores 0, reflecting the raw field distribution's even 50/50 split between RF systems and edge AI computing and formal verification / autoformalization for quantum information. The two reviewed 2026 preprints sit in disjoint areas — analog RF acceleration versus a Lean proof benchmark — so current evidence shows breadth across two fields rather than concentration in one.
Collaboration41/100
Scores 41 for collaboration, derived from 7 distinct coauthors in the current snapshot. The frequent-collaborator circle spans Zhihui Gao, Shi-Yuan Ma, Weichen Winston Yin, Tingjun Chen, Yiran Chen, Jacob M. Taylor, and Frank H. L. Koppens, mixing RF/radio-systems researchers with quantum-science names — consistent with the profile's even two-field split.
Activity—
Not yet scored: recent activity signals lack sufficient underlying data in this snapshot. This axis would measure ongoing research tempo — publications per year and preprint cadence — once a longer publication window is captured.
Reproducibility55/100
Reproducibility scores 55, the mean over 2 papers (reproMean 55, reproPapers 2). Evolve grades both as medium readiness: the RF CNN paper reports strong testbed results but omits latency, while AxQM releases 1,019 kernel-graded Lean tasks without model results yet, limiting end-to-end independent verification.
Research summary
Dirk R. Englund is profiled at the Massachusetts Institute of Technology, with an ORCID-linked record (0000-0002-1043-3489) name-resolved from public bibliographic data. The current Evolve snapshot is compact: two 2026 arXiv preprints, both reviewed by Evolve, split evenly (0.5 / 0.5) across two distinct fields — RF systems and edge AI computing, and formal verification / autoformalization for quantum information. The first paper, 'Radio-Frequency Convolutional Neural Networks', repurposes a radio mixer as an analog CNN accelerator; Evolve's review credits strong software-defined-radio testbed results while cautioning that energy claims exclude infrastructure costs and that latency is unreported. The second, 'AxQM', releases 1,019 kernel-graded Lean 4 proof tasks drawn from Nielsen–Chuang — per Evolve, the largest physics benchmark of its kind — with a sound design but no model results yet. The radar scores collaboration at 41 from 7 distinct coauthors, focus at 0 because of the even two-field split, and reproducibility at 55 as the mean over 2 papers graded medium. Output, impact, and activity axes lack sufficient data and are left unscored. Frequent coauthors include Zhihui Gao, Shi-Yuan Ma, Tingjun Chen, Weichen Winston Yin, Yiran Chen, Jacob M. Taylor, and Frank H. L. Koppens.
Collaboration pattern
The snapshot lists 7 distinct coauthors, with a frequent-collaborator circle of Zhihui Gao, Shi-Yuan Ma, Weichen Winston Yin, Tingjun Chen, Yiran Chen, Jacob M. Taylor, and Frank H. L. Koppens. The mix spans RF/radio-systems and quantum-science researchers, mirroring the profile's even 50/50 split across its two fields; the radar scores collaboration at 41.
Research keywords
Highlights
Textbook-scale Lean benchmark for quantum mechanics proofs
A radio mixer repurposed as an analog CNN accelerator
Timeline
- 2026Publishes 'Radio-Frequency Convolutional Neural Networks' on arXiv, repurposing a radio mixer as an analog CNN accelerator for edge AI inference.
- 2026Releases AxQM, a textbook-scale benchmark of 1,019 kernel-graded Lean proof tasks drawn from Nielsen–Chuang for formal proof synthesis in quantum mechanics.
- 2026Both preprints enter the Evolve review catalog, placing this profile across two fields: RF edge-AI computing and quantum autoformalization.
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 scores are computed from a small OpenAlex-derived snapshot covering only 2 reviewed papers; volume, citation, and activity metrics are absent.
- Output, impact, and activity axes are marked missing in the radar and are not estimated anywhere in this profile.
- Both reviewed papers are 2026 arXiv preprints; peer-review status and venue acceptance are not captured in the data.
- Profile is name-resolved from public bibliographic data linked to ORCID 0000-0002-1043-3489; country and biographical details are not provided.