Researcher Profile
Editor reviewedRWKV and efficient sequence modeling
PhD student at IDSIA USI-SUPSI working on machine learning for drug discovery
A distinctive page because his work bridges open-sequence-model experimentation with applied machine learning for molecules, proteins, and structural biology, and he shows up on multiple RWKV-family papers including the hybrid GoldFinch branch rather than only the first release.
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About This Page
This profile is meant to help you get oriented quickly: why this researcher matters, what to read first, and where to explore next.
Last reviewed
March 18, 2026
Official And External Links
The ideas, systems, and research directions that make this person worth knowing.
01
Machine learning for molecules, proteins, and graph learning
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Open sequence-model work across multiple RWKV-family papers including GoldFinch
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AI applications in structural biology and drug discovery
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RWKV and efficient sequence modeling
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RWKV: Reinventing RNNs for the Transformer Era
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RWKV (project)
Canonical papers, project pages, or repositories that anchor this profile.
Additional papers, projects, or repositories that help flesh out the profile.
Additional links that help verify and flesh out this profile.
People worth exploring next because they share topics, labs, or source material with this profile.
Worth tracking because he is one of the contributors who stays with the RWKV line from the original paper through Eagle/Finch, GoldFinch, and into RWKV-7, which is exactly the kind of repeated authorship signal that makes these long-tail pages valuable.
Worth tracking if you care about alternatives to the standard transformer playbook, especially the line of work trying to keep strong language-model performance while making inference and memory use much cheaper.
A strong open-model and data-centric page because his work sits close to the infrastructure that made OLMo and Dolma useful to the broader research community rather than just another benchmark-driven model release.
Co-authored RWKV: Reinventing RNNs for the Transformer Era.
Useful because it turns an otherwise thin RWKV byline into a real systems profile: after the original paper, his public work tracks toward large-scale pretraining infrastructure, pipeline parallelism, and systems support for frontier-scale models.
Co-authored RWKV: Reinventing RNNs for the Transformer Era.