Author

Igor Krawczuk

École Polytechnique Fédérale de LausanneORCID 0000-0002-5281-8926Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioengineering· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:imaging· 1 Research Focus:imputation· 1 Approach:machine learning (classical)· 1 Assay:protein structure· 1

Frequent Co-Authors

Adaptyv Competition Organizers· 1 shared Ahmad Zaman Qamar· 1 shared Aleksandra RađenovićiD· 1 shared Alexander Naka· 1 shared Anthony GitteriD· 1 shared B. Drake· 1 shared Bruno E. CorreiaiD· 1 shared Chance A. Challacombe· 1 shared

Activity by Year

2025-2025 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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1 shared paperBioengineering

Adaptyv Competition Organizers

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2 papers

Recent·Most cited

Deep-learning-assisted SICM for enhanced real-time imaging of nanoscale biological dynamics

Zahra Ayar, Marcos Penedo, B. Drake, Jialin Shi, Samuel M. Leitão, Igor Krawczuk, Helena Miljkovic, Aleksandra Rađenović, Jelena Ban, Volkan Cevher, Georg Ernest Fantner

This paper introduces a deep-learning-assisted framework to enhance real-time SICM imaging by reducing scan time while maintaining image quality and structural integrity.

Small Methods · Oct 13, 2025Peer-reviewedBioengineering

Crowdsourced Protein Design: Lessons From the Adaptyv EGFR Binder Competition

Tudor‐Stefan Cotet, Igor Krawczuk, Filippo Stocco, Noelia Ferruz, Anthony Gitter, Yoichi Kurumida, Lucas de Almeida Machado, Francesco Paesani, Cianna N. Calia, Chance A. Challacombe, Nikhil Haas, Ahmad Zaman Qamar, Bruno E. Correia, Martin Pačesa, Lennart Nickel, , Adaptyv Competition Organizers

The paper analyzes a crowdsourced protein design competition, showing participants designed EGFR binders with varying affinities and highlighting challenges in benchmarking and experimental design.

bioRxiv · Apr 17, 2025 · PreprintBioengineering