Meet Barot

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Hello, my name is Meet Barot. I'm interested in machine learning, proteins, networks, collective intelligence and decentralization.
I did my PhD at New York University in the Center for Data Science; I graduated in 2023.

I am currently doing consulting work for companies that need AI expertise, focusing on language models and network analysis. Research-wise I'm working on meta-learning and open-ended learning methods for neural networks. Specifically, I'm modeling neural network learning as neural cellular automata processes.

If you'd like to chat, email me at my name (no spaces or periods or anything) at

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Tymor Hamamsy, Meet Barot, James T Morton, Martin Steinegger, Richard Bonneau, and Kyunghyun Cho, Learning sequence, structure, and function representations of proteins with language models, bioRxiv 2023.11.26.568742; doi:

Meet Barot, Vladimir Gligorijević, Richard Bonneau, Kyunghyun Cho, Automated Protein Function Description for Novel Class Discovery, bioRxiv 2022.10.13.512154; doi: (A version of this was in the NeurIPS 2022 AI4Science workshop here.)

Meet Barot, Vladimir Gligorijević, Kyunghyun Cho, Richard Bonneau, NetQuilt: deep multispecies network-based protein function prediction using homology-informed network similarity, Bioinformatics, 2021;, btab098,
NetQuilt Simons Foundation News Article
NetQuilt CDS Blog Post

Vladimir Gligorijević, Meet Barot, Richard Bonneau, deepNF: deep network fusion for protein function prediction, Bioinformatics, Volume 34, Issue 22, 15 November 2018, Pages 3873-3881,

As a function prediction method submitter to the 3rd Critical Assessment of Function Annotation (CAFA) challenge:
Zhou, N., Jiang, Y., Bergquist, T.R. et al. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome Biol 20, 244 (2019).

Recorded Talks

Meet Barot, Daniel Berenberg, James Morton, Vladimir Gligorijević, Kyunghyun Cho, Richard Bonneau, (2020). Learning sequence, structure and network features for protein function prediction. 20-minute talk delivered virtually at the ISMB Conference. Recording

Meet Barot, Vladimir Gligorijević, Kyunghyun Cho, Richard Bonneau, (2019). Graph-Regularized Autoencoders for Protein Feature Learning. 10-minute talk delivered at the ISMB/ECCB Joint Conference, Basel, Switzerland. Recording

ISMB 2022 Material

Function SIG Poster: Automated Protein Function Description for Novel Class Discovery

ISMB/ECCB 2021 Material

Function SIG Talk Presentation Slides: seqSCAN: Unsupervised Classification of Proteins for New Function Discovery

Other links

Zhuolin's jewelry store! (Etsy alternative)

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