Meet Barot
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 nyu.edu.
Late Breaking Abstract Poster: Meta-Neural Cellular Automata
Late Breaking Abstract Document: Meta-Neural Cellular Automata
Demo video showing a neural network being updated to solve the iris classification task (the "task neural network"), using a local update rule which is defined by another neural network (the "local rule network"). In this video, I am setting weights to zero (in red) to see if the local rule updates afterward can recover performance of the network:
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: https://doi.org/10.1101/2023.11.26.568742
Meet Barot, Vladimir Gligorijević, Richard Bonneau, Kyunghyun Cho, Automated Protein Function Description for Novel Class Discovery, bioRxiv 2022.10.13.512154; doi: https://doi.org/10.1101/2022.10.13.512154 (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, https://doi.org/10.1093/bioinformatics/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, https://doi.org/10.1093/bioinformatics/bty440
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). https://doi.org/10.1186/s13059-019-1835-8
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
Function SIG Poster: Automated Protein Function Description for Novel Class Discovery
Function SIG Talk Presentation Slides: seqSCAN: Unsupervised Classification of Proteins for New Function Discovery
Zhuolin's jewelry store! (Etsy alternative)