About

I’m a PhD student in Computer Science at New York University, advised by Oded Regev. I develop interpretable machine learning methods for computational biology, with a focus on genomic sequence-to-function models, sequence design, and RNA regulatory mechanisms.

Before NYU, I received my B.S. (2024) and M.S. (2025) in Computer Science from UCLA, where I worked with Elior Rahmani on robust representation learning from electronic health records.

News

Oct 2026Giving a talk at NECB 2026 on interpretable sequence-to-function models.Aug 2026Poster accepted at MLCB 2026 (presenting in November).Feb 2026Our biobank-scale gene–environment interaction paper was accepted at RECOMB 2026.Aug 2025Started my PhD in Computer Science at NYU, advised by Oded Regev.

Selected Publications

Deciphering regulatory logic with interpretable sequence-to-function models

Arush Ramteke*, Simon Liu*, Oded Regev

MLCB 2026 (poster)

CoRGI: GNNs with Convolutional Residual Global Interaction for Lagrangian Simulation

Ethan Ji, Yuanzhou Chen, Arush Ramteke, Fang Sun, Tianrun Yu, Jai Parera, Wei Wang, Yizhou Sun

KDD 2026BibTeX

@inproceedings{ji2026corgi,
  title     = {CoRGI: GNNs with Convolutional Residual Global Interaction for Lagrangian Simulation},
  author    = {Ji, Ethan and Chen, Yuanzhou and Ramteke, Arush and Sun, Fang and Yu, Tianrun and Parera, Jai and Wang, Wei and Sun, Yizhou},
  booktitle = {Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year      = {2026}
}

A biobank-scale method for learning environmental modulators of gene-environment interaction underlying complex traits

Zhengtong Liu, Arush Ramteke, Aakarsh Anand, Aditya Gorla, Moonseong Jeong, Sriram Sankararaman

RECOMB 2026BibTeX

@inproceedings{liu2026biobank,
  title     = {A biobank-scale method for learning environmental modulators of gene-environment interaction underlying complex traits},
  author    = {Liu, Zhengtong and Ramteke, Arush and Anand, Aakarsh and Gorla, Aditya and Jeong, Moonseong and Sankararaman, Sriram},
  booktitle = {Research in Computational Molecular Biology (RECOMB)},
  year      = {2026}
}

Disease subtyping using electronic health records via contrastive learning with latent domain generalization

Arush Ramteke, Elior Rahmani

ML4H 2024BibTeX

@inproceedings{ramteke2024disease,
  title     = {Disease subtyping using electronic health records via contrastive learning with latent domain generalization},
  author    = {Ramteke, Arush and Rahmani, Elior},
  booktitle = {Machine Learning for Health (ML4H)},
  year      = {2024}
}

All publications →