Arush Ramteke

Arush Ramteke

PhD Student, Computer Science
New York University

CV

Education

  • New York University: Ph.D. in Computer Science, advised by Oded Regev 2025–present
  • University of California, Los Angeles: M.S. in Computer Science. Thesis: Subtyping Glaucoma with Robust Contrastive Dimension Reduction 2025
  • University of California, Los Angeles: B.S. in Computer Science, Minor in Bioinformatics, Magna Cum Laude 2024

Research Experience

  • Regev Lab, NYU: PhD Researcher Aug 2025–present
    Interpretable sequence-to-function models of combinatorial regulatory logic in post-transcriptional RNA processing (splicing, mRNA degradation, subnuclear localization).
  • Rahmani Lab, UCLA: Graduate Researcher May 2023–Jul 2025
    Robust representation learning and latent domain discovery for patient stratification from longitudinal EHRs; glaucoma subtyping and treatment selection.
  • Bouchard Lab, UCLA: Student Researcher May 2022–Jul 2025
    Interpretable, uncertainty-aware landslide susceptibility mapping with spatial Neural Additive Models; GPU-accelerated Jacobian and NTK computation for Levenberg–Marquardt training.

Honors

  • Shivkumar Endowed Scholarship in Computer Science, UCLA 2022
  • Tau Beta Pi Engineering Honor Society
  • Upsilon Pi Epsilon Computing Honor Society

Publications

Conference Papers

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 2026
@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 2026
@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 2024
@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}
}

Posters & Abstracts

Deciphering regulatory logic with interpretable sequence-to-function models
Arush Ramteke*, Simon Liu*, Oded Regev
MLCB 2026 (poster)

In Preparation

Deciphering regulatory logic with interpretable sequence-to-function models
Arush Ramteke*, Simon Liu*, Oded Regev
In preparation · journal version
Precision first-line therapy selection for lowering intraocular pressure: generalizable patient stratification from electronic health records
Arush Ramteke, Elior Rahmani, Shahin Hallaj, Sally L. Baxter
In preparation

Talks

  • Deciphering regulatory logic with interpretable sequence-to-function models
    New England Computational Biology Symposium (NECB) · Oct 2026
  • Interpretable Neural Additive Models for Landslide Susceptibility Across Multiple Triggers
    Southern California Geomorphology Symposium · 2025

Teaching & Service

  • Reader, Introduction to Data Science, UCLA Winter 2024
  • Peer Tutor, Upsilon Pi Epsilon & Tau Beta Pi, UCLA 2021–2024
  • Reviewer, RECOMB 2024, 2025; MLCB 2026