Nicholas Ho

Hello! I'm Nicholas, a Computational Biology PhD student at Carnegie Mellon University, advised by Professor Jian Ma and Professor Eric Xing.

My research is supported by the NSF Graduate Research Fellowship (GRFP). I previously interned at GenBio.AI.

I develop biological foundation models and study how their representations uncover new insights and improve counterfactual predictions.

Cell Perturbation — Performed a comprehensive evaluation of perturbation response prediction, training and benchmarking diffusion, flow matching, Schrödinger bridge, and Graph Neural Network models for unseen chemical and genetic perturbations.
Single Cell — Led AIDO.Cell, a 600M-parameter whole-transcriptome model, and designed HEIMDALL to study how tokenizer design affects transfer learning in single-cell foundation models.
DNA — Pretrained AIDO.DNA, a 7B-parameter genome foundation model trained to learn evolutionary representations for cis-regulatory sequence-to-function prediction.
Spatial Transcriptomics — Co-authored AIDO.Tissue, a spatial transcriptomics foundation model that learns tissue-level context through spatial cell-guided pretraining.

Nicholas Ho playing with a yoyo on a rooftop

nzh [AT] cs.cmu.edu

Feel free to reach out—I'm always happy to discuss interesting problems.

In my spare time I enjoy yoyoing.

Latest Posts

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Publications

HEIMDALL: Disentangling Tokenizer Design for Robust Transfer in Single-Cell Foundation Models

Ellie Haber^*, Shahul Alam^*, Nicholas Ho^*, Renming Liu, Evan Trop, Shaoheng Liang, Muyu Yang, Spencer Krieger, and Jian Ma

Cell Systems, 2026

Bib HTML

@article{haber2025heimdall,
  title = {{HEIMDALL: Disentangling Tokenizer Design for Robust Transfer in Single-Cell Foundation Models}},
  author = {Haber, Ellie and Alam, Shahul and Ho, Nicholas and Liu, Renming and Trop, Evan and Liang, Shaoheng and Yang, Muyu and Krieger, Spencer and Ma, Jian},
  year = {2026},
  journal = {Cell Systems},
  url = {https://www.biorxiv.org/content/10.1101/2025.11.09.687403v3},
}

AIDO.Tissue: Spatial Cell-Guided Pretraining for Scalable Spatial Transcriptomics Foundation Model

Jing Gong^*, Yixuan Wang^*, Nicholas Ho, Xingyi Cheng, Le Song, and Eric Xing

In ICML 2025 Generative AI and Biology (GenBio) Workshop, 2025

Bib HTML

@inproceedings{gong2025aidotissue,
  title = {{AIDO.Tissue: Spatial Cell-Guided Pretraining for Scalable Spatial Transcriptomics Foundation Model}},
  author = {Gong, Jing and Wang, Yixuan and Ho, Nicholas and Cheng, Xingyi and Song, Le and Xing, Eric},
  year = {2025},
  booktitle = {ICML 2025 Generative AI and Biology (GenBio) Workshop},
  url = {https://www.biorxiv.org/content/10.1101/2025.07.04.663102v1},
}

Foundation Models Improve Perturbation Response Prediction

Elijah Cole, Geert-Jan Huizing, Sohan Addagudi, Nicholas Ho, Euxhen Hasanaj, Merel Kuijs, Toby Johnstone, Maria Carilli, Alec Davi, Caleb Ellington, Christoph Feinauer, Pan Li, Romain Menegaux, Shahin Mohammadi, Yanjun Shao, Josiah Zhang, Emma Lundberg, Le Song, Ziv Bar-Joseph, and Eric P. Xing

bioRxiv, 2026

Bib HTML

@article{cole2026perturbation,
  title = {{Foundation Models Improve Perturbation Response Prediction}},
  author = {Cole, Elijah and Huizing, Geert-Jan and Addagudi, Sohan and Ho, Nicholas and Hasanaj, Euxhen and Kuijs, Merel and Johnstone, Toby and Carilli, Maria and Davi, Alec and Ellington, Caleb and Feinauer, Christoph and Li, Pan and Menegaux, Romain and Mohammadi, Shahin and Shao, Yanjun and Zhang, Josiah and Lundberg, Emma and Song, Le and Bar-Joseph, Ziv and Xing, Eric P.},
  year = {2026},
  journal = {bioRxiv},
  url = {https://www.biorxiv.org/content/10.64898/2026.02.18.706454v2},
}

Scaling Dense Representations for Single Cell with Transcriptome-Scale Context

Nicholas Ho, Caleb N. Ellington, Jinyu Hou, Sohan Addagudi, Shentong Mo, Tianhua Tao, Dian Li, Yonghao Zhuang, Hongyi Wang, Xingyi Cheng, Le Song, and Eric P. Xing

In NeurIPS Workshop on AI for New Drug Modalities, 2024

Bib HTML

@inproceedings{ho2024aidocell,
  title = {{Scaling Dense Representations for Single Cell with Transcriptome-Scale Context}},
  author = {Ho, Nicholas and Ellington, Caleb N. and Hou, Jinyu and Addagudi, Sohan and Mo, Shentong and Tao, Tianhua and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Cheng, Xingyi and Song, Le and Xing, Eric P.},
  year = {2024},
  booktitle = {NeurIPS Workshop on AI for New Drug Modalities},
  url = {https://www.biorxiv.org/content/10.1101/2024.11.28.625303v1},
}

Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale

Caleb N. Ellington, Ning Sun, Nicholas Ho, Tianhua Tao, Sazan Mahbub, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, and Eric P. Xing

In NeurIPS Workshop on AI for New Drug Modalities, 2024

Bib HTML

@inproceedings{ellington2024aidodna,
  title = {{Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale}},
  author = {Ellington, Caleb N. and Sun, Ning and Ho, Nicholas and Tao, Tianhua and Mahbub, Sazan and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Song, Le and Xing, Eric P.},
  year = {2024},
  booktitle = {NeurIPS Workshop on AI for New Drug Modalities},
  url = {https://www.biorxiv.org/content/10.1101/2024.12.01.625444v1},
}

Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics

Nicholas Ho, John Kevin Cava, John Vant, Ankita Shukla, Jacob Miratsky, Pavan Turaga, Ross Maciejewski, and Abhishek Singharoy

In Machine Learning In Structural Biology (MLSB) Workshop at the 36th Conference on Neural Information Processing Systems, 2022

Bib HTML

@inproceedings{ho2022pathways,
  title = {{Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics}},
  author = {Ho, Nicholas and Cava, John Kevin and Vant, John and Shukla, Ankita and Miratsky, Jacob and Turaga, Pavan and Maciejewski, Ross and Singharoy, Abhishek},
  year = {2022},
  booktitle = {Machine Learning In Structural Biology (MLSB) Workshop at the 36th Conference on Neural Information Processing Systems},
  url = {https://www.biorxiv.org/content/10.1101/2022.10.04.510845v1},
}