Publications

* Co-first  · + Co-second  · † Co-senior  ·  A full list is also available on Google Scholar.

2026

  1. Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning

    Daniel Shao, Joel Runevic, Richard J Chen, Drew FK Williamson, Ahrong Kim, Andrew H Song†, Faisal Mahmood†

    ICLR, 2026

  2. Deep-learning triage of three-dimensional pathology datasets for comprehensive and efficient pathologist assessments

    Gan Gao, Renao Yan+, Andrew H Song+, Huai-Ching Hsieh, Lindsey A Erion Barner, Fiona Wang, David Brenes, Sarah SL Chow, Rui Wang, Kevin W Bishop, et al.

    Nature Biomedical Engineering, 2026

  3. STP-BENCH: A Unified Systematic Benchmark for Virtual Spatial Transcriptomics from Histopathology Images

    Youngmin Chung*, Ji Hun Ha*, Andrew H. Song*, Cristina Almagro-Pérez, Chaeyoung Seo, Won Jun Suh, Jeong Won Beom, Kyoung Bin Oh, Eytan Ruppin†, Faisal Mahmood†, Joo Sang Lee†

    arXiv, 2026

  4. Towards Spatial Transcriptomics-driven Pathology Foundation Models

    Konstantin Hemker*, Andrew H Song*, Cristina Almagro-Pérez, Guillaume Jaume, Sophia J Wagner, Anurag Vaidya, Nikola Simidjievski, Mateja Jamnik, Faisal Mahmood

    arXiv, 2026

2025

  1. Multimodal Whole Slide Foundation Model for Pathology

    Tong Ding*, Sophia J. Wagner*, Andrew H Song*, Richard J. Chen*, Ming Y. Lu, Andrew Zhang, Anurag J. Vaidya, Guillaume Jaume, Muhammad Shaban, Ahrong Kim, Drew F. K. Williamson, Bowen Chen, Cristina Almagro-Perez, Paul Doucet, Sharifa Sahai, Chengkuan Chen, Daisuke Komura, Akihiro Kawabe, Shumpei Ishikawa, Georg Gerber, Tingying Peng, Long Phi Le, Faisal Mahmood

    Nature Medicine, 2025

  2. AI-driven 3D spatial transcriptomics

    Cristina Almagro-Pérez*, Andrew H Song*, Luca Weishaupt, Ahrong Kim, Guillaume Jaume, Drew FK Williamson, Konstantin Hemker, Ming Y Lu, Kritika Singh, Bowen Chen, et al.

    arXiv, 2025

  3. Molecular-Driven Foundation Model for Oncologic Pathology

    Anurag Vaidya*, Andrew Zhang*, Guillaume Jaume*, Andrew H Song+, Tong Ding+, Sophia J. Wagner+, Ming Y. Lu, Paul Doucet, Harry Robertson, Cristina Almagro-Perez, Richard J. Chen, Dina ElHarouni, Georges Ayoub, Connor Bossi, Keith L. Ligon, Georg Gerber, Long Phi Le, Faisal Mahmood

    Nature Cancer (In press), 2025

  4. A Foundation Model for Spatial Proteomics

    Muhammad Shaban*, Yuzhou Chang*, Huaying Qiu+, Yao Yu Yeo+, Andrew H Song+, Guillaume Jaume+, Yuchen Wang, Luca L Weishaupt, Tong Ding, Anurag Vaidya, et al.

    arXiv, 2025

  5. Do Multiple Instance Learning Models Transfer?

    Daniel Shao, Richard J Chen, Andrew H Song, Joel Runevic, Ming Y. Lu, Tong Ding, Faisal Mahmood

    International Conference on Machine Learning (ICML), 2025

  6. Generative Artificial Intelligence in Anatomic Pathology

    Victor Brodsky*, Ehsan Ullah*, Andrey Bychkov, Andrew H Song, Eric E Walk, Peter Louis, Ghulam Rasool, Rajendra S Singh, Faisal Mahmood, Marilyn M Bui, et al.

    Archives of Pathology & Laboratory Medicine, 2025

2024

  1. Analysis of 3D pathology samples using weakly supervised AI

    Andrew H Song, Mane Williams+, Drew FK Williamson+, Sarah SL Chow, Guillaume Jaume, Gan Gao, Andrew Zhang, Bowen Chen, Alexander S Baras, Robert Serafin, Richard Colling, Michelle R Downes, Xavier Farre, Peter Humphrey, Clare Verrill, Lawrence D True, Anil V Parwani, Jonathan TC Liu†, Faisal Mahmood†

    Cell, 2024

  2. Hest-1k: A dataset for spatial transcriptomics and histology image analysis

    Guillaume Jaume*, Paul Doucet*, Andrew H Song, Ming Y Lu, Cristina Almagro-Pérez, Sophia J Wagner, Anurag J Vaidya, Richard J Chen, Drew FK Williamson, Ahrong Kim, et al.

    Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2024

  3. Multimodal Prototyping for cancer survival prediction

    Andrew H Song, Richard J Chen, Guillaume Jaume, Anurag Jayant Vaidya, Alexander Baras, Faisal Mahmood

    International Conference on Machine Learning (ICML), 2024

  4. Multistain Pretraining for Slide Representation Learning in Pathology

    Guillaume Jaume*, Anurag Vaidya*, Andrew Zhang+, Andrew H Song+, Richard J Chen, Sharifa Sahai, Dandan Mo, Emilio Madrigal, Long Phi Le, Faisal Mahmood

    European Conference on Computer Vision (ECCV), 2024

  5. Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology

    Andrew H Song*, Richard J Chen*, Tong Ding, Drew FK Williamson, Guillaume Jaume, Faisal Mahmood

    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

  6. Transcriptomics-guided Slide Representation Learning in Computational Pathology

    Guillaume Jaume*, Lukas Oldenburg*, Anurag Jayant Vaidya, Richard J. Chen, Drew FK Williamson, Thomas Peeters, Andrew H Song, Faisal Mahmood

    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

  7. Demographic bias in misdiagnosis by computational pathology models

    Anurag Vaidya*, Richard J Chen*, Drew FK Williamson*, Andrew H Song, Guillaume Jaume, Yuzhe Yang, Thomas Hartvigsen, Emma C Dyer, Ming Y Lu, Jana Lipkova, Muhammad Shaban, Tiffany Y Chen, Faisal Mahmood

    Nature Medicine, 2024

  8. Towards a general-purpose foundation model for computational pathology

    Richard J Chen*, Tong Ding*, Ming Y Lu*, Drew FK Williamson*, Guillaume Jaume, Andrew H Song, Bowen Chen, Andrew Zhang, Daniel Shao, Muhammad Shaban, Mane Williams, Lukas Oldenburg, et al.

    Nature Medicine, 2024

  9. Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments

    Gan Gao*, Andrew H Song*, Fiona Wang, David Brenes, Rui Wang, Sarah SL Chow, Kevin W Bishop, Lawrence D True, Faisal Mahmood, Jonathan TC Liu

    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshop on Computer Vision for Microscopy Image Analysis (CVMI), 2024

  10. Two-Phase Multitask Autoencoder-Based Deep Learning Framework for Subject-Independent EEG Motor Imagery Classification

    Changgyun Jin, Andrew H Song, Seong-Eun Kim

    IEEE Access, 2024

2023

  1. Artificial intelligence for digital and computational pathology

    Andrew H Song*, Guillaume Jaume*, Drew FK Williamson, Ming Y Lu, Anurag Vaidya, Tiffany R Miller, Faisal Mahmood

    Nature Reviews Bioengineering, 2023

2022

  1. Integrating context for superior cancer prognosis

    Guillaume Jaume*, Andrew H Song*, Faisal Mahmood

    Nature Biomedical Engineering, 2022

  2. Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling

    Iain Carmichael*, Andrew H Song*, Richard J Chen, Drew FK Williamson, Tiffany Y Chen, Faisal Mahmood

    International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2022

  3. Investigating morphologic correlates of driver gene mutation heterogeneity via deep learning

    Andrew H Song, Drew FK Williamson, Faisal Mahmood

    Cancer Research, 2022

  4. Covariance-free sparse Bayesian learning

    Alexander Lin, Andrew H Song, Berkin Bilgic, Demba Ba

    IEEE Transactions on Signal Processing, 2022

  5. High-dimensional sparse Bayesian learning without covariance matrices

    Alexander Lin, Andrew H Song, Berkin Bilgic, Demba Ba

    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022

  6. Mixture model auto-encoders: Deep clustering through dictionary learning

    Alexander Lin, Andrew H Song, Demba Ba

    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022

  7. Adaptive state-space multitaper spectral estimation

    Andrew H Song*, Seong-Eun Kim*, Emery N Brown

    IEEE Signal Processing Letters, 2022

Before 2021

  1. Gaussian process convolutional dictionary learning

    Andrew H Song, Bahareh Tolooshams, Demba Ba

    IEEE Signal Processing Letters, 2021

  2. PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components

    Andrew H Song, Demba Ba, Emery N Brown

    Uncertainty in Artificial Intelligence (UAI), 2021

  3. Convolutional dictionary learning based auto-encoders for natural exponential-family distributions

    Bahareh Tolooshams*, Andrew H Song*, Simona Temereanca, Demba Ba

    International Conference on Machine Learning (ICML), 2020

  4. Channel-attention dense u-net for multichannel speech enhancement

    Bahareh Tolooshams, Ritwik Giri, Andrew H Song, Umut Isik, Arvindh Krishnaswamy

    IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

  5. Convolutional dictionary learning with grid refinement

    Andrew H Song, Francisco J Flores, Demba Ba

    IEEE Transactions on Signal Processing, 2020

  6. Multitaper infinite hidden markov model for eeg

    Andrew H Song, Leon Chlon, Hugo Soulat, John Tauber, Sandya Subramanian, Demba Ba, Michael J Prerau

    IEEE Engineering in Medicine and Biology Society (EMBC), 2019

  7. A smoother state space multitaper spectrogram

    Andrew H Song, Sourish Chakravarty, Emery N Brown

    IEEE Engineering in Medicine and Biology Society (EMBC), 2018

  8. Pharmacological modulation of noradrenergic arousal circuitry disrupts functional connectivity of the locus ceruleus in humans

    Andrew H Song, Aaron Kucyi, Vitaly Napadow, Emery N Brown, Marco L Loggia, Oluwaseun Akeju

    Journal of Neuroscience, 2017

  9. GABAA circuit mechanisms are associated with ether anesthesia-induced unconsciousness

    Oluwaseun Akeju, Allison E Hamilos, Andrew H Song, Kara J Pavone, Patrick L Purdon, Emery N Brown

    Clinical Neurophysiology, 2016

  10. Electroencephalogram signatures of ketamine anesthesia-induced unconsciousness

    Oluwaseun Akeju, Andrew H Song, Allison E Hamilos, Kara J Pavone, Francisco J Flores, Emery N Brown, Patrick L Purdon

    Clinical Neurophysiology, 2016

  11. Bring your own learner: A cloud-based, data-parallel commons for machine learning

    Ignacio Arnaldo, Kalyan Veeramachaneni, Andrew H Song, Una-May O'Reilly

    IEEE Computational Intelligence Magazine, 2015