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ORCIDAndrew H. Song
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2020 – today
- 2026
[i26]Konstantin Hemker, Andrew H. Song, Cristina Almagro-Pérez, Guillaume Jaume, Sophia J. Wagner, Anurag Vaidya, Nikola Simidjievski, Mateja Jamnik, Faisal Mahmood:
Towards Spatial Transcriptomics-driven Pathology Foundation Models. CoRR abs/2602.14177 (2026)- [i25]Daniel Shao, Joel Runevic, Richard J. Chen, Drew F. K. Williamson, Ahrong Kim, Andrew H. Song, Faisal Mahmood:
Mixture of Mini Experts: Overcoming the Linear Layer Bottleneck in Multiple Instance Learning. CoRR abs/2603.22198 (2026) - [i24]Xuan Wang, Zhongling Xu, Gopi Kannedhara, Joakim Nguyen, Jian Yu, Jinrui Fang, Abdurrahmaan Baghdadi, Tianlong Chen, Awais Naeem, Chandra Krishnan, Edward Castillo, Andrew H. Song, Ankita Shukla, Ying Ding, Nicholas Konz, Hairong Wang:
ConceptM3oE: Concept-Guided Multimodal Mixture of Experts for Interpretable Computational Pathology. CoRR abs/2605.24399 (2026) - 2025
- [c15]Daniel Shao, Richard J. Chen, Andrew H. Song, Joel Runevic, Ming Y. Lu, Tong Ding, Faisal Mahmood:
Do Multiple Instance Learning Models Transfer? ICML 2025 - [i23]Anurag Vaidya, Andrew Zhang, Guillaume Jaume, Andrew H. Song, Tong Ding, Sophia J. Wagner, Ming Y. Lu, Paul Doucet, Harry Robertson, Cristina Almagro-Pérez, Richard J. Chen, Dina ElHarouni, Georges Ayoub, Connor Bossi, Keith L. Ligon, Georg Gerber, Long Phi Le, Faisal Mahmood
:
Molecular-driven Foundation Model for Oncologic Pathology. CoRR abs/2501.16652 (2025) - [i22]Cristina Almagro-Pérez, Andrew H. Song, Luca L. Weishaupt, Ahrong Kim, Guillaume Jaume, Drew F. K. Williamson, Konstantin Hemker, Ming Y. Lu, Kritika Singh, Bowen Chen, Long Phi Le, Alexander S. Baras, Sizun Jiang, Ali Bashashati, Jonathan T. C. Liu, Faisal Mahmood:
AI-driven 3D Spatial Transcriptomics. CoRR abs/2502.17761 (2025) - [i21]Muhammad Shaban, Yuzhou Chang, Huaying Qiu, Yao Yu Yeo, Andrew H. Song, Guillaume Jaume, Yuchen Wang, Luca L. Weishaupt, Tong Ding, Anurag Vaidya, Abdallah Lamane, Daniel Shao, Mohammed Zidane, Yunhao Bai, Paige McCallum, Shuli Luo, Wenrui Wu, Yang Wang, Precious Cramer, Chi Ngai Chan, Pierre Stéphan, Johanna Schaffenrath, Jia Le Lee, Hendrik A. Michel, Caiwei Tian, Cristina Almagro-Pérez, Sophia J. Wagner, Sharifa Sahai, Ming Y. Lu, Richard J. Chen, Andrew Zhang, Mark Edward M. Gonzales, Ahmad Makky, Jia-Ying Joey Lee, Hao Cheng, Nourhan El Ahmar, Sayed Matar, Maximilian Haist, Darci Phillips, Yuqi Tan, Garry P. Nolan, W. Richard Burack, Jacob D. Estes, Jonathan T. C. Liu, Toni K. Choueiri, Neeraj Agarwal, Marc Barry, Scott J. Rodig, Long Phi Le, Georg Gerber, Christian M. Schürch, Fabian J. Theis, Youn H. Kim, Joe Yeong, Sabina Signoretti, Brooke E. Howitt, Lit-Hsin Loo, Qin Ma, Sizun Jiang, Faisal Mahmood:
A Foundation Model for Spatial Proteomics. CoRR abs/2506.03373 (2025) - [i20]Daniel Shao, Richard J. Chen, Andrew H. Song, Joel Runevic, Ming Y. Lu, Tong Ding, Faisal Mahmood:
Do Multiple Instance Learning Models Transfer? CoRR abs/2506.09022 (2025) - 2024
- [j5]ChangGyun Jin, Andrew H. Song, Seong-Eun Kim:
Two-Phase Multitask Autoencoder-Based Deep Learning Framework for Subject-Independent EEG Motor Imagery Classification. IEEE Access 12: 77356-77367 (2024) - [c14]Gan Gao, Andrew H. Song, Fiona Wang, David Brenes, Rui Wang, Sarah S. L. Chow, Kevin W. Bishop, Lawrence D. True, Faisal Mahmood, Jonathan T. C. Liu:
Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments. CVPR Workshops 2024: 6955-6965 - [c13]Guillaume Jaume, Lukas Oldenburg, Anurag Vaidya, Richard J. Chen, Drew F. K. Williamson, Thomas Peeters, Andrew H. Song, Faisal Mahmood:
Transcriptomics-Guided Slide Representation Learning in Computational Pathology. CVPR 2024: 9632-9644 - [c12]Andrew H. Song, Richard J. Chen, Tong Ding, Drew F. K. Williamson, Guillaume Jaume, Faisal Mahmood:
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology. CVPR 2024: 11566-11578 - [c11]Guillaume Jaume, Anurag Vaidya, Andrew Zhang, Andrew H. Song, Richard J. Chen, Sharifa Sahai, Dandan Mo, Emilio Madrigal, Long Phi Le, Faisal Mahmood:
Multistain Pretraining for Slide Representation Learning in Pathology. ECCV (33) 2024: 19-37 - [c10]Andrew H. Song, Richard J. Chen, Guillaume Jaume, Anurag J. Vaidya, Alexander S. Baras, Faisal Mahmood:
Multimodal Prototyping for cancer survival prediction. ICML 2024: 46050-46073 - [c9]Guillaume Jaume, Paul Doucet, Andrew H. Song, Ming Yang Lu, Cristina Almagro-Pérez, Sophia J. Wagner, Anurag Vaidya, Richard J. Chen, Drew F. K. Williamson, Ahrong Kim, Faisal Mahmood:
HEST-1k: A Dataset For Spatial Transcriptomics and Histology Image Analysis. NeurIPS 2024 - [i19]Andrew H. Song, Guillaume Jaume, Drew F. K. Williamson, Ming Y. Lu, Anurag Vaidya, Tiffany R. Miller, Faisal Mahmood:
Artificial Intelligence for Digital and Computational Pathology. CoRR abs/2401.06148 (2024) - [i18]Guillaume Jaume, Lukas Oldenburg, Anurag Vaidya, Richard J. Chen, Drew F. K. Williamson, Thomas Peeters, Andrew H. Song, Faisal Mahmood:
Transcriptomics-guided Slide Representation Learning in Computational Pathology. CoRR abs/2405.11618 (2024) - [i17]Andrew H. Song, Richard J. Chen, Tong Ding, Drew F. K. Williamson, Guillaume Jaume, Faisal Mahmood:
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology. CoRR abs/2405.11643 (2024) - [i16]Gan Gao, Andrew H. Song, Fiona Wang, David Brenes, Rui Wang, Sarah S. L. Chow, Kevin W. Bishop, Lawrence D. True, Faisal Mahmood, Jonathan T. C. Liu:
Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments. CoRR abs/2406.07061 (2024) - [i15]Guillaume Jaume, Paul Doucet, Andrew H. Song, Ming Y. Lu, Cristina Almagro-Pérez, Sophia J. Wagner, Anurag J. Vaidya, Richard J. Chen, Drew F. K. Williamson, Ahrong Kim, Faisal Mahmood:
HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis. CoRR abs/2406.16192 (2024) - [i14]Andrew H. Song, Richard J. Chen, Guillaume Jaume, Anurag J. Vaidya, Alexander S. Baras, Faisal Mahmood:
Multimodal Prototyping for cancer survival prediction. CoRR abs/2407.00224 (2024) - [i13]Guillaume Jaume, Anurag Vaidya, Andrew Zhang, Andrew H. Song, Richard J. Chen, Sharifa Sahai, Dandan Mo, Emilio Madrigal, Long Phi Le, Faisal Mahmood:
Multistain Pretraining for Slide Representation Learning in Pathology. CoRR abs/2408.02859 (2024) - [i12]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-Pérez, Paul Doucet, Sharifa Sahai, Chengkuan Chen, Daisuke Komura, Akihiro Kawabe, Shumpei Ishikawa, Georg Gerber, Tingying Peng, Long Phi Le, Faisal Mahmood:
Multimodal Whole Slide Foundation Model for Pathology. CoRR abs/2411.19666 (2024) - 2023
- [i11]Andrew H. Song, Mane Williams, Drew F. K. Williamson, Guillaume Jaume, Andrew Zhang, Bowen Chen, Robert Serafin, Jonathan T. C. Liu, Alexander S. Baras, Anil V. Parwani, Faisal Mahmood:
Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples. CoRR abs/2307.14907 (2023) - [i10]Richard J. Chen, Tong Ding, Ming Y. Lu, Drew F. K. Williamson, Guillaume Jaume, Bowen Chen, Andrew Zhang, Daniel Shao, Andrew H. Song, Muhammad Shaban, Mane Williams, Anurag Vaidya, Sharifa Sahai, Lukas Oldenburg, Luca L. Weishaupt, Judy J. Wang, Walt Williams, Long Phi Le, Georg Gerber, Faisal Mahmood:
A General-Purpose Self-Supervised Model for Computational Pathology. CoRR abs/2308.15474 (2023) - 2022
- [b1]Andrew H. Song:
Generative models for neural time series with structured domain priors. Massachusetts Institute of Technology, USA, 2022 - [j4]Andrew H. Song, Bahareh Tolooshams, Demba E. Ba:
Gaussian Process Convolutional Dictionary Learning. IEEE Signal Process. Lett. 29: 95-99 (2022) - [j3]Andrew H. Song, Seong-Eun Kim, Emery N. Brown:
Adaptive State-Space Multitaper Spectral Estimation. IEEE Signal Process. Lett. 29: 523-527 (2022) - [j2]Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba E. Ba:
Covariance-Free Sparse Bayesian Learning. IEEE Trans. Signal Process. 70: 3818-3831 (2022) - [c8]Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba E. Ba:
High-Dimensional Sparse Bayesian Learning without Covariance Matrices. ICASSP 2022: 1511-1515 - [c7]Alexander Lin, Andrew H. Song, Demba E. Ba:
Mixture Model Auto-Encoders: Deep Clustering Through Dictionary Learning. ICASSP 2022: 3368-3372 - [c6]Iain Carmichael, Andrew H. Song, Richard J. Chen, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood:
Incorporating Intratumoral Heterogeneity into Weakly-Supervised Deep Learning Models via Variance Pooling. MICCAI (2) 2022: 387-397 - [i9]Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba E. Ba:
High-Dimensional Sparse Bayesian Learning without Covariance Matrices. CoRR abs/2202.12808 (2022) - [i8]Iain Carmichael, Andrew H. Song, Richard J. Chen, Drew F. K. Williamson, Tiffany Y. Chen, Faisal Mahmood:
Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling. CoRR abs/2206.08885 (2022) - 2021
- [c5]Andrew H. Song, Demba E. Ba, Emery N. Brown:
PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components. UAI 2021: 1371-1381 - [i7]Andrew H. Song, Bahareh Tolooshams, Demba E. Ba:
Gaussian Process Convolutional Dictionary Learning. CoRR abs/2104.00530 (2021) - [i6]Alexander Lin, Andrew H. Song, Berkin Bilgic, Demba E. Ba:
Covariance-Free Sparse Bayesian Learning. CoRR abs/2105.10439 (2021) - [i5]Alexander Lin, Andrew H. Song, Demba E. Ba:
Mixture Model Auto-Encoders: Deep Clustering through Dictionary Learning. CoRR abs/2110.04683 (2021) - 2020
- [j1]Andrew H. Song, Francisco J. Flores, Demba E. Ba:
Convolutional Dictionary Learning With Grid Refinement. IEEE Trans. Signal Process. 68: 2558-2573 (2020) - [c4]Bahareh Tolooshams, Ritwik Giri, Andrew H. Song, Umut Isik, Arvindh Krishnaswamy:
Channel-Attention Dense U-Net for Multichannel Speech Enhancement. ICASSP 2020: 836-840 - [c3]Bahareh Tolooshams, Andrew H. Song, Simona Temereanca, Demba E. Ba:
Convolutional dictionary learning based auto-encoders for natural exponential-family distributions. ICML 2020: 9493-9503 - [i4]Bahareh Tolooshams, Ritwik Giri, Andrew H. Song, Umut Isik, Arvindh Krishnaswamy:
Channel-Attention Dense U-Net for Multichannel Speech Enhancement. CoRR abs/2001.11542 (2020)
2010 – 2019
- 2019
- [c2]Andrew H. Song, Leon Chlon, Hugo Soulat, John Tauber, Sandya Subramanian, Demba E. Ba, Michael J. Prerau:
Multitaper Infinite Hidden Markov Model for EEG. EMBC 2019: 5803-5807 - [i3]Bahareh Tolooshams, Andrew H. Song, Simona Temereanca, Demba E. Ba:
Deep Exponential-Family Auto-Encoders. CoRR abs/1907.03211 (2019) - [i2]Andrew H. Song, Francisco J. Flores, Demba E. Ba:
Fast Convolutional Dictionary Learning off the Grid. CoRR abs/1907.09063 (2019) - 2018
- [c1]Andrew H. Song, Sourish Chakravarty, Emery N. Brown:
A Smoother State Space Multitaper Spectrogram. EMBC 2018: 33-36 - [i1]Leon Chlon, Andrew H. Song, Sandya Subramanian, Hugo Soulat, John Tauber, Demba E. Ba, Michael J. Prerau:
Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference. CoRR abs/1805.07300 (2018)
Coauthor Index
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