A Smoother State Space Multitaper Spectrogram. - Andrew H. Song; Sourish Chakravarty; Emery N. Brown (EMBC, 2018)

Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference. - Leon Chlon; Andrew H. Song; Sandya Subramanian; Hugo Soulat; John Tauber; Demba E. Ba; Michael J. Prerau (CoRR, 2018)

Multitaper Infinite Hidden Markov Model for EEG. - Andrew H. Song; Leon Chlon; Hugo Soulat; John Tauber; Sandya Subramanian; Demba E. Ba; Michael J. Prerau (EMBC, 2019)

Deep Exponential-Family Auto-Encoders. - Bahareh Tolooshams; Andrew H. Song; Simona Temereanca; Demba E. Ba (CoRR, 2019)

Fast Convolutional Dictionary Learning off the Grid. - Andrew H. Song; Francisco J. Flores; Demba E. Ba (CoRR, 2019)

Channel-Attention Dense U-Net for Multichannel Speech Enhancement. - Bahareh Tolooshams; Ritwik Giri; Andrew H. Song; Umut Isik; Arvindh Krishnaswamy (ICASSP, 2020)

Convolutional dictionary learning based auto-encoders for natural exponential-family distributions. - Bahareh Tolooshams; Andrew H. Song; Simona Temereanca; Demba E. Ba (ICML, 2020)

Convolutional Dictionary Learning With Grid Refinement. - Andrew H. Song; Francisco J. Flores; Demba E. Ba (IEEE Trans. Signal Process., 2020)

PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components. - Andrew H. Song; Demba E. Ba; Emery N. Brown (UAI, 2021)

Mixture Model Auto-Encoders: Deep Clustering Through Dictionary Learning. - Alexander Lin; Andrew H. Song; Demba E. Ba (ICASSP, 2022)

High-Dimensional Sparse Bayesian Learning without Covariance Matrices. - Alexander Lin; Andrew H. Song; Berkin Bilgic; Demba E. Ba (ICASSP, 2022)

Incorporating Intratumoral Heterogeneity into Weakly-Supervised Deep Learning Models via Variance Pooling. - Iain Carmichael; Andrew H. Song; Richard J. Chen; Drew F. K. Williamson; Tiffany Y. Chen; Faisal Mahmood 001 (MICCAI (2), 2022)

Adaptive State-Space Multitaper Spectral Estimation. - Andrew H. Song; Seong-Eun Kim; Emery N. Brown (IEEE Signal Process. Lett., 2022)

Gaussian Process Convolutional Dictionary Learning. - Andrew H. Song; Bahareh Tolooshams; Demba E. Ba (IEEE Signal Process. Lett., 2022)

Covariance-Free Sparse Bayesian Learning. - Alexander Lin; Andrew H. Song; Berkin Bilgic; Demba E. Ba (IEEE Trans. Signal Process., 2022)

Generative models for neural time series with structured domain priors. - Andrew H. Song (2022)

Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples. - 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 001 (CoRR, 2023)

A General-Purpose Self-Supervised Model for Computational Pathology. - 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; et al. (CoRR, 2023)

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 S. L. Chow; Kevin W. Bishop; Lawrence D. True; Faisal Mahmood 001; Jonathan T. C. Liu (CVPR Workshops, 2024)

Transcriptomics-Guided Slide Representation Learning in Computational Pathology. - Guillaume Jaume; Lukas Oldenburg; Anurag Vaidya; Richard J. Chen; Drew F. K. Williamson; Thomas Peeters; Andrew H. Song; Faisal Mahmood 001 (CVPR, 2024)

Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology. - Andrew H. Song; Richard J. Chen; Tong Ding; Drew F. K. Williamson; Guillaume Jaume; Faisal Mahmood 001 (CVPR, 2024)

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 001 (ECCV (33), 2024)

Multimodal Prototyping for cancer survival prediction. - Andrew H. Song; Richard J. Chen; Guillaume Jaume; Anurag J. Vaidya; Alexander S. Baras; Faisal Mahmood 001 (ICML, 2024)

HEST-1k: A Dataset For Spatial Transcriptomics and Histology Image Analysis. - 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 001 (NeurIPS, 2024)

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)

Artificial Intelligence for Digital and Computational Pathology. - Andrew H. Song; Guillaume Jaume; Drew F. K. Williamson; Ming Y. Lu; Anurag Vaidya; Tiffany R. Miller; Faisal Mahmood 001 (CoRR, 2024)

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; et al. (CoRR, 2024)

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-Pérez; Richard J. Chen; Dina ElHarouni; et al. (CoRR, 2025)

AI-driven 3D Spatial Transcriptomics. - 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; et al. (CoRR, 2025)

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; Abdallah Lamane; Daniel Shao; et al. (CoRR, 2025)

Do Multiple Instance Learning Models Transfer? - Daniel Shao; Richard J. Chen; Andrew H. Song; Joel Runevic; Ming Y. Lu; Tong Ding; Faisal Mahmood 001 (CoRR, 2025)